How powerful is your AI operation?

Agentic Power is the skilled work your AI produces for every hour you spend directing it. AP = Skilled Hours ÷ Direction Hours.

How much skilled work can one hour of human attention command?

Developed by Dr. Mark Allen at HeroForge.AI

Executive summary

Measure. Increase. Direct.

  1. AI is becoming a workforce you can direct.

    People and organizations can delegate useful work to AI while supplying goals, context, judgment and accountability.

  2. Measure the power of your AI operation.

    Agentic Power is the skilled work your AI produces for every hour you spend directing it.

    AP = 12× means 12 hours of skilled work for every hour you spent directing the AI. Count the work you’re keeping, finished or in progress. For unfinished work, count only the part that’s done. Count all Direction Hours, including setup, review and fixing.

  3. Understand its economic efficiency.

    AP$ is a second, optional number. It compares what comparable human labor would cost with what your AI work actually cost.

    AP$ = Equivalent Labor Cost ÷ Total Attributable Agentic Cost

    AP$ = 10× means an estimated $10 of comparable human labor cost for every $1 of attributable agentic cost. This does not establish actual savings or profit.

  4. Understand what shapes your power, and where to improve it.

    Quality comes first: top 1% work earns more Skilled Hours and needs fewer Direction Hours to fix. Then ask three questions. What does your AI own? How independently does it work? How does it get better? The answers show where to improve.

  5. Adopt capability instead of building everything yourself.

    Adopt stronger models, agent working environments, tools, reusable skills, workflows and complete AI staff. Assemble and adapt existing capability to your work.

  6. Use Agentic Engineering to turn capability into greater power.

    Improve how the components work together: responsibilities, context, permissions, coordination, evaluation and human oversight. Measure whether the changes produce more work per Direction Hour.

  7. Build improvement into the operation.

    Use results to improve instructions, workflows and system design. People can make those improvements, use AI assistance, or authorize evaluated changes within defined boundaries.

  8. Direct greater power toward worthwhile outcomes.

    More capacity and better economics create options. Human judgment determines which goals deserve that capacity. Assess the outcomes as well as the power available.

Busy AI is not productive AI.

A hundred agents producing a thousand drafts nobody keeps is not productive power.

Tokens, prompts, agent counts and runtime don’t answer the question that matters:

How much useful skilled work did the system produce for each hour a person spent directing it?

A familiar idea: horsepower.

When steam engines began replacing animal power, people needed a practical way to understand and compare what unfamiliar machines could do. James Watt helped popularize horsepower as a common language for machine power.

Horsepower gave people an intuitive measuring stick for a new source of productive capability. AI now presents a similar problem for knowledge work.

Physics gives a useful picture here. Power is work divided by time. Agentic Power is the skilled work your AI produces for every hour you spend directing it.

1 · Measure your Agentic Power

Agentic Power.

Agentic Power is the skilled work your AI produces for every hour you spend directing it. AP = Skilled Hours ÷ Direction Hours.

Same work. Same quality. A normal stretch of time.

Skilled Hours

How long skilled people would need to do the Work Produced. For unfinished work, count only the part that’s done.

Count hours at the skill level the work actually meets. Top 1% work counts top-expert hours.

If a skilled analyst would need ten hours for a report, it’s worth 10 Skilled Hours. Even if you couldn’t write it yourself.

Work Produced is everything your AI made during the audit period that you’re keeping, finished or in progress. Unfinished work counts for the part that’s done. Discarded work doesn’t count. Every AP number lists the Work Produced behind it.

Direction Hours

Your time directing the AI: briefing, setup, reviewing and fixing.

That also covers context, upkeep, training, connecting tools, coordination, handling exceptions, evaluation and improving the system.

Count the active time of everyone who directs the AI. Don’t count time the AI runs on its own, or time spent waiting.

Elapsed time, how long the work took on the calendar, is reported separately. It never goes in AP’s denominator.

Economic translation

Not all skilled hours cost the same.

AP counts every Skilled Hour the same. That keeps it simple. But ten hours of bookkeeping and ten hours of specialized legal work cost very different amounts.

AP$ is a second, optional number. It compares what comparable human work would cost with what your AI work cost. It is not savings or ROI.

Optional: put a dollar figure on it (AP$)

Equivalent Labor Cost
= Σ (Skilled Hours × benchmark hourly labor cost)

For each kind of work, multiply its Skilled Hours by the appropriate hourly labor cost, then add the amounts.

10 Skilled Hours bookkeeping × USD 50/hour = USD 500

10 Skilled Hours specialized legal × USD 400/hour = USD 4,000

Illustrative rates, not observed market benchmarks.

Agentic Power Efficiency (AP$)

Economic efficiency

Equivalent Labor Cost translates Skilled Hours into benchmark human labor cost. Total Attributable Agentic Cost includes direct AI spending, the cost of Direction Hours, and other costs attributable to the same work and period.

AP$
= Equivalent Labor Cost ÷ Total Attributable Agentic Cost

AP$ = 10× means the work represents an estimated $10 of comparable human labor cost for every $1 of total attributable agentic cost. This does not establish actual savings or profit.

Agentic Power tells us how much skilled work human attention can command. AP$ tells us how economically that work was produced compared with obtaining comparable human labor.

It does not tell you what the work was worth to the business. This is not ROI, realized savings, profit, or business value.

ILLUSTRATIVE EXAMPLE

AGENTIC POWER · AP = 17.5×

35 Skilled Hours ÷ 2 Direction Hours

EQUIVALENT LABOR COST
USD 4,50020 Skilled Hours bookkeeping × USD 50
10 Skilled Hours software × USD 150
5 Skilled Hours specialized legal × USD 400

TOTAL ATTRIBUTABLE AGENTIC COST
USD 420Direct AI Cost USD 120
Direction Cost USD 300
Other attributable costs USD 0

SECONDARY ECONOMIC METRICAP$ = 10.7×

4,500 ÷ 420 = 10.714…

Every USD 1 of attributable agentic cost produced work equivalent to approximately USD 10.70 of benchmark human labor cost.

This is not ROI, realized savings, profit, or business value.

A current operating view covers the selected period. A fully loaded view also accounts for attributable setup and earlier investment across the lifecycle.

Current operating view · Labor market: United States national (fictional assumption) · Rate basis: custom internal rate · Source: fictional teaching assumptions, not observed benchmarks · Source date: September 2026 · Currency: USD. Direction Hours: 2 hours × USD 150/hour. Setup investment is not estimated; this is not a lifecycle claim.

Agentic Power measures productive amplification.
AP$ measures economic efficiency.
Business outcomes tell us whether the work was worth doing.

Scroll sideways to compare AP and AP$.

Two Ratios. Two Different Questions.
ComparisonAPAgentic PowerAP$Agentic Power Efficiency
QuestionHow much skilled work can one hour of human attention command?How much comparable human labor cost can one dollar of agentic cost command?
FormulaSkilled Hours ÷ Direction HoursEquivalent Labor Cost ÷ Total Attributable Agentic Cost
Example17.5×10.7×
MeasuresProductive amplificationEconomic efficiency

Neither metric tells you whether the work was worth doing. Business outcomes do.

Economic methodology and reporting rules

Economic treatment of the case studies

Ajay Jani, MD

Agentic Power Efficiency (AP$): Not yet estimated.

The 200 Skilled Hours cover mixed administrative work, not 200 physician hours. Credentialing, licensing administration, administrative support and tax-document/bookkeeping support are candidate skill categories. Their non-overlapping Skilled Hours allocation, market and rate basis are not documented sufficiently to publish a dollar range.

Rowena Chua, MD
+ Emmanuel Cecilio

The historical approximately $1 million conventional-development estimate is context, not a new Equivalent Labor Cost calculation. An independent translation needs documented architecture, frontend, backend, UX/design, testing, documentation and project-management components. Compare that result with the historical estimate only after calculation; do not force agreement. No independent dollar estimate is claimed here. Document a non-overlapping skill and Skilled Hours allocation, market, rate basis and sources first; then calculate Equivalent Labor Cost independently. Similar results may be convergent evidence; material differences require an explanation of assumptions. Agentic Power Efficiency is not yet estimated.

Dr. Mark Allen

Agentic Power Efficiency (AP$): Not yet estimated.

The self-audit prices Skilled Hours but does not document a skill-cost allocation, market or rate basis, so no dollar range is published.

Reuven “rUv” Cohen

The modeled 80M Skilled Hours numerator already carries substantial uncertainty. Multiplying it by a conventional software rate would add another unsupported assumption. Economic translation: not estimated until a defensible skill-cost baseline exists. No Agentic Power Efficiency is calculated for this case.

Four glimpses of the agentic future

Four operations. Four estimates of Agentic Power.

An AI Employee does one recurring job. An AI Department runs a whole business function. A persistent operation keeps working within set permissions. These labels describe how the work is organized. They don’t tell you how much AP it produces.

AI Employee

Ajay Jani, MD

AP ≈ 16.7×
  • ≈200 Skilled Hours
  • ≈12 Direction Hours
  • ≈2 elapsed days

The work and the approach

Ajay used Claude Cowork with custom AI Employees and reusable Skills (saved instructions and procedures for recurring work) to tackle unfinished administration. This built on earlier instruction in writing prompts and using AI to improve them. The work included a credentialing application, tax-document preparation, license tracking and document organization.

Ajay described saving “hundreds of hours.”

200 estimated Skilled Hours ÷ 12 self-reported Direction Hours. Dr. Mark Allen confirms the work met a high standard.

AI Department

Rowena Chua, MD
+ Emmanuel Cecilio

AP = 55×
  • ≈22,000 Skilled Hours
  • ≈400 combined Direction Hours
  • ≈110 elapsed days

The work and the approach

Rowena had no technical background; Emmanuel had some IT experience but was not a software engineer. During a three-day Mastermind retreat, Dr. Mark Allen taught them to use Claude Code with HeroForge Genesis, a pre-built AI software development department. Their website, marketing and communications, AI clinical reporting, education platform and staff clinical workflows are now in production. Rowena describes the system as integral to her business.

A continued-learning and Mastermind journey over approximately 110 days; other systems remain in development.

Retrospective HeroForge.AI / AI-assisted work estimate.

Self-audit · New product build

Dr. Mark Allen

AP ≈ 150×
  • ≈652 Skilled Hours (range 457.25 to 922.75)
  • 4.33 Direction Hours, self-reported
  • ≈38 elapsed hours

The work and the approach

Dr. Mark Allen built a new product, an AI inbox assistant called Unicatr, in about two days: brainstorms, a prototype, a researched spec, a live website, a lesson and workshop, and a tested v0.1 app (952 passing tests). All outputs were accepted on October 24, 2026. The onboarding spec was accepted as a draft and priced at draft quality.

His AP read about 15× at first look, about 24× with the work accepted so far, and about 150× once the app build was accepted. Those early readings counted only accepted work. The current rule counts work you’re keeping, finished or in progress.

Self-audit, not independently verified. The app was accepted as tested in demo mode; real accounts not yet tried.

Persistent AI Operating System

Reuven “rUv” Cohen

AP ≈ 33,000×

FRONTIER CASE

AI-DERIVED PRODUCTION-HISTORY ESTIMATE

  • ≈80M Skilled Hours estimated
  • ≈2,404 Direction Hours modeled
  • ≈253-day measurement window

The work and the approach

Public software including RuView, Ruflo, RuVector and MetaHarness. rUv ran a detailed audit prompt in his own Codex environment and verbally reported an estimate of 10 million working days for his year-to-date code output.

Direction Hours estimated using an assumed daily schedule.

Operating models describe the work. AP measures the result.
Different work and estimation methods; these cases are not a competitive leaderboard. Broader delegation or greater autonomy does not by itself establish higher AP, better quality or better outcomes.

Behind the 33,000× estimate: public projects, the calculation and its limits

Behind the large number: a visible body of work.

rUv builds high-visibility open-source software. These public projects make part of that output inspectable.

  • RuView: WiFi-based presence and movement sensing.
  • Ruflo: AI-agent coordination, shared memory and workflows.
  • RuVector: vector and graph memory infrastructure.
  • MetaHarness: tools for building focused working environments for AI agents.
Explore evidence and calculation
Public GitHub API counts verified September 18, 2026, 17:30 UTC. Counts change.
ProjectWhat the project doesStarsForks
RuViewUses WiFi signals to sense presence and movement without cameras.94,38212,490
RufloCoordinates AI agents, shared memory and autonomous workflows.72,7858,625
RuVectorVector and graph memory infrastructure for AI systems.4,503603
MetaHarnessBuilds focused working environments for AI agents, with tools, memory and learning loops.66282

Project descriptions and counts checked against public GitHub sources. Stars and forks indicate public interest, not validation of Skilled Hours.

The starting point is rUv’s AI-assisted analysis of his own production history: an estimate that reproducing his 2026 year-to-date code output would require approximately 10 million human working days. This is a self-reported estimate over an estimated window, not an independent audit.

Normalize the output

10,000,000 working days × 8 hours
= ≈80,000,000 Skilled Hours

State the Direction Hours assumption

253 calendar days × 9.5 active hours/day
= 2,403.5 hours (≈2,404)

Calculate the ratio

80,000,000 ÷ 2,403.5 ≈ 33,285
AP ≈ 33,000×

January 1 through September 10 is 253 inclusive days. At 8 assumed Direction Hours/day, AP ≈ 39,526×; at 12 hours/day, AP ≈ 26,350×. Rounded: roughly 40,000× to 26,000×.

This shows how AP changes when the assumed Direction Hours change, not a confidence interval. The output baseline is not independently audited; human attention is assumed, not measured. The calculation illustrates the claim’s scale. The same Work Produced and credible-baseline rules apply even when the number is very large.

Descriptions summarize the linked project documentation. Repository existence and reach do not independently validate the estimated 80 million Skilled Hours. Public projects can include community contributions, imported history and generated artifacts; attribution and which work is being kept must be reconciled in a full Audit.

“As an advisor to Cognitum.One, I have an unusual view into rUv’s software output beyond what is visible on GitHub.

I have personally watched his autonomous systems produce more high-quality software during a week when he was on vacation than I have seen many conventional organizations produce in a month.”

Dr. Mark Allen

Dr. Mark Allen is an advisor to Cognitum.One. This is first-person observation, not an independent benchmark.

A different operating model can create a different scale of output.

Proven work keeps moving when human attention moves elsewhere. That is why autonomy can make Agentic Power so large. Elapsed time still matters: the same AP delivered in two days or two months can serve very different needs.

The Agentic Power Audit

What is your Agentic Power?

The Agentic Power Audit runs inside your own AI. It lists the work your AI made and estimates the Skilled Hours. You confirm your Direction Hours. You get your AP and the list of work behind it.

See a sample result (fictional)

Example Agentic Power Profile

FICTIONAL EXAMPLE · ILLUSTRATIVE RATES

Your Agentic Power

AP ≈ 17.5×

184 Skilled Hours ÷ 10.5 Direction Hours
Seven-day window

AGENTIC POWER EFFICIENCYAP$ = 10.8×

Current Operating AP$ · USD 17,100 ÷ USD 1,585

Work Produced

184 Skilled Hours

Equivalent Labor Cost: USD 17,100
Total Attributable Agentic Cost: USD 1,585

Direct AI Cost USD 220
Direction Cost USD 1,312.50
Other attributable costs USD 52.50

Labor market: United States national (fictional assumption) · Rate basis: custom internal rate · Source: fictional Profile assumptions, not observed benchmarks · Source date: September 2026 · Currency: USD. Direction Hours: 10.5 hours × USD 125/hour. Historical setup investment is not estimated; this is not a fully loaded result. These are not Dr. Mark Allen’s or HeroForge.AI’s economics.

What did it produce?

Customer research, a released reporting application, a sales campaign and three completed administrative processes.

Work Produced list, Skilled Hours and illustrative rates

What was it directed toward?

Return routine operator time while maintaining quality.

Routine Direction Hours: 9 → 6
Across comparable weekly workloads.

Routine directing is part of total Direction Hours. The 10.5-hour total also includes other attributable effort, including improvement work.

Partial coverage: three of four declared sources. Figures describe covered work only; the example does not establish causation.

Diagnostics

These observations help identify constraints and choose an improvement to test. They cover different parts of the work, not one combined operating level. The next sections explain how to use delegation, autonomy, system design and improvement to increase AP.

Responsibility delegated
Recurring specialist roles account for the largest share of Work Produced.
How work runs
Interactive work accounts for the largest share of Work Produced.
System design
Good task context; tools, permissions and the working environment need improvement.
Improving the system
AI suggests changes; people approve and apply them.
Main bottleneck
Repeated human handoffs
Next improvement
Test one recurring workflow with clear acceptance checks.

Where does your AI work live?

There are two audits. Pick the one that matches where your AI does its work. Both look back over the last 30 days unless you name another period.

Both follow the same steps. First, an instant result: your AP and your Work Produced list, with Direction Hours estimated by the AI. Then one question: “About how many hours did you spend directing AI during this audit period of [period]?” The audit fills in the dates. Your answer makes the result exact. The AI can’t see your time, so it asks instead of guessing.

Chat Audit

For work done in chatbots such as ChatGPT, Claude or Gemini. Paste the prompt into the chatbot you use most.

It reviews the memory and chat history it can see, tells you what it found, and shows its estimate and assumptions. It does not assume it can see all your other chats.

Download prompt

Paste into a chatbot. The audit runs there.

View Chat Audit prompt
# Run my Agentic Power Chat Audit now

This is a copy-and-paste prompt for a chatbot such as ChatGPT, Claude or Gemini. Treat it as my instruction to run the audit now. Do not summarize or rewrite it. No companion file is needed.

Framework publication destination: [HeroForge.AI/agentic-power](https://heroforge.ai/agentic-power).

Agentic Power (AP) = Skilled Hours ÷ Direction Hours. It is the skilled work my AI produces for every hour I spend directing it.

- **Work Produced:** everything my AI made during the audit period that I'm keeping, finished or in progress. Discarded or abandoned work does not count.
- **Skilled Hours:** how long skilled people would need to do the Work Produced, at the same quality, without generative AI. For unfinished work, count only the part that's done.
- **Direction Hours:** my time directing the AI: briefing, setup, reviewing and fixing. Count everyone who directed it, including time spent coordinating. Time the AI runs on its own does not count.

List the Work Produced behind the Skilled Hours as evidence.

Follow the four steps below in order.

## Step 1: Set the period

Default period: the 30 days ending at this audit request (start inclusive, end exclusive, my timezone if known). If I name a different period, use mine. Work you cannot date: include it only if it likely falls in the period, and label it “date unknown”. State the period at the top of every result.

## Step 2: Give the instant result. Ask nothing first.

Begin your response: “I’ll review the memory and chat history I can see for this period, find the work your AI produced, and estimate your AP. Then I’ll ask you one question to make it accurate.” Then actually do that review before you write a number.

**What to review.** Use the saved memory, recalled past chats, this conversation and anything I supplied. If a memory or chat-history search tool is available and authorized, use it to find work produced, deliverables and directing time. Search remembered project and deliverable names even when memory has no time logs. Do not stop at an empty conversation if relevant memory exists. Do not claim to see chats or settings you cannot inspect. Report which sources you used and which were unavailable, and tell “unavailable” apart from “reviewed, nothing found.” No web research, account connections or file collectors are needed.

**Using memory.** Specific remembered outcomes can support the estimate. Label them “memory-based; not independently verified.” Memory can be incomplete or stale: merge duplicates and prefer newer corrections. General facts about my job, tools or plans do not prove work was produced. Plans are not output. Do not invent details to fill gaps. Research, analysis, documents, software, systems, teaching materials and operational work all count when I'm keeping them, finished or in progress. For mixed human and AI projects, state an assumed AI share and count only that share. Contracts, awards, revenue and events are not AI output; count the deliverables behind them once.

**Skilled Hours.** For each distinct item of Work Produced, estimate how long skilled people would need to produce the same result at the same quality without generative AI. Give low, likely and high, and explain the estimate in a line. Count each item once. For unfinished work, count only the part that's done. Say whether you know I'm keeping it from what you observed, from memory, from what I said, or by assumption. Keep uncertain items conditional. Leave out work I discarded or abandoned.

**Direction Hours, estimated by AI.** AI cannot see my directing time, so estimate it from the work. Break each item into briefing, setup, review, fixing and coordination. Choose low, likely and high, and label all of them “estimated by AI”. Include collaborators with an assumed combined time if theirs is unknown. Do not infer directing time from elapsed calendar time, gaps between messages, token counts or AI runtime. Do not double count one person’s overlapping attention. Convert minutes to hours.

**Calculate.** Add Skilled Hours and Direction Hours separately, then divide. Do not average the ratios of single tasks. Headline: **AP ≈ X×. Direction Hours estimated by AI.** Show the totals and the division. Give a range from explicit inputs: low = Skilled low ÷ Direction high, high = Skilled high ÷ Direction low. If any Direction Hours value is zero, that endpoint is undefined, never infinite: do not report a finite upper estimate. No made-up confidence percentages. Do not promise accuracy or improvement.

When you find real work, never fall back to a fixed score. Missing evidence does not mean low productivity. A real calculation can come out at 1× or less; report it as it is. Unknown status, AI share or collaborator time widens the range. It does not block the first result. Your central estimate is your best judgment; the conservative case belongs in the range.

If no usable work is visible, show **AP = 1×. Illustrative starting reference, not your measured AP**, from a neutral example: 30 minutes of comparable skilled work ÷ 30 minutes of directing. Do not invent past work, score unseen work as zero, or call a beginner low-performing. It can never serve as a baseline for later growth claims.

**Calibration example.** Memory shows a provider inventory I'm keeping, worth about 16 Skilled Hours, with no time log. A fair first pass assumes 2 Direction Hours (0.5 briefing and setup, 0.75 review, 0.75 fixing): **AP ≈ 8×. Direction Hours estimated by AI.** With Skilled Hours of 8 to 24 and Direction Hours of 1 to 4, the range is **2× to 24×** (8 ÷ 4 to 24 ÷ 1). These numbers show the reasoning only. Use the work you actually find.

**Write the instant result** in plain language, about 250 to 400 words plus a small table:

1. **Period:** start and end, or the work boundaries if dates are unknown.
2. **Your AP:** the headline with its label, Skilled Hours ÷ Direction Hours, one plain sentence on what it means, and the range.
3. **Work Produced:** a short table of items with source, finished or in progress (with the part done), Skilled Hours and estimated Direction Hours.
4. **Coverage:** what you could review, what you could not, and the biggest uncertainty. Do not count this audit or its report as output.
5. **The one question** from Step 3. Ask nothing else.

## Step 3: Ask one question, then give the accurate result

Ask exactly: **“About how many hours did you spend directing AI during this audit period of [period]?”** Replace [period] with this audit’s actual dates, for example “Sep 23 to Oct 23, 2026”. You may follow it with: “Low, likely, high is fine.”

When I answer, recalculate with my numbers and label the headline **AP ≈ X×. Self-reported Direction Hours.** Use my likely value for the headline and my low and high for the range. A single number is fine; do not invent a range around it. Keep the AI-estimated result beside it so I can see both.

If my hours may include AI that this chat cannot see, add one line under the result: this may read low, because work from those other systems is not counted yet. Do not ask a second question about it here. Step 4 confirms which systems my hours covered.

If the instant result was the illustrative 1× reference, there are no real Skilled Hours to divide. Do not calculate a personal AP from my hours. Keep the label “Illustrative starting reference, not your measured AP”, record my hours in the checkpoint, and ask what I made with AI that I'm keeping.

If I answer zero, AP is undefined, never infinite. If only my low is zero (for example 0 / 2 / 4), the high end of the range is undefined; report the low and likely results and do not give a finite upper estimate. Ask whether any setup, prompting, review or fixing happened. If it did, use that time. If I give no answer, keep the instant result and its label.

Recalculate whenever I correct an item, mark one kept, in progress or discarded, or add one.

## Step 4: Ask about my other AI systems

After the accurate result in Step 3, or right after Step 2 if I skip the question, ask me this:

**“Do you have other AIs running? For example other chatbots, coding agents, scheduled or autonomous agents, automations like n8n or Zapier, or AI staff. What kind are they?”**

If I say no, stop. If I name other systems, give me one Satellite Audit prompt for each kind, in its own copyable block. Use the Satellite Audit prompt under that heading in this prompt or file. Fill in the system name and kind and this audit’s period. Add one line that tells it where to look:

- **Chatbot:** review your memory and chat history.
- **Coding agent or agent harness:** review your session logs, commits and changed files.
- **Scheduled or autonomous agent:** review your run logs, outputs and failures. Setup, review and upkeep count as Direction Hours.
- **Automation (n8n, Zapier, Make or similar):** review your run history. Count only runs that produced something I'm keeping.
- **AI staff or a custom agent:** review the work you produced that I'm keeping.

Tell me: “Paste this prompt inside that system. It returns a block that starts with SATELLITE AUDIT RESULT. Paste that block back here.”

### Merge the blocks I paste back

1. **Check the period.** Every block must cover this audit’s period. If a block covers a different period, say so, keep it out of the combined result and ask me to re-run that Satellite Audit with this audit’s period.
2. **Build one combined Work Produced list** from this audit’s list and every block. Leave out anything marked discarded. The same piece of work can show up in more than one system under different names. For example, a chatbot drafts a report and a coding agent publishes it. Match items by name, file, link, version and date. Treat every “also in” note that names another merged system as a possible duplicate: if you cannot match it to an item there, ask me before you count it. Count each item once.
3. **Split only real parts.** If two systems each produced a distinct, separately useful part, give each its own Skilled Hours. Otherwise credit the item once, to the system that produced the version being kept, and list the other system as a contributor with 0 Skilled Hours. Use that system’s Skilled Hours estimate. If the estimates differ, show both and ask me which one fits the version I'm keeping. Never pick the larger one yourself.
4. **Combined Skilled Hours** = the sum over unique items, for low, likely and high separately. For unfinished work, count only the part that's done. Never add a duplicated item twice.
5. **Combined Direction Hours.** If I answered the Step 3 question, do not assume my answer covered the other systems. I may have counted only this one. Before you merge, ask me once for each system: “Did your [N] hours include directing [system]? Setup, review and upkeep count.” Add a system’s Direction Hours only if I say no, so no hour counts twice. Skip them only if I say yes. If I skipped the question, this audit’s Direction Hours are an AI estimate for this system only, so add every block’s Direction Hours. If a system’s Direction Hours are unknown, ask me for them. Never treat unknown as zero.
6. **Combined AP** = combined Skilled Hours ÷ combined Direction Hours. Never average the AP numbers of separate systems. Give the range the same way as Step 2: low = combined Skilled low ÷ combined Direction high, high = combined Skilled high ÷ combined Direction low. If combined Direction low is zero, the high end is undefined, never infinite: do not report a finite upper estimate.
7. **Show each system’s share** in a short table: system, kind, autonomy level, unique Skilled Hours, share of combined Skilled Hours, Direction Hours with basis, and cost if known. Work out each share from likely Skilled Hours: the system’s unique likely Skilled Hours ÷ combined likely Skilled Hours. Shares add up to 100%. For a system whose Direction Hours were already included in my Step 3 answer, write “included in your [N] hours” in its Direction Hours cell instead of a number.
8. **Label honestly.** The combined result carries its weakest Direction Hours label. If combined Direction Hours are zero or unknown, AP is undefined, never infinite. Autonomous agents still need setup, review and upkeep, so ask for that time. If this audit found no real work, its illustrative example stays out of the combined result.

## Keep a checkpoint for a later audit

End each result with a compact **AP checkpoint**: baseline-1 (or followup-1), framework v1.7.0, period, people covered, Work Produced, Skilled Hours and Direction Hours with their labels, AP and range, assumptions, missing sources, systems merged and any prior checkpoint. Keep it in this conversation; do not claim it is saved to lasting memory. Say: “You can stop here. To measure new work later, come back to this chat.”

For a follow-up, measure only new work since the last checkpoint, and leave out the audit exchanges. Count only the new part of work that was in progress last time. Keep the baseline, avoid duplicate items, and keep earlier setup visible. If the last checkpoint is unavailable, ask for it rather than invent one. Compare AP only across matching coverage and quality. Never turn an illustrative baseline into a growth claim. A full Agentic Power Profile is optional, only on request.

## Privacy and boundaries

Use only information actually available within authorized scope. Do not request credentials, install anything, modify projects, send messages or publish. HeroForge.AI does not receive records merely because this prompt is copied or downloaded; processing follows the chosen chatbot provider’s policies. Keep sensitive details out of summaries. If I request a public summary, label it “Draft for your review” before any sharing, remove private source links or IDs, and do not bundle source packets. Sharing requires a separate explicit instruction.

## Satellite Audit prompt

Fill in the bracketed lines before you hand this to me.

```text
# Run my Agentic Power Satellite Audit

Before you paste this prompt, fill in the bracketed lines below. At least fill in the period.

I am measuring my Agentic Power across several AI systems. My main audit runs in a different AI. Your job: report on the work done in this system only, and return one result block that I will paste back into the main audit. Do not calculate my overall AP.

System: [name and kind of this system]
Period: [copy the main audit’s period here exactly, start and end with timezone. If it is blank, use the 30 days ending at this message, and say so in the block, because the main audit can only merge a block for its own period]
Where to look: [memory and chat history / session logs, commits and files / run logs and outputs / run history / delivered work]

Agentic Power (AP) = Skilled Hours ÷ Direction Hours.

- **Work Produced:** everything this system made during the period that I'm keeping, finished or in progress. Discarded or abandoned work does not count.
- **Skilled Hours:** how long skilled people would need to do the Work Produced, at the same quality, without generative AI. For unfinished work, count only the part that's done.
- **Direction Hours:** my time directing this system: briefing, setup, reviewing and fixing. Time the AI runs on its own does not count.

Do this:

1. Review only the records you can actually see in this system for the period. Say what you could not see.
2. List each item of Work Produced once, marked finished or in progress. Group retries and subtasks into the one item they produced. Leave out discarded work, failed runs, heartbeats and repeated unchanged reports.
3. Estimate Skilled Hours for each item as low, likely and high. For an item in progress, estimate only the part that's done. Price the result, not the prompt, the token count or the runtime.
4. Direction Hours: label time from a time log “logged”. Label time “self-reported” only when I state it in this run of the audit. Hours you derive from memory or earlier chats, and hours you estimate from the tasks (briefing, review, fixing, upkeep), are labeled “estimated by AI”. Never infer it from runtime or gaps between messages. If you cannot estimate it, write unknown.
5. Mark any item that was also worked on in another AI system. Name each such system exactly as I name it, separated by commas, so the main audit can avoid counting it twice.
6. Autonomy level, one of:
   - A1 Interactive: It waits for you after every reply. If you walk away, the work stops.
   - A2 Delegated: You hand off a whole job. It keeps working without you and comes back when done or stuck.
   - A3 Scheduled: A time or cadence starts the work.
   - A4 Event-Driven: An event starts the work.
   - A5 Proactive: It finds and starts useful work toward a goal, within set permissions.
7. Cost: the actual cost for the period if you can see it. Otherwise write unknown. Do not guess.

Return only this block, filled in, as plain text:

SATELLITE AUDIT RESULT
System:
Kind: chatbot / coding agent / scheduled or autonomous agent / automation / AI staff / other
Period: start to end, timezone
Skilled Hours: low / likely / high
Direction Hours: number or low / likely / high, or unknown (basis: logged, self-reported, estimated by AI, or unknown)
Work Produced:
- item | finished or in progress | Skilled Hours low / likely / high | evidence | date | also in: <exact System name>[, <exact System name>] or none
Autonomy level:
Cost: amount and currency, or unknown
Coverage gaps:
END SATELLITE AUDIT RESULT

Use only what you are authorized to see. Do not install anything, change anything, send messages or publish. Keep private details out of the block: describe each item in a few plain words.
```

Agent Audit

For work done by coding agents and agent tools with file access, such as Claude Code, Codex or Claude Cowork. Paste the starting prompt into the agent.

The prompt points your agent to the complete guide. If it can’t open the guide, download it and attach it in the same session. The agent reads its own session records, files and commits, read-only.

Read the guideDownload guide

Start in one session. Your agent leads the audit.

View Agent Audit starting prompt
# Start my Agentic Power Agent Audit

Use the Agent Audit Guide, framework v1.7.0, to run an audit of my AI work. This works in a coding agent such as Claude Code or Codex, or in an agent tool with file access such as Claude Cowork.

First open and read the complete guide at:
https://heroforge.ai/agentic-power/downloads/agentic-power-agent-audit.md

Readable guide and instructions:
https://heroforge.ai/agentic-power/audit/

Confirm whether you successfully read the complete guide and identify its version. If you cannot retrieve it, stop and ask me to attach the downloaded Agent Audit Guide or paste its contents. Do not claim to have followed a guide you could not read, or substitute a made-up method.

Once the guide is available, follow its four steps. Default period: the 30 days ending at this request, unless I name another period. Give the instant result first: AP and the Skilled Hours behind it, counting the work I'm keeping, finished or in progress, and listing it as evidence, with Direction Hours estimated by AI and labeled that way. Then ask me the one question about my directing time and give the accurate result. Then ask what other AIs I run and hand me a Satellite Audit prompt for each kind.

Start read-only. Check your actual file and tool access. Review the records you can reach for the period: local sessions, projects and work files, not only this conversation. Access to an app does not establish access to every conversation, account, or device. Tell me which sources you actually searched and which remain inaccessible or unsearched.

Do not treat session length or unattended runtime as my directing time. Use a private folder we agree on before saving evidence; otherwise return records here. Do not modify source projects, install tools, send messages, or publish anything.

Running more than one AI?

Many people now run several AIs: a chatbot, a coding agent, a scheduled agent, an automation in n8n or Zapier. Both audits ask what else you run. For each kind, they hand you a short Satellite Audit prompt. Paste it into that system. It returns a small result block. Paste that block back into your first audit.

The audit merges the blocks into one combined AP. Work that shows up in two systems counts once, credited to the system that produced the version being kept. You see each system’s share. This matters more as agents run on their own: an autonomous agent still needs Direction Hours to set up, review and maintain, and those hours count.

Download prompt

Your main audit fills this in for each system. Copy it here to start one yourself.

View Satellite Audit prompt
# Run my Agentic Power Satellite Audit

Before you paste this prompt, fill in the bracketed lines below. At least fill in the period.

I am measuring my Agentic Power across several AI systems. My main audit runs in a different AI. Your job: report on the work done in this system only, and return one result block that I will paste back into the main audit. Do not calculate my overall AP.

System: [name and kind of this system]
Period: [copy the main audit’s period here exactly, start and end with timezone. If it is blank, use the 30 days ending at this message, and say so in the block, because the main audit can only merge a block for its own period]
Where to look: [memory and chat history / session logs, commits and files / run logs and outputs / run history / delivered work]

Agentic Power (AP) = Skilled Hours ÷ Direction Hours.

- **Work Produced:** everything this system made during the period that I'm keeping, finished or in progress. Discarded or abandoned work does not count.
- **Skilled Hours:** how long skilled people would need to do the Work Produced, at the same quality, without generative AI. For unfinished work, count only the part that's done.
- **Direction Hours:** my time directing this system: briefing, setup, reviewing and fixing. Time the AI runs on its own does not count.

Do this:

1. Review only the records you can actually see in this system for the period. Say what you could not see.
2. List each item of Work Produced once, marked finished or in progress. Group retries and subtasks into the one item they produced. Leave out discarded work, failed runs, heartbeats and repeated unchanged reports.
3. Estimate Skilled Hours for each item as low, likely and high. For an item in progress, estimate only the part that's done. Price the result, not the prompt, the token count or the runtime.
4. Direction Hours: label time from a time log “logged”. Label time “self-reported” only when I state it in this run of the audit. Hours you derive from memory or earlier chats, and hours you estimate from the tasks (briefing, review, fixing, upkeep), are labeled “estimated by AI”. Never infer it from runtime or gaps between messages. If you cannot estimate it, write unknown.
5. Mark any item that was also worked on in another AI system. Name each such system exactly as I name it, separated by commas, so the main audit can avoid counting it twice.
6. Autonomy level, one of:
   - A1 Interactive: It waits for you after every reply. If you walk away, the work stops.
   - A2 Delegated: You hand off a whole job. It keeps working without you and comes back when done or stuck.
   - A3 Scheduled: A time or cadence starts the work.
   - A4 Event-Driven: An event starts the work.
   - A5 Proactive: It finds and starts useful work toward a goal, within set permissions.
7. Cost: the actual cost for the period if you can see it. Otherwise write unknown. Do not guess.

Return only this block, filled in, as plain text:

SATELLITE AUDIT RESULT
System:
Kind: chatbot / coding agent / scheduled or autonomous agent / automation / AI staff / other
Period: start to end, timezone
Skilled Hours: low / likely / high
Direction Hours: number or low / likely / high, or unknown (basis: logged, self-reported, estimated by AI, or unknown)
Work Produced:
- item | finished or in progress | Skilled Hours low / likely / high | evidence | date | also in: <exact System name>[, <exact System name>] or none
Autonomy level:
Cost: amount and currency, or unknown
Coverage gaps:
END SATELLITE AUDIT RESULT

Use only what you are authorized to see. Do not install anything, change anything, send messages or publish. Keep private details out of the block: describe each item in a few plain words.

Using this in a workshop? Run the Chat Audit before you begin. Return to the same chat afterward and use the workshop follow-up prompt to measure just the new interval. If other chats are inaccessible, one short recap supplies the missing work.

See how the Audit works · Try the AP calculator

The audit runs in your chosen AI environment. Downloading a guide does not send HeroForge.AI your work records.

Your AP tells you how much productive power the operation delivers now. To increase it, start with the quality of the work. Then look at how responsibility is delegated, how independently work proceeds, and how the operation improves. Measure again to see whether a change helped.

2 · Increase your Agentic Power

Adopt capability. Build your power.

You raise AP by using AI capability that already exists.

Start with what slows you down. You may need a better model, a better working environment, a tool, a reusable skill, a proven workflow, or a ready-made AI employee, team or department.

A model provides the AI’s underlying capabilities. A harness is the working environment that connects the model to instructions, tools, memory and controls. Adopting a better combination can change what work you can delegate and how much attention it needs.

Capability creates possibilities. Agentic Power is realized in use.

You still supply goals, context, permissions, review and judgment. The same model or system can produce different AP in different hands. Count setup, adaptation and onboarding effort when measuring the result.

Start with quality. Then use delegation, autonomy and self-improvement to find opportunities. Agentic Engineering helps you turn those opportunities into a working operation.

Top 1% quality

Quality comes first.

Properly directed, AI produces top 1% work. It can do work you don’t know how to do, with skills you don’t have.

Quality drives both halves of AP. Better work earns more Skilled Hours. Work that comes back right means fewer Direction Hours spent fixing.

Low quality hurts at every level. Delegate more of it and you have more to fix. Add autonomy and the mistakes come faster.

So quality is taught first, before delegation and autonomy. It comes from clear instructions, the right context, Skills, harness and checks.

The AI Staff Delegation Ladder

The higher you climb, the more you direct goals.

You can hand AI more responsibility over time: tasks, then roles, outcomes and whole operations. Use the ladder to ask what your AI could reliably own next. You stay accountable.

AI staff is an organizational metaphor: software systems can take on roles and responsibilities without being human employees.

R labels the responsibility level, from R0 (human work) to R6 (an AI portfolio).

↑ Broader responsibility · Read upward from R0

R6

AI Portfolio

Govern multiple AI-native operations.

“Allocate resources across these operations within our goals and risk limits.”
R5

AI Company

Delegate organizational goals + constraints.

“/goal Grow revenue while maintaining these quality, margin and customer constraints.”
R4

AI Department

Delegate a business function.

“/goal Increase qualified marketing pipeline by 25% within this budget.”
R3

AI Team

Delegate an outcome.

“/goal Launch the customer research report by Friday.”
R2

AI Employee

Delegate a recurring role.

“Own our weekly competitive intelligence.”
R1

AI Assistant

Delegate tasks.

“Draft this email.”
R0

Human

You perform the work.

No responsibility delegated to AI.
Instructions become more abstract as responsibility rises. A higher rung needs the capability, coordination and controls to deliver what was delegated.
Task→Role→Outcome→Goal→Goals + constraints

/goal is an illustrative interface metaphor, not a universal command. Goals still need quality standards, constraints and escalation rules.

The higher you climb, the less you direct tasks and the more you direct goals.

If repeated task-by-task instructions consume your attention, test delegating a complete recurring responsibility with clear standards and escalation rules. Broader delegation helps only if the work you keep justifies the human effort it requires.

Skills Tools / Connectors

A useful AI Employee needs more than a good prompt. It needs to know its job and have the tools required to do it.

Skills make the role repeatable. Tools / Connectors provide access to approved information and allow permitted actions, with appropriate context and checks.

The Autonomy Levels

Work can begin without waiting for you.

Autonomy Levels show what starts the work, and whether proven work can keep going when you’re not there.

A1–A5 label how a job starts and continues, from interactive work to proactively pursuing an ongoing goal.

  1. A1 Interactive

    It waits for you after every reply. If you walk away, the work stops.

    Your next instruction
  2. A2 Delegated

    You hand off a whole job. It keeps working without you and comes back when done or stuck.

    A defined assignment
  3. A3 Scheduled

    A time or cadence starts the work.

    A time or cadence
  4. A4 Event-Driven

    An event starts the work.

    A lead, email or other signal
  5. A5 Proactive

    Identifies and initiates useful work toward an ongoing goal, within defined permissions.

    An ongoing objective
Autonomy changes what initiates the work.

A4 responds to a defined event. A5 identifies what needs doing and initiates the next action. Running continuously alone does not establish A5.

Look for unnecessary starts, handoffs and check-ins. A defined assignment, schedule or event trigger may let proven work proceed with less attention. Keep review and escalation where they protect quality, and measure the resulting AP.

When Work Produced keeps growing without the same growth in Direction Hours, AP can rise dramatically.

An AI Employee can proactively identify and address needs within its role. An AI Department can still wait for a human assignment. Responsibility and autonomy are different choices.

Agentic Engineering

Turn capability into a productive operation.

Agentic Engineering is the human discipline of selecting, combining, designing, directing, evaluating and improving AI systems that perform useful work. It turns adopted or newly built capability into an operation you can rely on and improve.

  1. Prompt

    The instruction.

  2. Context

    What the AI sees and knows.

  3. Harness

    The model’s working environment: tools, memory, permissions and controls.

  4. Loop

    Repeat actions, check results and correct the work until it meets the goal.

  5. Graph / Organization

    How agents, tools, loops and people coordinate.

Each step zooms out from one instruction toward the entire operation. Improve the layer that constrains useful work, then measure whether the change increased AP.

Put AI staff into practice.

When capability is organized as AI staff, use this lifecycle to put it to work and improve it. Models, harnesses, tools and workflows support the staff at every stage.
  1. Hire

    Choose or build a specialist for a real job.

  2. Onboard

    Connect context, tools, permissions and standards.

  3. Train

    Teach Skills and evaluate representative work.

  4. Deploy

    Run within boundaries and collect evidence.

  5. Promote

    Expand responsibility when performance earns trust.

Deployment → evidence → learning → improvements → earned trust
Evidence feeds back into Skills, context, tools and operating design at every stage. Use what you learn to improve the next cycle.

Goal-Directed Self-Improvement

Improve the system doing the work.

Proactive work advances the goal. Self-directed improvement strengthens the system that pursues it.

Autonomy asks how independently it works. Improvement asks how it gets better.

Use results from completed work to improve the instructions, tools or workflow used for future jobs. People can lead this feedback loop or give AI increasing ownership of it.

Proactive work can use an unchanged process.A research agent may notice an emerging issue and prepare a briefing without being asked, using the same research Skill.

Improvement can happen between assignments.A research agent may identify a weakness in its research process, test a revised Skill, and retain the revision after demonstrating better results. People set the goals and boundaries; higher-risk changes can require approval.

  1. Goal
  2. Act
  3. Measure
  4. Evaluate
  5. Learn
  6. Improve
  7. Act again
↶ Learn from the outcome. Improve the next cycle.

Some Agentic Power can be reinvested into creating more future Agentic Power.

Choose a recurring error or source of rework. Use its evidence to improve the instructions or workflow, test comparable work again, and measure AP with the improvement effort included. Measure whether the change improves the work you keep, its quality or the human attention it needs.

Goal-Directed Self-Improvement is an Agentic Engineering method that can support every stage of the AI staff lifecycle. It can operate within an AI Employee, Team, Department or Company.

Improve toward the actual goal: revenue, better software, or less owner / operator attention with equal or better quality. Human judgment sets the constraints, quality standards and safety boundaries.

What the system can improve

Prompts, Skills, context, memory, tools, harnesses, loops, graphs, routing and evaluations can all improve through evidence. Permit low-risk changes within approved boundaries; review higher-risk changes and preserve rollback.

For a detailed assessment of who owns the improvement process, see the Audit’s improvement statuses.

Understand your operation. Choose your next improvement.

Ask three questions: Responsibility: What does it own? Autonomy: How independently does it work? Improvement: How does it get better? The map shows responsibility and autonomy. The improvement loop helps you change the operation over time; its detailed status belongs in the Audit.

These numbers label categories; they are not scores to add or multiply.

Use your position to identify constraints and choose a change to test. Measure AP before and after comparable work to find out whether the change helped.

Rowena and Emmanuel’s software operation illustrates the notation: R4 · A2: AI Department · Delegated. They delegate departmental work through assignments and lead improvements themselves. A question to test might be whether a proven job could start on a schedule. The labels do not show that a change is needed, and they do not determine the case’s AP = 55×.

Agentic Power: AP equals Skilled Hours divided by Direction Hours. Agentic Engineering enables a system with R, the AI Staff Delegation Ladder, vertically and A, the Autonomy Levels, horizontally. A separate feedback loop improves the operation; map coordinates do not measure AP.
Download the framework graphic · AP = Skilled Hours ÷ Direction Hours.

Not every workflow should maximize autonomy. Risk, quality, economics, regulation, and human preference matter.

Try a different example

Explore delegation and autonomy

↑ Tasks → Roles → Outcomes → Goals → Goals + Constraints

Scroll across the map, or use the labeled selectors below.

R: AI Staff Delegation Ladder Scope of responsibility ↑
R6 Portfolio
R5 Company
R4 Department
R3 Team
R2 Employee
R1 Assistant
A1
Interactive
A2
Delegated
A3
Scheduled
A4
Event-Driven
A5
Proactive

A: Autonomy Levels Independence →

R0 Human is the reference: no AI autonomy or self-improvement defaults.

AI Team × Scheduled: delegate a coordinated team outcome, such as producing an evidence-backed report. A defined cadence starts the work, such as a weekly cycle. Power must be measured separately.

Apply coordinates to a defined workflow or operation, not automatically to a whole person. R and A are category identifiers: do not add, multiply or average them. Higher coordinates do not establish higher AP, quality or better outcomes.

Increase Agentic Power

Four levers. One productive system.

AP rises when Skilled Hours grow faster than the Direction Hours it takes to produce them. Pick a lever below, change one thing, and measure again.

Capability

Give AI the ability to do better work.

What to improve

Models, Skills, context and tools / connectors.

System design

Make the operation reliable and coordinated.

What to improve

Harnesses, loops, graphs, orchestration and evaluations.

Scale + autonomy

Let proven work run more broadly and independently.

What to improve

Parallelism, persistence, delegation and safe autonomy.

Learning

Turn performance evidence into better future execution.

What to improve

Feedback, evaluation, memory and Goal-Directed Self-Improvement.

The levers can reinforce one another. Better system design can reduce correction; reusable learning can improve the next assignment. Measure the result, including the human effort invested in improvement.

Once you have measured an improvement in AP, you can consider what it means for the capacity available to your organization.

Productive capacity becomes an organizational resource.

Productive Output
= Agentic Power × Direction Capacity

Productive output counts the work you keep that meets the agreed standard. Output is measured in Skilled Hours. Direction Capacity is measured in human hours.

Direction Capacity means the human hours available to direct the operation during the chosen period.

Higher Agentic Power.
Less human attention for the same output.

SAME OUTPUT: 1,000 Skilled Hours
AP = 10×100 Direction Hours
AP = 100×10 Direction Hours
Same work. Same quality. Illustrative arithmetic.

For a given quantity and quality of output, higher AP means fewer Direction Hours are required. For a fixed number of Direction Hours, higher AP means more productive capacity is available. Apply this relationship at a comparable AP and quality level; a past ratio does not guarantee unlimited scale.

Higher Agentic Power lets the same people produce far more. It can also make much leaner organizations possible.

From one workflow to a company.

Measure AP for a workflow, project, person, team, department or company.

Add up all the Work Produced. Add up everyone’s Direction Hours. Then divide. Do not average individual AP ratios.

An organizational calculation

100 Skilled Hours / 10 Direction Hours and 900 Skilled Hours / 30 Direction Hours combine to 1,000 Skilled Hours / 40 Direction Hours: AP = 25×, not the simple average of 10× and 30×. Count shared work and Direction Hours once.

The other economic curve

Comparable AI capability is getting cheaper.

AP can rise as models and systems get better. At the same time, the cost of running AI at a fixed level of capability has been falling fast.

This chart compares the cost of running models at a similar score on a broad knowledge benchmark (MMLU). Tokens are the small units of text used to price that processing.

GPT-3.5-level MMLU inference cost · measured threshold

November 2022$20.00per million tokens

October 2024$0.07per million tokens

More than 280-fold decline · Stanford AI Index 2025

Cost at a fixed capability threshold, not total organizational AI spend. Two observations, not a universal cost curve. Other measured rates vary widely and may not persist: sources and limits.

Agentic Power can rise while the unit cost of the intelligence underneath it falls.

Cheaper intelligence makes existing work cheaper.
It also makes new work worth doing.

Work that was too expensive can become worth attempting. Whether recent rates of decline will last is unknown. The jobs section explores both possibilities.

What does this mean for the people we hire?

In an AI-native economy, companies will increasingly hire, develop and reward people for how much useful work they can direct.

If two people perform broadly comparable work at the same quality bar, with similar scope and under comparable conditions, a consistent, substantial difference in AP is economically meaningful.

That matters for hiring, compensation, bonuses, promotion, equity, team structure and workforce planning.

Share the value people create.

When a small number of people create dramatically more productive capacity, there is a strong economic argument for sharing more of the value they create with them.

Higher compensation, bonuses, equity, profit sharing or broader responsibility may follow. This is an economic implication, not a salary formula.

Some people raise everyone else’s AP.

Agentic Engineers build and improve the instructions, tools, workflows, coordination and evaluations that help an organization use AI effectively. Their value can extend far beyond their personal output.

Capacity and economics tell you what is possible. Human judgment determines which outcomes deserve that power.

3 · Direct your power

Power is not the destination.

AP is the power. You choose where it goes.

Horsepower does not tell you where to drive.
Agentic Power does not tell you what to build.

Higher Agentic Power, well directed with good judgment, should increase an individual or organization’s ability to achieve the outcomes it cares about.

Those outcomes may be revenue, profit, retention, customer satisfaction, quality, software released, research completed, innovation, cycle time, cost reduction, lives improved, risk reduction or human time returned.

Track the power alongside the outcome.

AP ↑ Profit ?

AP ↑ Revenue ?

AP ↑ Customer satisfaction ?

AP ↑ Release quality ?

AP ↑ Cycle time ?

AP ↑ Owner / operator time returned ?

If AP rises but the intended outcome does not improve, investigate goal selection, judgment, prioritization, quality problems, organizational bottlenecks, inability to absorb the new capacity or effort directed toward low-value work.

Track comparable windows and changed conditions. A correlation between AP and an outcome does not establish causation.

An early outcome worth pursuing

First, get your time back.

For many people, the first important outcome of Agentic Power is simply getting their time back.

A physician stops spending nights on credentialing.

A founder stops manually assembling reports.

A small-business owner stops doing repetitive administration.

A marketer stops manually researching every campaign.

A developer stops supervising routine implementation.

An operator stops personally moving information between systems.

The initial goal can be simple: free the owner’s or operator’s time while maintaining or improving work quality.

Reclaimed time can become family, health, rest, creativity, sales, strategy, learning, relationships, higher-value work or simply fewer hours worked. You do not have to reinvest every saved hour into more production.

AI can give you time back.
You choose whether to reinvest it, and where.

Efficiency and opportunity

What happens to jobs?

What happens when everyone becomes dramatically more productive? Higher Agentic Power means fewer human hours may be required for many categories of work people are paid to perform today. Some jobs will shrink, disappear, be redesigned, or require fewer people. That disruption is real.

Existing paid work is not the boundary of useful human activity. Nathaniel Whittemore, host of The AI Daily Brief, describes a useful distinction between “efficiency AI” and “opportunity AI.” Efficiency AI applies AI to work we already do; opportunity AI pursues work previously too expensive, slow, specialized, labor-intensive, small in market size, or difficult to staff. Source and attribution.

EFFICIENCY AI vs OPPORTUNITY AI

Efficiency AI

Existing work

  • Faster
  • Cheaper
  • Less labor-intensive
  • More scalable

Lower cost /
less human time

Opportunity AI

Previously uneconomic possibility

  • Affordable
  • Possible
  • Scalable
  • Worth attempting

New product / service /
discovery / work

AGENTIC POWER ENABLES BOTH.

Efficiency AI reduces the cost of work we already do. Opportunity AI expands the set of work worth doing.

History, an example and the limits

The work humans are paid to do today is a bounded set. The opportunity space for new products, services, discoveries, experiences, businesses, and human ambitions is effectively unbounded. That is not a claim of mathematical infinity or unlimited demand.

The Industrial Revolution replaced vast amounts of human physical effort without eliminating human economic activity: it changed what humans did. Machines lowered task costs and made new industries and forms of work possible. AI may do something similar for cognitive work, but its pace, breadth, and social consequences may differ substantially. Historical context.

Agentic Power fuels both sides of the transition. Applied to existing work, it creates efficiency; applied to previously uneconomic possibilities, it creates opportunity. As comparable AI capability costs fall, a small company might test hundreds of product concepts instead of three, or build software for a niche internal problem that never justified an engineering team.

Illustrative thought experiment; fictional numbers: an expert analysis costing $50,000 is uneconomic if the decision it informs is worth only $5,000. If comparable-quality analysis costs $50, thousands of previously unjustifiable analyses could become viable. That is opportunity AI, provided the value, quality, and total delivery cost hold up.

AI will likely eliminate some jobs, change many more, and create work that does not exist today. An identity built on “I perform these tasks” is more exposed when AI can do them better, faster, and cheaper. A stronger position is learning Agentic Engineering: identify valuable problems, define goals, exercise judgment, build AI staff, direct teams, evaluate outcomes, discover opportunities, and improve systems. These capabilities can strengthen a person’s position without guaranteeing employment.

As Agentic Power increases, the human role can move up the AI Staff Delegation Ladder: from doing tasks to directing roles, managing outcomes, setting goals and constraints, and deciding what deserves to be done at all. The practical aim is to become someone who can direct AI toward useful outcomes. Jobs, displacement, and new work: the deeper discussion.

Our work moves upward.

We move from doing the work to directing increasingly capable digital organizations.

Doing→Directing→Designing→Deciding

Agentic Power gives us a way to measure how much productive capacity that transition creates. Leadership determines what we do with it.

“My mission is to increase my own Agentic Power, help others increase theirs as quickly as possible, and direct that power toward good.”

Dr. Mark Allen

At HeroForge.AI, that means helping people and organizations become better Agentic Engineers, increase their Agentic Power, and direct that capacity toward outcomes that matter.

Agentic Power multiplies execution.
Human judgment determines where that power goes.

Methodology and endnotes.

Measurement rules and limitations

Work Produced and a credible reference

An AP number is only as credible as its Skilled Hours estimate. Good AP measurement makes the baseline inspectable. Count the Work Produced: everything your AI made during the audit period that you’re keeping, finished or in progress.

Estimate the effort a competent skilled person or team would reasonably need for the same result at the same quality. For unfinished work, count only the part that’s done. Record scope, skill assumptions, evidence, uncertainty and deduplication. A human-only deliverable does not enter an AI operation’s numerator. Do not count raw output, discarded or abandoned work, duplicates, failed work, token volume, agent runtime, internal machine actions or output that humans must recreate.

For mixed work, attribute only the AI-produced portion you’re keeping. Count intermediate outputs only when independently useful and not also counted inside a final deliverable.

All Direction Hours

Include setup, training, context, tools, review, failed-attempt correction, coordination, exception handling, evaluation, maintenance and system improvement. Use active person-hours across everyone who directs the AI, with shared effort reconciled.

Preserve both a representative current window and earlier investment in the full-project view. Do not hide setup costs by selecting a convenient interval. Zero or unknown Direction Hours never yield infinity.

Individual and organizational AP

Use the same rule for a workflow, project, individual, team, department or company: total Skilled Hours divided by reconciled Direction Hours. Never average individual AP ratios. Avoid counting overlapping work products or shared human time twice.

AP comparisons are useful when scope, quality, baseline method, available tools and working conditions are sufficiently comparable. They need not be limited to one person over time.

A hiring use case

A standardized evaluation could hold constant the desired outcome, quality threshold, available tools, time window and security / policy constraints. Then record the Work Produced, Skilled Hours, Direction Hours, AP, elapsed time, judgment and final quality.

This can reveal productive capability and inform rewards. Disclose assistance and inherited assets; examine repeatability and contribution to others’ performance. This is a methodology direction, not a recruiting product or a salary formula.

AP’s limits

Agentic Power measures human-attention productivity, not compute efficiency; AI and infrastructure cost should be tracked separately.

AP does not measure intelligence, organizational health, resilience, strategic judgment, economic value or beyond-human capability. Track it alongside intended outcomes, costs, quality and risk. AP alone does not establish ROI or causation.

Evidence, terminology and case limits

Skilled Hours estimate what comparable human work would have required; they are not observed replacement labor. The cases use retrospective or production-history estimates; they are not independently audited benchmarks. Report elapsed time and uncertainty with every serious result.

The framework is proposed management terminology, not an externally validated industry standard. Agentic Engineering terminology is evolving rapidly; Graph Engineering in particular remains an emerging term.

Measured: Direct observation Estimated: Stated basis Derived: Arithmetic from inputs Illustrative: Explicit assumptions Self-reported: Subject’s claim Unknown: Insufficient evidence

Read the full standalone Audit method.

See how the Audit works

Choose a representative window and a consistent quality bar. Inventory accessible evidence, identify the Work Produced you’re keeping, count each item once, estimate competent-human effort, and reconcile all attributable human attention. Missing evidence stays visible.

Both audits start with an instant AP, with Direction Hours estimated by AI, then ask one question about your directing time for the accurate result. The full Evidence Profile adds AP$ when evidenced, Skilled Hours, Direction Hours, the Work Produced list, Equivalent Labor Cost, Direct AI Cost, Direction Cost, Total Attributable Agentic Cost, Elapsed Window and Intended Outcomes. Direction Cost is required for AP$; when unknown, show “AP$ pending Direction Cost.” Diagnostics cover delegation, autonomy, Agentic Engineering, self-improvement, the bottleneck and the Next Power Move.

Use the Chat Audit for work done in chatbots and the Agent Audit for work done by coding agents and agent tools. The Agent Audit guide holds the detailed accounting, evidence collection, diagnostic classifications and public-safe export rules.

Detailed fictional Profile and evidence

Your Agentic Power Profile

An operation, in perspective.

FICTIONAL EXAMPLE · ILLUSTRATIVE RATES

Example period: seven complete 24-hour days · Direction Hours: 10.5 · Coverage: partial, 3 of 4 declared evidence sources collected. Figures below describe covered work only.

YOUR AGENTIC POWERAP ≈ 17.5×

184 Skilled Hours ÷ 10.5 Direction Hours

AGENTIC POWER EFFICIENCYAP$ = 10.8×

Current Operating AP$ · USD 17,100 ÷ USD 1,585

Labor market: United States national (fictional assumption) · Rate basis: custom internal rate · Source: fictional Profile assumptions, not observed benchmarks · Source date: September 2026 · Currency: USD.

Skilled Hours184 hours
Direction Hours10.5 hours
Measurement Window7 days

What your AI produced

Fictional Work Produced list. Each entry has a distinct output, evidence record and Skilled Hours estimate; none is a real client record.

ItemEvidence referenceSkilled Hours
Customer research reportDEMO-01 / owner sign-off60
Released reporting applicationDEMO-02 / acceptance checks70
Sales campaign packageDEMO-03 / approved delivery30
Three completed administrative processesDEMO-04–06 / completion records24

Six kept outputs total 184 Skilled Hours. The in-progress item has no finished part yet, so it adds nothing in this example.

What would comparable human capacity cost?

Equivalent Labor Cost: USD 17,100. Fictional category rates: research 60 Skilled Hours × USD 80 = USD 4,800; software 70 Skilled Hours × USD 120 = USD 8,400; marketing 30 Skilled Hours × USD 90 = USD 2,700; administration 24 Skilled Hours × USD 50 = USD 1,200. Each category uses its existing Skilled Hours, with no overlap.

What did the AI operation cost?

Direct AI Cost: USD 220. Direction Cost: 10.5 hours × USD 125 = USD 1,312.50. Other attributable operating cost: USD 52.50. Total Attributable Agentic Cost: USD 1,585. All are fictional current-window assumptions; no general overhead is allocated. Historical build investment is not estimated, so Fully Loaded AP$ remains pending.

Illustrative AP$ scenario range: AP$ = 8.8×–13.2×. Central estimate: 10.8×. Skilled Hours held fixed; category labor rates vary ±10%, giving Equivalent Labor Cost USD 15,390 / 17,100 / 18,810. Total attributable cost varies USD 1,426.50 / 1,585 / 1,743.50 (each cost component ±10%). Low divides 15,390 by 1,743.50; high divides 18,810 by 1,426.50. These are scenarios, not a statistical confidence interval.

What was this power directed toward?

Return routine operator time while maintaining quality. Intended goal: no more than 6 routine Direction Hours per comparable weekly workload, with all Work Produced meeting the documented quality bar.

Previous comparable windowAP = 12×

180 Skilled Hours / 15 Direction Hours
9 routine hours

Current covered windowAP ≈ 17.5×

184 Skilled Hours / 10.5 Direction Hours
6 routine hours

Fictional DEMO-TIME-01/02 and DEMO-QA-01/02 records. Both windows cover the same three surfaces and quality bar; the fourth remains uncollected. All kept outputs clear that bar. Total Direction Hours include improvement effort; routine time is a subset. The changes coincide; this comparison alone does not prove causation.

AI Staff Delegation Profile

Share of Skilled Hours

Assistant12%
Employee46%
Team34%
Department8%

Company 0% · Portfolio 0% · Unknown 0%
Dominant: Employee · Highest evidenced: Department

Autonomy Profile

Share of Skilled Hours

Interactive42%
Delegated31%
Scheduled12%
Event-Driven15%

Persistent 0% · Unknown 0%

Capacity Allocation

Efficiency 45% · Learning 25% · Growth 20% · Unknown 10%

Share of Skilled Hours by intended capacity destination. This is not financial value.

Reliability & Economics

6 kept · 1 in progress · 1 discarded · 1 unverified

Kept work enters Skilled Hours. Unfinished work counts only for the part done; discarded work counts nothing. Fictional attributable-cost assumptions are shown above; no realized savings or ROI claimed.

Agentic Engineering Profile

These labels describe evidence of use: not yet used, emerging, repeated, or built into a maintained working process (operationalized). They are not an overall score.

Prompt Engineering
Repeated
Context Engineering
Operationalized
Harness Engineering
Emerging
Loop Engineering
Emerging
Graph Engineering
Not yet used

Strongest capability: Context Engineering. Example records show maintained source packs and quality checks used in every recurring briefing.

Largest opportunity: Loop Engineering. Several proven workflows still depend on human initiation and handoffs.

Goal-Directed Self-Improvement

Self-improvement status: S2 Assisted

Fictional evidence: the explicit goal is reducing routine operator attention with quality maintained. Failure notes and successful patterns are captured. An agent proposes Skills changes; a human approves and applies them. The proposed changes also consider context, tools and workflow. Evaluation records are incomplete; permission changes and higher-risk updates require human approval.

Next experiment: record each evaluation and compare a proposed workflow change against the current version before adoption.

See improvement status definitions.

Primary BottleneckPeople coordinating specialist handoffs

Fictional evidence: 6 of 10.5 Direction Hours went to manually managing specialist handoffs.

Next Power MoveCoordinate proven AI Employees behind an AI Team Leader.

Build one evaluated handoff loop. Record failures and successful patterns, propose a Skill update for human approval, then compare Direction Hours and quality before expanding it.

Improvement Audit: who owns the improvement process?

S1–S5 describe increasing AI ownership of improvement. S1 is human-led. Any level can use a systematic, continuous process.

S1 Manual
People identify, test and apply improvements.
S2 Assisted
AI suggests or helps with changes; people drive the improvement process.
S3 Managed
AI runs an assigned improvement cycle and proposes tested changes; people approve adoption.
S4 Adaptive
People define the improvement targets or categories; AI tests and adopts changes within them. Higher-risk changes need approval.
S5 Self-Directed
AI identifies and prioritizes what to improve in its skills or system, tests changes, and retains demonstrated improvements within defined permissions.

S4 improves within human-defined targets or categories; S5 also identifies and prioritizes what to improve, guided by human-set goals and constraints. A5 proactively advances the work; S5 directs improvement of the skills or system doing it. Neither level establishes the other. Require evidence of evaluation, retained changes, rollback and escalation. Persistent runtime alone does not establish an improvement status. Keep unknowns unknown; greater ownership alone does not establish higher AP.

What the Audit asks about engineering and improvement

For each engineering practice, supply examples, repeated use, maintained procedures, and evaluation evidence. Classify only where supported: Not yet used, Emerging, Repeated, or Operationalized. Missing evidence stays unknown.

  • Are goals explicit, with measurable outcome criteria?
  • Does the system capture failures and successful patterns?
  • Are evaluations recorded?
  • Are Skills updated from evidence?
  • Does context improve over time? Do tools and workflows improve?
  • Can agents propose improvements?
  • Which changes happen automatically, and within what boundaries?
  • Which changes require human approval?

The answers inform the Next Power Move. They do not produce a fake overall engineering score.

Try the AP calculator

Try the arithmetic

A quick calculation.
A clearer question.

Use a dated window, a consistent quality bar, and a documented skilled-human baseline.

Elapsed time is wall-clock time, including nights and waiting. It is not summed agent runtime. Direction Hours include everyone’s briefing, setup, reviewing, fixing and coordination.

One headline number. Transparent inputs.

Illustrative inputs. Replace them with your own evidence. Nothing is sent or stored.

Your Agentic PowerAP = 100×Skilled Hours ÷ Direction Hours
Skilled Hours100 Skilled HoursWork Produced at a comparable quality bar
Direction Hours1 hour
Elapsed Window2 daysOperational context

100 Skilled Hours ÷ 1 Direction Hour so AP = 100×. Elapsed window: 2 days. Illustrative arithmetic.

Keep uncertainty visible.

Skilled Hours are estimates. Document the competent-human reference, task boundaries, and evidence. Use ranges when justified; do not manufacture precision. Unlike tasks are not automatically comparable.

Date the improvement.

A current-performance window shows Agentic Power, its inputs, and the AI Staff Delegation and Autonomy profiles. Keep the quality bar constant and flag inherited work. The full-project view retains setup, training, failed attempts, corrections, all Direction Hours, and earlier investment.

Count the whole scope.

Count the Work Produced once. Keep time spent on failed attempts in the denominator. Include everyone who directs the AI; separate manual production. Zero output gives zero AP when Direction Hours are positive.

Jobs, displacement, and new work

Tasks are not whole jobs

AI can reduce the labor required for existing tasks. Jobs also bundle relationships, judgment, accountability, coordination, context, and decision-making. Automation may remove parts of a job before an entire role disappears; employers may also reorganize the remaining work across fewer people. Some occupations will likely shrink or disappear, while many others change substantially. Task exposure is not an observed job-loss count. The ILO’s task-based research helps distinguish these questions. Sources and limits.

How falling costs open new work

The economics layer supplies a mechanism, not an employment forecast. As Agentic Power rises and unit AI capability cost falls, the cost of producing comparable-quality skilled outputs can fall. Direction Hours, verification, integration and other attributable costs still matter. Higher AP alone does not establish a lower total cost or a valuable outcome.

When the cost of producing intelligence falls, work that was previously too expensive becomes economical. A service affordable enough to use once a year may become worth using weekly or continuously. Falling AI capability costs do not only threaten existing labor economics. They can create demand for entirely new forms of cognition and work. That demand depends on real usefulness, budgets, trust, and adoption; it is not automatic.

The fictional $50,000 / $5,000 / $50 example in the main text illustrates a cost-benefit threshold, not an observed price, realized saving, or claim that all expert analyses can be automated. Comparable quality and the full cost of delivery must be established. Individualized tutoring, niche internal software, broader scientific hypothesis testing, and a solo entrepreneur serving a previously unstaffable market are possibilities, not measured outcomes.

From efficiency to opportunity to beyond-human scale

This is a conceptual progression, not a new metric, axis, or branded framework:

  1. Efficiency AI: humans already perform the work; AI lowers its time or cost. For example, drafting a contract faster, subject to appropriate review.
  2. Opportunity AI: humans could perform the work, but it was previously too expensive or impractical. For example, reviewing every historical contract in a small company once the marginal cost is low enough.
  3. Beyond-human-scale capability: the same scale of work would never realistically be staffed with humans. For example, continuously evaluating millions of legal, scientific, security, software, or economic scenarios.

The existing Skilled Hours boundary still applies. Estimate reasonable human effort for the comparable outcome you’re keeping, not every internal machine search. When no defensible skilled-human baseline exists, describe the capability separately and exclude it from AP. Opportunity does not license invented Skilled Hours or hypothetical labor-cost totals.

Opportunity does not erase transition pain

Industrial automation both displaced physical tasks and enabled new economic activity. That history offers a mechanism to examine, not a promise that AI will repeat the same employment trajectory. Cognitive automation may affect a broad range of occupations simultaneously. Timing, wages, bargaining power, geography, access to training, and the distribution of gains remain uncertain.

Aggregate future opportunity does not mean each displaced person can transition easily, quickly, or at comparable pay. New activity can benefit different people and places from those bearing the losses. Building judgment, goal selection, evaluation, and opportunity discovery can strengthen a person’s position; it is not employment insurance, and individual adaptation cannot carry the whole social burden.

The Agentic Power Framework makes no macroeconomic employment forecast. The same productive-capacity change can reduce labor demand for existing work and make new categories of work viable. Which force dominates aggregate employment is outside the framework’s empirical claim. The purpose here is to understand that underlying transformation and direct increasingly capable AI toward useful outcomes.

When Skilled Hours stop making sense

Agentic Power works especially well when AI accelerates work competent humans already know how to perform: software, research, analysis, administration, marketing, legal work, design and operations.

AI also enables work humans would never economically perform at the same scale. A system might simulate ten million software architectures overnight, correlate billions of security events, search enormous molecular or materials spaces, connect new papers across thousands of journals or test millions of pricing and supply-chain combinations.

It would be misleading to equate ten million machine experiments with ten million human experiments. No real business would have hired people to perform that internal process.

Measure the human-comparable outcome you keep, not the machine’s internal search.

Internal machine activity10 million architecture searches

Do not convert each search into hypothetical human labor.

Outcome you keepOne expert-quality production architecture

Estimate the reasonable human effort for a comparable architecture.

A security system may inspect billions of events. Where possible, estimate the comparable investigation, diagnosis, remediation plan or incident resolution / prevention work. Do not price the impossible manual reading of every log, or count speculative incidents prevented without evidence.

When even the final outcome has no human analogue

If no credible skilled-human baseline exists, do not manufacture Skilled Hours. Report:

“Beyond-human-scale capability: no defensible Skilled Hours baseline.”

Describe the outcome separately. Do not convert it into AP or silently include it in an aggregate numerator. A mixed operation can report AP for the defensible human-comparable scope and list the excluded capabilities alongside it.

Agentic Power measures AI-amplified skilled work that people could also do. Some of AI’s most important future value may eventually come from work that was never economically possible for humans to perform at all.

If comparable AI capability continues to become cheaper, this boundary may become increasingly important. It is an open area for future work; this framework does not invent another metric to resolve it.

Origins of the Agentic Power Framework

The phrase ‘agentic power’ has appeared previously in unrelated academic and AI contexts. The Agentic Power Framework defined here uses the term specifically for Skilled Hours per Direction Hour: skilled work produced for each hour a person spends directing AI.

  1. September 6, 2026

    The OpenAI spark

    OpenAI published “Research acceleration: The view inside OpenAI.” It reported approximately 3.1 agent-workdays of effort for every human workday in OpenAI Research.

    This raised a question for Dr. Mark Allen: what does an agent-workday actually tell us about useful skilled output? Runtime is not necessarily value. Agent count is not necessarily useful work.

  2. September 10, 2026 · Vibecast

    The rUv conversation

    Dr. Mark Allen discussed the measurement problem with Reuven Cohen, rUv. rUv independently presented experimental “agentic horsepower,” called APX, using an exponent-style formulation to compare human and agent output.

    Both agreed that a unit for human-versus-agent productive output was needed. Dr. Mark Allen advocated a direct multiplier businesspeople could understand without reversing an exponent. He raised the importance of skilled work the operator could not personally perform and discussed client examples.

    Autonomy emerged as a critical multiplier, and rUv demonstrated his persistent autonomous operating model. The Rowena + Emmanuel case uses 400 combined hours and AP = 55×.

  3. The current framework

    A direct management measure

    Dr. Mark Allen subsequently developed the Agentic Power Framework around AP = Skilled Hours ÷ Direction Hours, where Skilled Hours count the Work Produced, finished or in progress, together with the AI Staff Delegation Ladder, the Autonomy Levels, Agentic Engineering, Goal-Directed Self-Improvement and the Agentic Power Audit and Profile.

OpenAI did not create or endorse Agentic Power. rUv is not represented as endorsing Dr. Mark Allen’s final framework. Dr. Mark Allen did not invent APX. APX and Agentic Power are distinct metrics. The supplied automatic transcript was inspected; it supports the spoken estimate but is not independently verified raw audit evidence. Dr. Mark Allen supplies the Codex-run detail.

A reference you can share

How to cite this framework.

Dr. Mark Allen. “The Agentic Power Framework.” Version 1.7.0, October 2026. HeroForge.AI. https://heroforge.ai/agentic-power.

The Agentic Power Framework
Version 1.7.0 · October 2026
Developed by Dr. Mark Allen at HeroForge.AI

Download the foundational paper (PDF, version 1.5.0)
Explore the shareable framework cards

Permanent publication: HeroForge.AI/agentic-power. No DOI or external archival record is claimed. Version record.

The PDF is version 1.5.0 and uses older wording. This page is current.

Plain definitions

Glossary

The measure

Agentic Power (AP)
The skilled work your AI produces for every hour you spend directing it. AP = Skilled Hours ÷ Direction Hours.
Work Produced
Everything your AI made during the audit period that you’re keeping, finished or in progress. Unfinished work counts for the part that’s done.
Skilled Hours
How long skilled people would need to do the Work Produced. For unfinished work, count only the part that’s done.
Direction Hours
Your time directing the AI: briefing, setup, reviewing and fixing.
Top 1% quality
Work that meets the standard of the best people in the field.
Audit Period
The time window being measured. Default is the last 30 days; you can choose another.
≈
An estimate.

The audits

Chat Audit
The audit for chat AIs like ChatGPT, Claude and Gemini.
Agent Audit
The audit for AI agents like Claude Code, Codex and Cowork.
Satellite Audit
A short audit you run in your other AIs, then paste back so results merge without double counting.
Instant AP
Your first result, with Direction Hours estimated by AI.
Accurate AP
Your result after you answer one question with your own Direction Hours.

The economics

Agentic Power Efficiency (AP$)
Equivalent Labor Cost ÷ Total Attributable Agentic Cost.
Equivalent Labor Cost
What skilled people would have cost to do the same work.
Total Attributable Agentic Cost
What the AI work cost you: subscriptions, usage, tools and setup.

Delegation: how much responsibility you hand AI

AI Assistant
Tasks.
AI Employee
A recurring role.
AI Team
An outcome.
AI Department
A business function.
AI Company
Organizational goals and constraints.
AI Portfolio
Governing multiple AI-run operations.

Autonomy: what starts the work

A1 Interactive
It waits for you after every reply. If you walk away, the work stops.
A2 Delegated
You hand off a whole job. It keeps working without you and comes back when done or stuck.
A3 Scheduled
A time or cadence starts the work.
A4 Event-Driven
An event starts the work.
A5 Proactive
It finds and starts useful work toward a goal, within set permissions.

Building the operation

Agentic Engineering
Designing how AI does the work: Prompt, Context, Harness, Loop, Graph.
Harness
The AI’s working environment: tools, memory, permissions and controls.
Loop
Repeating, checking and correcting until the work meets the goal.
Skills
Reusable instructions that teach AI staff a job.
AI staff lifecycle
Hire, Onboard, Train, Deploy, Promote.
Agentic Power Profile
Your full readout: AP, Work Produced, delegation, autonomy and economics.

Where AP points

Efficiency AI
Doing existing work with less of your time.
Opportunity AI
Doing work that was too costly to do before.
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