SkyWaysThe agentic manual

The agentic PDLC for business sponsors: ask for two numbers

You are the only person on the programme with no delivery deadline, which is exactly why the governance loop, and the stop decision, are yours.

6 minutesBeginnerLesson 8 of 9Updated By

On this page
  1. Sound familiar?
  2. What changes for a sponsor?
  3. Your part, step by step
  4. Where you'll use it
  5. Why it matters
  6. Try it
  7. Key takeaways
  8. FAQ
  9. Apply it in your role
  10. Sources and credits
All lessonsStart here

Getting started

  1. What is the agentic PDLC?
  2. How this tutorial works

Agentic PDLC fundamentals

  1. The evolution of the PDLC
  2. Why agentic AI projects fail
  3. P0 · Frame
  4. P1 · Design & Spec
  5. The one hard gate
  6. P2 · Build & Prove
  7. P3 · Run & Learn
  8. The eight loops
  9. The evidence pack

Methods decoded

  1. AI-DLC vs AIDD vs agentic SDLC
  2. What is AI-DLC?
  3. What is AIDD?
  4. What is the BMAD Method?
  5. What is spec-driven development?
  6. One lifecycle for every method
  7. How much process a change needs

Running delivery

  1. How to run an agentic AI project
  2. Bolts vs sprints
  3. A board for agentic work
  4. Cut delivery from months to weeks
  5. Review AI code by risk
  6. How accurate must an agent be?
  7. Prove the agent meets its bar
  8. Shadow mode and cut-over
  9. Why the AI bill is 4× the estimate
  10. Guardrails that hold
  11. The five governance gates
  12. Catch AI drift
  13. AI incident postmortems

By role

  1. For product managers
  2. For program managers
  3. For solution architects
  4. For engineers
  5. For forward-deployed engineers
  6. For QA
  7. For DevOps and platform
  8. For business sponsors
  9. For executives

Teams and organisation

  1. Team structure for agentic AI
  2. Measuring AI productivity
  3. The maturity model
  4. Rolling it out in 90 days

Practice

  1. The SkyWays case study
  2. The operating rhythm
  3. The workbench
  4. Twelve exercises

Interviews and careers

  1. What is an FDE?
  2. Six answer frameworks
  3. AI PM interview questions
  4. FDE interview questions
  5. GenAI engineer questions
  6. Agentic engineer questions
  7. AWS GenAI questions

In short

The business sponsor of an agentic programme owns four things. They are whether it is funded past its first cycle, the autonomy ceiling for the organisation, what will be accepted as evidence, and the governance loop. The sponsor exercises all four by insisting on three reports: two numbers every cycle, a drift readout every week, and an incident brief after every incident.

baselinenowchangeperson-days per story8.04.6−43%token spend per storynone$310review hours added1.22.0+0.8re-runs per storynone1.4baselinenowchangeperson-days per story8.04.6−43%token spend per storynone$310review hours added1.22.0+0.8re-runs per storynone1.4

The review row and the re-run row are what keep the first number honest.

The programme is cancelled on the number you hid, never the one you showed.

Two numbers, and the two rows that stop either being gamed.

In this lesson you'll learn:

  • what a sponsor owns, what a sponsor shapes, and what a sponsor must not touch;
  • the three reports to receive, and what their absence tells you;
  • what to do on the two days that actually test the role: the bill, and the incident.

Sound familiar?#

  • The last three reviews had a screen share and no interval.
  • The first you heard of the cost was from finance, at its worst.
  • Every postmortem ends with a list of actions, and the same thing happens again.

The sponsor's power is not in the reviews; it is in what the reviews are required to contain.

What changes for a sponsor?#

Most of the job is familiar: backing a programme, setting risk appetite, reading a business case, stopping a programme that is not working. What a model in the middle changes is what counts as evidence for each of those:

You have always done thisWhat changes
Backing a programme, and stopping oneThe stop decision rests on evidence you specified in advance: a demo is not evidence
Setting risk appetiteIt becomes an autonomy level per action, not one setting for "the AI"
Reading a business caseThe running cost moves with behaviour, not volume: a bill can multiply on flat traffic
Steering and statusTwo numbers against a dated baseline, with the review hours visible
Asking whether it worksA lower bound per slice, because a score without an interval has said nothing

Your part, step by step#

Step 1 · Know what is yours, what you shape, and what you must not touch#

You ownYou shapeYou must not touch
Funding past cycle oneThe roadmap, the product manager'sThe verdict on a slice
The organisation's autonomy ceilingThe architecture, the architect'sWhich slice ships first
What you will accept as evidenceThe bar, which is derived, not chosenThe gate decisions
The governance loop, and the name on itThe design

The temptation is to reach into delivery when a number disappoints. It never works, and it costs the role its point: an independent reading of whether the programme is working.

Step 2 · Insist on three reports#

ReportCadenceIf it is missing
Two numbers: the saving beside the spend, with review hours and re-runsEvery cycleYou will hear the cost from finance instead
Drift readout: the output mix against its thresholdsWeeklyBehaviour will change with no deploy and no alert
Incident brief: the missing control, the fix, the next P0On every incidentPostmortems will end in a name rather than a control

Step 3 · Fund on trajectory, not on promise#

A first cycle that saves time and costs more is normal. Fund past it on the trajectory: the review load falling, and the re-run count falling with it. At SkyWays, this manual's fictional airline, day ninety's report showed 43% fewer person-days per story and $310 of tokens per story, with review hours up and the reason they would fall. The programme continued because both numbers came from the team.

Step 4 · When the bill arrives, ask which signature#

A bill several times its estimate on flat traffic is normal in early cycles, and it is a design question. Ask which of four signatures the per-call log shows (tokens per call up, tier mix moved to the expensive model, cache hit ratio down, retries up) and which decision record allowed it. A spending freeze stops the work and teaches nothing. Why the bill is 4×

context1.62,100→3,360tokens×tier1.550%→100%frontier×cache1.371%→9% hit,f = 0.40×attempts1.421.2→1.7per case= 4.4× the estimate, on flat trafficcontext1.62,100→3,360tokens×tier1.550%→100%frontiercache1.371%→9% hit,f = 0.40×attempts1.421.2→1.7per case×= 4.4× the estimate,on flat traffic

Four ordinary habits, each a sensible decision by a careful person.

Fix in order of (factor − 1) ÷ days: context, tier, cache, then the breaker.

They multiply. The largest ratio change is not the largest factor.

Step 5 · When the incident arrives, ask which control#

Ask one question: which enforced control would have made this impossible? Do not accept a name, a reminder or a prompt edit as the answer. AI incident postmortems

Where you'll use it#

  • At the start: name who owns governance. If the answer is "we all do", nobody does.
  • Every cycle: the two numbers, on one line, from the team.
  • On the two hard days: the bill and the incident, each with its one question.

Why it matters#

Programmes are cancelled on the number that was hidden, the one the sponsor saw for the first time when it was worst. The sponsor's three reports make every number arrive early, from the team, with its pair, which is what lets a programme survive a first cycle that costs more than it saves.

Try it#

Your programme's quarterly update shows a single accuracy figure of 91%, a demo, and "costs within budget". What do you ask for before the next funding decision?

Show the answer

The two numbers, per-slice evidence, and the drift readout. Ask for the saving and the spend on one line against the baseline, with review hours and re-runs beside them; the accuracy per slice, each with its sample size and lower bound, against a derived bar; and the weekly output mix since launch. "Within budget" is not a cost per case, 91% is not proof, and a demo is not evidence. Decide on the three reports, not on the update.

Key takeaways#

  1. The sponsor owns funding past cycle one, the autonomy ceiling, what counts as evidence, and governance.
  2. Three reports: two numbers every cycle, drift weekly, an incident brief on every incident.
  3. On the two hard days, ask which signature and which control, never for a freeze or a name.

FAQ#

What does a sponsor do in an AI project?#

Decides whether the programme is funded past its first cycle, sets the organisation's ceiling on what the agent may do without a person, specifies what will count as evidence, and owns governance (the one loop no delivery role is accountable for) by insisting on three regular reports.

How should a sponsor measure an AI programme's ROI?#

With two numbers reported together every cycle: the saving against a dated baseline, and the full cost including tokens, review time and re-runs. Fund past the first cycle on the trajectory of the review load and re-run count, not on a promise.

What should an AI steering committee ask?#

Which of these are rules? What may the agent do without a person, and who decided? What are the two numbers? What maturity level are we, and what is the next control? Those four questions, asked every cycle, take about ten minutes.

Should a sponsor approve AI releases?#

No. Release, behaviour and expansion decisions belong to the product manager and QA, on evidence. The sponsor specifies what evidence is acceptable and owns the governance loop, not the individual gates.

Apply it in your role#

If you are…Do thisThe AI-augmented shortcut
A forward-deployed engineerGive the customer's sponsor the two numbers every cycle, from you, before anyone asks. It is what renews an engagement.Ask a model to draft the sponsor update from the cycle data in five lines.
A product manager or FDPMBring the sponsor decisions, not status: the autonomy ceiling, what counts as evidence, funding past cycle one.Have a model turn your status report into the decisions only the sponsor can make, with options.
A GenAI or agentic AI engineerMake the sponsor's three reports automatic: the two-number line, the drift readout and the incident brief.Ask a coding agent for the scheduled job that assembles all three.

Across the enterprise. Every sponsor in the portfolio receives the same three reports in the same format, so funding decisions compare like with like.

The ten-minute workflow. A sponsor update in the only shape that works:

Here is our programme update: <paste>. Rewrite it for the business sponsor as: the two numbers on one
line (the saving beside the spend, with review hours and re-runs), the decisions only the sponsor can
make this cycle with options and our recommendation, and one risk. Keep it under 200 words.

Sources and credits#

IdeaOriginSource
The sponsor's owns, shapes and must-not-touch; the three reportsOriginal: this manualRole: Sponsor
The four questions and the two-number reportOriginal: this manualFor leadership
Measures reported beside their side effectsBorrowedGrove, A. (1983). High Output Management. Random House
The SkyWays figuresIllustrative: a fictional airlineThe workbench

Prefer GitHub? This track is on the wiki, and this lesson is markdown. Found a mistake? Edit this lesson, or say so.