Agenda
Built around human execution
Specs in · Evidence out
Who decides · Who verifies
Press → or tap to start
Part I
The Old Model
Built around human execution — an assumption that is quietly ending.
Every practice in software development rests on one assumption: a human will do the work.
True for seventy years.
No longer.
Machine-speed execution.
Human-speed process.
That is not progress. That is a mismatch.
Code
written faster
Requirements
still vague
Design
still implicit
Verification
still manual
Governance
after the fact
Every practice exists because of a human limit.
- Stand-up
- mental states must be synchronized
- Story points
- capacity is finite and variable
- Code review
- humans make mistakes
- User stories
- communication is lossy
- Roles
- one brain can't hold it all
The SDLC is coordination machinery for human execution.
Would this practice survive if the executor were a machine?
Stand-ups. Estimation. Code review. Org charts.
Principles survive. Practices often don't.
Survives
Working software, frequently. Integration. Automated tests.
Fades
Sprints. Stand-ups. Story points. Ceremonies.
Agile is not obsolete. It is incomplete.
There is no AI team.
An AI PM, an AI architect, an AI QA — a human team, just faster?
- Roles exist for limits, coordination, accountability
- An agent is one intelligence, one context
- Job titles import human limits into machines
When you hear an agent described with a job title, ask: what deliverable does it produce?
Negotiable — the most dangerous property
An agent cannot join the conversation. It guesses.
Inference is not negotiation.
- PPrecise — no ambiguity
- RReviewable — at the right level
- EEvidenced — proof of completion
- CComplete — criteria, constraints, failures
- IIndependent — verifiable alone
- SSmall — precise + complete
- EExecutable — machine-actionable
Part II
Machine Execution
Specifications in. Evidence out. Harness in between.
Specifications are the interface
Plain English is not a specification.
- Requirements
- what was intended
- Design
- why this approach
- Tasks
- what was executed
- Evidence
- did it satisfy intent
Precision is not bureaucracy. It is what makes autonomy governable.
Tokens are not quality.
A million tokens to build the wrong thing is waste — at machine speed.
Correct deliverable → Verified outcome → Business value Tokens appear nowhere in this chain.
"Which model?" is the wrong first question.
Context
what it sees
Tools
what it can do
Constraints
what it cannot
Memory
what persists
Feedback
tight loops
Validation
objective gates
Checkpoints
where it stops
Permissions
least privilege
A powerful engine and no steering wheel is not a fast car. It is an accident.
Autonomous coding ≠ autonomous development
Intent → Requirements → Design → Tasks → Implementation → Verification → Deployment → Monitoring ▲ autonomous coding addresses exactly one link
- No requirements
- No design
- No evidence trail
Part III
Governance & Synthesis
Who decides. Who verifies. Who is accountable.
Human-in-the-loop is not a checkbox
Theater
5,000 lines. 30 files. One "approve".
Governance
Requirement. Design. Evidence. Decision.
Both had a human in the loop.
Six levels of human involvement
- 0 · None
- reversible actions
- 1 · Notification
- review after the fact
- 2 · Approval w/ override
- human can block
- 3 · Approval required
- production, compliance
- 4 · Human-directed
- human defines the approach
- 5 · Human-executed
- judgment only
Review intent. Review approach. Review evidence. Code — by exception.
Two lists.
What you built — and what you documented to prove it.
- The requirement — already exists
- The history — already recorded
- The tests — already run
- The approvals — already logged
The evidence package assembles itself.
Eight stages. Four gates. One evidence trail.
Intent ↓ Requirements ─────────────── ○ Human review gate ↓ Design ─────────────────── ○ Human review gate ↓ Tasks ─────────────────── ○ Human review gate ↓ Agentic execution ↓ Verification ────────────── ○ Human review gate ↓ Deployment ↓ Evidence (continuous, produced by the process itself)
Not waterfall — cycles in minutes, evidence every time.
The whole argument
A mental model that fits on one page.
The leverage shifts
Developers
specify · constrain · verify
Architects
govern decisions
Product managers
define intent
Managers
manage the system
Test engineers
design verification
People don't disappear. Their leverage changes.
The old question
How do we organize people to execute software work?
The new question
How do humans specify, constrain, verify, and govern machine execution?
The executor becomes the director.
Closing
Stop fitting agents into our old organization. Redesign the lifecycle around what machines can do.
From Agentic SDLC — Rethinking How We Build Software with AI Agents, by Leonardo Sampaio.