Build your team's Agentic SDLC. Four weeks, on your real codebase.

Vibe coding vs. agentic engineering

We distinguish four different ways of working:

• one-off code generation,
• interactive pair programming,
• delegating tasks,
• autonomous execution inside a defined process.

What a production AI-assisted SDLC looks like

We analyse the actual flow:

• idea → UX → spec → tech design,
• implementation → PR → review → E2E,
• dev → sanity check → staging → product acceptance.

For each stage we assign: Human / Agent / Human + Agent.

Autonomy boundaries

We create the first decision matrix:

• Agent can decide independently.
• Agent proposes — human approves.
• Only a human decides.

Architecture remains a team responsibility. Agents may analyse and implement accepted patterns, not redefine system foundations alone.

The repository as the agent's workplace

We inspect what the agent must be able to understand:

• repository structure and architecture,
• coding standards and conventions,
• documentation and tests,
• CI, observability and security boundaries.

First Agentic Readiness Review

We assess architecture, separation of responsibilities, testability, documentation, CI, observability, design-system constraints and security boundaries.

We do not try to fix everything. We identify the constraints that most limit delegation.

What would a strong new developer need to know?

If a good engineer must learn something before safely changing this repository, the agent also needs a reliable way to access it.

Specification-driven development

A good specification defines:

• the problem and expected behavior,
• scope and out of scope,
• acceptance criteria and edge cases,
• dependencies and unknowns.

It does not need to dictate every implementation detail.

Grill the spec

The agent acts as a critical partner before implementation. Its job is to find ambiguity, contradictions, missing cases, hidden product decisions and missing acceptance criteria.

The cheapest bug is the one found before implementation begins.

Tech design

After the spec stabilizes, the agent can inspect the repo, find similar implementations, propose an approach and analyse consequences.

Humans authorize architectural decisions.

Skills as an organizational standard

A skill records a repeatable way of performing work. It is not a “superprompt.” It becomes part of the team's operating system.

Skills as a living product

We watch for recurring failure signals: the same mistake, overly academic feedback, irrelevant optimization, inconsistent results or model changes affecting behavior.

Then we improve the skill — not every future conversation manually.

start-feature

How the agent knows it is done

We define an Agent Definition of Done. Typical evidence:

• acceptance criteria satisfied,
• tests passing and new behavior covered,
• lint / typecheck passing,
• review agent finds no blockers,
• required PR description completed,
• key decisions can be explained.

Agentic code review

A separate reviewer agent receives the spec, tech design, coding standards, PR scope and severity policy.

We focus on issues that materially affect the system.

Testing as a feedback loop

Automated tests become a core communication mechanism between the system and the agent.

Integration and E2E tests become especially important as autonomy increases.

Accepting work without reading every line

The default question shifts from:

“Do I understand every change in the diff?”

toward:

“Do I have enough evidence that the change satisfies the spec and does not damage the system?”

What builds confidence

• specification,
• tests,
• agentic review,
• observability,
• control questions,
• system behavior and sanity check.

This does not ban human code review. It changes the default way trust is established.

Run one real ticket end to end

spec → tech design → implementation → agent review → tests → human acceptance

If the organization is ready, the target is to reach at least a test environment.

The team runs the process

The Tech Lead selects a new task and guides the team through the full cycle. We observe and intervene only when the system requires it.

Failure analysis

We study where the system did not work:

• Why did the agent make the wrong decision?
• What context was missing?
• Was the spec insufficient?
• Do we need a new or less aggressive skill?
• Was a test missing?
• Did architecture block delegation?
• Was the problem outside a reasonable autonomy boundary?

Observability

Agents should have the diagnostic information they need without unnecessary production permissions.

We design access to logs, traces, errors and staging behavior — without giving the agent autonomous production deployment or modification rights.

Economics

We analyse model cost, task execution cost, rework, human time and lead time.

The optimization target is not the cheapest model. It is the cost of delivering a correct feature.

How the system evolves after the pilot

We define:

• the process owner,
• the skills-library owner,
• how new skills are proposed and reviewed,
• how context is updated,
• the cadence of AI-SDLC retrospectives.

Your team runs a task independently

The team takes a real task through the new process. We act as observers and reviewers of the system.