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AI is changing software development. The organization must adapt.

We help you redesign how your teams build software with AI. In three monthly sprints, we design your Agentic SDLC, prove it with real engineering teams, and build the practices, controls and metrics needed to scale.

Book an intro callAssess your AI readiness

For engineering leaders · platform teams · product organizations · software delivery teams

Swipe left for mission-critical delivery →
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THE OPERATING MODEL

The AI-first software factory

Human direction. Agent execution. Continuous verification.

Large enterprise · Greenfield delivery at scale
The AI-first software factory operating modelSoftware developers guide coding agents. Coding agents connect directly to the enterprise platform for code and docs, approved tools and skills, GitHub and internal data and logs, then implement and test changes. Coding agents submit a pull request for automated verification. Automated verification combines CI build, test and security checks plus a performance harness with agentic code review by a cloud agent and a security agent. Failed checks return to coding agents. The release policy routes low-risk changes to controlled deployment; elevated risk or uncertainty requires engineer approval. Deployment feeds production observation and agent investigation. Proposed fixes return to coding agents and re-enter verification. All stages share human accountability, scoped access, audit trails and platform standards.Approved context & toolsSoftware DevsOutcomes · Acceptance criteriaArchitecture · Risk policyEnterprise context & platformCode & docs · Approved tools & skillsGitHub · Internal data & logsCoding agentsPlan · Implement · Write testsPull request submitted for verificationPullrequestAutomated verificationCI checksBuild · Tests · Security+ perf harnessAgentic code reviewCloud agentSecurity agentRelease policysatisfied?Elevated risk /uncertaintyEngineerapprovalStandard low-risk pathFailed checks return to coding agentsProposed fixesre-enter verificationAgent investigationDiagnose incidentsPropose improvementsProduction observationReliability · PerformanceProduct outcomesControlled deploymentStaged rollout · Health checksRollbackShared foundation: human accountability · Scoped access · Audit trails · Platform standards
1 / 2 · Greenfield delivery
The core gap

Copilot licenses are not an AI strategy.

Becoming AI‑first starts with individual experimentation, grows through shared practices and communities, and reshapes how work is organized. People discover better ways to work with AI, help others adopt them, and redesign workflows around human judgment and AI capabilities.

Becoming an AI-first organization

Experiment widely. Codify what works. Redesign how work gets done.

Explore AI across real work, identify the practices that create value, spread them through the organization, and redesign workflows around what humans and AI each do best.

Swipe to explore the three stages →

  1. 01

    Explore

    Discover AI-first ways of working

    Build the habit of asking: “How would I do this with AI first?”

    Explore planning, analysis, writing, coding, testing, documentation, research, coordination and decision preparation.

    Who
    Individuals · Teams · Early adopters
    How
    Try AI across real work · Compare human-first and AI-first approaches · Share discoveries
    Evidence
    Tasks transformed · Time & quality gains · New workflows discovered · Failure modes
    Controls
    Approved tools · Data boundaries · Safe experimentation
    Governed by
    Guardrails, not approvals · Policy from the Risk & Governance Council · Enforced by the platform
    Moves on when
    Someone else reuses it · An owner is named · A risk tier is assigned
  2. 02

    Codify

    Turn discoveries into shared practices

    Identify what works, make it teachable, and spread it through the community.

    Capture effective workflows, prompts and agent patterns. Turn personal discoveries into organizational knowledge.

    Who
    Practitioners · AI ambassadors · Managers · Domain experts
    How
    Document patterns · Create playbooks · Run peer demos · Develop ambassadors · Coach others
    Evidence
    Practices reused by others · Adoption beyond the original user · Consistent quality · Learning velocity
    Controls
    Peer review · Named owners · Practice standards · Appropriate human verification
    Governed by
    Standards from the AI Office · AI ambassadors in every team · Owners in the agent register
    Moves on when
    Measured gain against a baseline · Approved if high-risk · Funding secured · Impact on roles assessed
  3. 03

    Redesign

    Make AI-first the default operating model

    Reorganize workflows around what humans and AI each do best.

    Update handoffs, responsibilities and review mechanisms. Automate selected steps where appropriate.

    Who
    Team leads · Business owners · Technical owners · Transformation leaders
    How
    Redesign workflows · Reallocate human/AI tasks · Update roles · Embed proven practices
    Evidence
    Adoption across teams · End-to-end cycle time · Quality · Capacity created · Business impact
    Controls
    Accountability · Quality gates · Monitoring · Escalation · Recovery
    Governed by
    Business owners · Steering Committee funding · Risk & Governance Council approval · HR when roles change
    Stays live when
    Reviewed quarterly · Still used and delivering value · Retired if not

Humans + AI: choose how work gets done

AI produces → human judges → AI revises → human decides

Some workflows may evolve from human-first → AI-assisted → AI-first → partially autonomous. More autonomy is not the right destination for every task.

Choose the allocation based on risk, judgment required, reversibility, quality requirements and business context.

Community & learning throughout. Share discoveries · Build playbooks · Develop ambassadors · Coach peers

Governance throughout. Controls grow with impact, autonomy and reliance.

Don’t scale automations. Scale new ways of working.

AI-first becomes an organizational capability when discoveries turn into shared practices — and shared practices reshape how work is organized.

BEFORE YOU SCALE AI, CAN YOUR TEAM ANSWER THESE?

01 / 06
Delegation

What should engineers delegate to AI?

Can your team answer this today?

Scroll to explore, or choose a question above.

Agentic SDLC closes the gap between individual AI usage and reliable software delivery.

The Program

Three sprints to change how your teams work with AI.

One continuous engagement · 3 sequential sprints · Approximately 3 months

A three-month engagement that moves from diagnosis to real-world validation and then to repeatable adoption.

We work with your teams on their actual software delivery environment — existing repositories, workflows, tools and constraints.

The objective is not to produce another AI roadmap. It is to establish an AI-enabled software delivery model that has been tested on real engineering work and can be scaled across the organization.

  1. Sprint 1 — Assess & Design

    Month 1

    Define how your teams should work with AI.

    We start by understanding how software is delivered today, where AI is already being used, and where the greatest opportunities and risks lie.

    Together, we define the first version of your Agentic SDLC operating model and select the real engineering workflows that will be used to test it.

    We cover

    • AI Engineering Maturity
    • Current workflows
    • Roles & responsibilities
    • Human/agent delegation
    • Tooling & platform constraints
    • Governance
    • Baseline metrics
    • Pilot selection
    Outcome

    Your Agentic SDLC blueprint and pilot plan.

    A concrete operating model, agreed success criteria and a clear starting point for experimentation.

  2. Sprint 2 — Pilot & Prove

    Month 2

    Test the model on real software delivery.

    The second sprint moves from design to evidence.

    We work alongside selected engineering teams on real delivery work — features, refactoring, testing, migration, maintenance or other representative workflows.

    Teams learn by doing, with hands-on coaching and role-based training. Workflows evolve through experimentation. Results are measured against the baseline established in Sprint 1.

    We cover

    • Hands-on coaching
    • Agentic workflows
    • Context engineering
    • Coding & testing
    • Review & verification
    • CI/CD integration
    • Workflow redesign
    • Measurement
    Outcome

    A proven AI-enabled delivery workflow.

    You know where AI creates value, where human supervision remains essential, and which practices are worth standardizing.

  3. Sprint 3 — Operationalize & Scale

    Month 3

    Turn what worked into an organizational capability.

    The final sprint converts pilot learnings into repeatable engineering practices.

    Successful patterns are documented and standardized. Governance, tooling, metrics and responsibilities are clarified. Internal champions are prepared to support the next teams.

    We cover

    • Engineering playbooks
    • Quality gates
    • Governance
    • Platform alignment
    • Metrics
    • Role expectations
    • Internal champions
    • Rollout priorities
    Outcome

    An Agentic SDLC operating model ready to scale.

    A tested way of working, supported by evidence, practices, controls and a roadmap for broader adoption.

Three months. One continuous transformation.

  1. Sprint 1 — Design it

    Assess → Select → Design

  2. Sprint 2 — Prove it

    Pilot → Coach → Measure

  3. Sprint 3 — Scale it

    Standardize → Govern → Expand

From scattered AI usage to a measurable engineering practice.

Human direction. Agent execution. Continuous verification.

Book an intro callSee the maturity scale
AI Engineering Maturity Scale

Where does your team stand?

Self-Assessment for Executives

5 stages from ad-hoc AI usage to full fleet orchestration. Click any level to explore what it means and how to advance.

L2selected level

Key takeaway: AI maturity is not defined by the tools you buy, but by the autonomy of your CI/CD pipelines and the discipline of your supervision model.

Agentic engineering curriculumClaude Partner NetworkMember · Get certified by Anthropic

Working with coding agents: the engineering skills that matter

Five practical modules on specifying tasks, equipping agents, checking their work and coordinating delivery.

Five practical modules · Build reliable agentic engineering habits

  1. Module 1: Master the agent workflow

    • Experiment with agents across analysis, coding, debugging, and documentation.
    • Understand planning, tool use, context limits, and feedback loops.
    • Recognize when to steer, restart, or take over.

    Practice: Complete a familiar engineering task with an agent and examine where your intervention mattered.

    Explore

  2. Module 2: Specify and delegate

    • Define the objective, constraints, invariants, and acceptance criteria.
    • Break work into bounded, independently verifiable changes.
    • Surface assumptions and resolve uncertainty before execution.

    Practice: Turn an ambiguous request into an executable task brief.

    Specify

  3. Module 3: Equip the agent

    • Prepare repository instructions, architectural context, and useful examples.
    • Make tools, builds, tests, and diagnostics accessible.
    • Set permissions, execution boundaries, and stopping rules.

    Practice: Prepare a real repository for repeatable agent-assisted development.

    Equip

  4. Module 4: Verify and debug

    • Review behavior, architecture, and the actual changes.
    • Challenge agent-written tests against requirements and known failure cases.
    • Investigate regressions, edge cases, and performance issues before accepting the result.

    Practice: Find a deliberately introduced defect and produce a reviewed change with validation evidence.

    Verify

  5. Module 5: Orchestrate and own delivery

    • Coordinate agents where tasks can be separated, and manage integration.
    • Establish review gates, escalation, release checks, and recovery.
    • Measure completed work, review effort, rework, time, and cost.

    Practice: Deliver a bounded change through a repeatable workflow your team can reuse.

    Orchestrate

Module 5 builds on the foundations established in Modules 1–4

Finish with a working software change, evidence that it meets the requirements and a workflow your team can reuse.

Classify the task, not the person.

Apply the right level of discipline based on the consequences of failure.

Internal tools can carry high risk.

Internal systems can still impact people, data, operations and reputation.

More agents ≠ stronger assurance.

Assurance comes from discipline, controls and accountability, not agent count.

Autonomy earns trust through evidence, enforced controls and human accountability.

Inside the agent toolkit

Claude Agent SDK, slide by slide

Explore the loop, tools, hooks and guardrails behind an agent. Select a slide to take a closer look.

13 slides · Original deck in English
Open the full deck
Metrics

Is AI actually improving software delivery?

We combine delivery data with quarterly feedback from your teams to track quality, lead time, rework, review effort and cost. Together, these measures show where AI helps, where it creates extra work and what needs to change.

Delivery data · Continuous

What the System Produces

Five groups of metrics track AI use, software quality, delivery performance, agent behavior and cost throughout the development workflow.

Metric groups
  1. AI adoption

    Who is using AI, where, and how often.

    • Active users
    • Tool Adoption Rate
  2. Acceptance & Quality

    Whether the output is trusted enough to ship.

    • Acceptance rate
    • Defect rate
    • Change in test coverage
  3. Velocity

    DORA metrics, segmented by agent involvement.

    • Lead time
    • Deployment frequency
    • Change failure rate
  4. Agent Behavior

    How well agents operate within their guardrails.

    • Escalation quality
    • Supervision burden
    • Goal completion
  5. Cost & Return

    Whether the economics are improving.

    • Token spend
    • Cost per accepted change
Team feedback · Quarterly

What people experience

A quarterly survey combines a shared set of questions with questions tailored to each respondent’s role.

Common baselineFor every respondent

Calibrates role, engagement model, AI usage frequency, autonomy level, and learning posture — the context every other answer is read against.

Role branches
  • DeveloperCoding-side branch

    Covers how AI shows up across the day-to-day developer loop, from authoring to verification, and how agentic tooling is adopted.

  • QA · Automation · Release QualityQuality-side branch

    Covers AI in the test lifecycle — from scenario generation through maintenance, flakiness, and release-readiness decisions.

  • PM · PO · BA · OpsDelivery-side branch

    Covers AI across planning, documentation, reporting, risk, and operational signals — the work around the code.

The questionnaire is proprietary and is provided as part of the engagement.

Audience

Who Agentic SDLC is for

Built for organizations that need AI speed with delivery accountability.

Engineering leaders

You need to understand whether AI coding tools are creating real productivity, where risks are emerging, and how to scale adoption safely.

Platform and tooling teams

You need to integrate AI tools into the engineering environment: repositories, CI/CD, documentation, identity, policies, and internal standards.

Product and delivery leaders

You need to understand how AI changes planning, estimation, review, quality, and delivery predictability.

Software delivery organizations

You need to prepare for a shift from staffing-based delivery to measurable AI-enabled execution.

Nicolas Boitout, Lead Expert — AI Engineering and Digital Transformation Expert, Founder of Agentic SDLC.
Start the conversation

Where could AI make a difference in your engineering teams?

Start with a conversation about your current practices and priorities: skills, workflows, quality or measurement.

Book an intro callAssess your AI readiness

No generic AI evangelism. No tool-only training. The focus is delivery: workflows, quality, supervision, metrics, and adoption.