The AI-Native SDLC Playbook

Anthropic's Applied AI team has published a playbook detailing how enterprises can transform their software development lifecycle (SDLC) into an AI-native process using Claude Code, arguing that traditional approval gates and reviews now bottleneck productivity gains from agentic coding. The guide outlines six stages—planning, design, building, testing, deploying, and maintaining—where AI can automate handoffs and accelerate processes, while keeping humans in the loop. The shift is driven by the fact that code generation is no longer the bottleneck, moving the constraint to planning, review, and deployment stages.

Organizations have started using AI to write code at a speed unthinkable one year ago, yet the processes around the code haven't changed at the same pace. Many engineering teams still have the same approval gates, reviews, handoffs, and policies, stalling productivity gains made by using agentic coding solutions like Claude Code. The software development lifecycle SDLC is the process that takes software from idea to production. Most organizations run some version of the same six stages, covering planning, design, building, testing, deploying, and maintaining software. Traditionally, each stage is a discrete phase owned by a different role. Product managers write requirements, technical architects turn them into designs, engineers build the designs, QA teams at regulated enterprises verify it, releases teams ship it, and operations monitors what is running. Work moves between the phases through documents, tickets, and sign-offs. The traditional software development lifecycle SDLC is process-heavy to ensure accountability and control at each step. However, the traditional SDLC was designed to maximize efficiency in an era where the most time-consuming and expensive stage was writing and implementing code, which is no longer the case. PRDs, estimation rituals, and product security reviews all existed to force alignment during what could be weeks, months, or quarters of development work. The traditional SDLC also features controls that assume every step is performed by humans. The organizations generating the most value have rebuilt their process around what agentic AI can now do, while ensuring that humans stay in the loop. In this guide, we walk through several of our Applied AI team's best practices for integrating Claude internally across each stage of the SDLC to accelerate development and make processes run faster, inspired by working with our customers. When code is no longer the bottleneck and the build phase runs faster than the traditional SDLC allows for, three things become true: The bottleneck moves to the steps to the left and right of the build phase. This is mainly plan, review/test, and deploy, which still run at human speed. The controls stop matching reality and become intractable. Reviewing each line by hand made sense when a person had written it, but it can't keep up once agents write most of the diff. Governance costs increase because exceptions still route through meetings and committees that meet weekly or monthly. Let's use a security bottleneck as an example. Security teams are sized for human output, so when agents multiply code output, either the review queue builds or code ships under-reviewed. A regulated organization can't accept either outcome, so its security and policy checks have to keep pace with the agents. To better realize the productivity gains of and secure agentic AI, the traditional SDLC lifecycle requires the same level of transformation as the implementation phase has undergone. The AI-native SDLC is a reimagined process that combines the old control objectives with new enforcement. Instead of a linear flow, the process becomes a loop, and AI is embedded at each point. The AI-native SDLC promotes automated handover and triggering of subsequent plays, helping to address the manual and clunky nature of handoff between the phases of the traditional SDLC. You'll also hear this shift called the agentic SDLC, the AI SDLC, or simply agentic software development — the labels differ, but they describe the same thing. The shifts across the six stages of an AI-native SDLC The table below highlights the ends of the spectrum between traditional SDLC and AI-native SDLC, supported by Claude. Most organizations sit somewhere between the two columns. Stage Traditional SDLC AI-native SDLC Plan Requirements gathered by committee, distilled through workshops and sign-offs, written up by hand Claude synthesizes pain points straight from the sources and captures them within intent.md which is human readable and machine actionable Design Spec written by analysts, parsed by designers Requirements and design compressed into one working session with an agent, guided by standards encoded as skills, versioned in git Build Tests and code are handwritten and documentation is written after the main development happens Tests and code are generated by AI and institutional knowledge is maintained as versioned machine-readable CLAUDE.md files and skills Test QA gates at stage boundaries Continuous evals woven through implementation Deploy Humans review every line of code and governance occurs in review cycles, often inconsistently Layers of agentic review with human review reserved for regulated and critical code. Governance is enforced as the AI acts, with hooks as approval gates Maintain Humans watch production for bugs Agents monitor live deployments. Any breached control band is diagnosed and written back into the loop as a new intent.md The thread running through the right-hand column is the committed artifact. Each stage ends by writing one to version control including intent.md, spec.md, plan.md, the diff and its tests, the PR with its review findings, and the incident record and the next stage begins by reading it. For the early stages, .md files are the predominant artifact because a product owner and an agent can both read and act on the same file. From Build onward, the artifact is code and its records. The chain of commits is also the audit trail: who asked for what, what the agent produced, and who approved it. Humans remain accountable for every decision that requires judgment. In the agentic SDLC world, the human attention shifts along with the artifacts that must be reviewed. Every stage commits an artifact the next stage can read. Together, the intent, the spec, the plan, the diff and the review findings are the audit trail. Plays The plays are the core of the playbook and are grouped into six non-linear stages Plan, Design, Build, Test, Deploy, Maintain , which together cover the complete lifecycle. Each play covers: What changes; Getting started; Concrete steps for implementation; Governance considerations; and How you measure whether it worked. The steps are modular and organizations may choose to prioritize transforming different stages at different times based on their unique needs. Each play names its dependencies under "Prerequisites," which the dependency graph further illustrates. A stage ends by committing an artifact with the commit initiating the next stage. An accepted intent.md triggers the requirements and design pass, an approved spec.md triggers plan mode, a merged PR triggers the pipeline, and a breached control band in production writes the next intent.md and so the loop continues. First, you prompt each step by hand with the end state being a loop in which each accepted artifact fires the next gate. Human attention concentrates at the gates, reviewing what the agent flagged rather than starting each stage from scratch. 01 Plan Ideas stop waiting for someone to write them up. Intent is captured once, in the originator's own words, as a version-controlled artifact the next stage can act on. Capture as intent.md The intent.md, which kicks off the software development process can enter through different routes. A person has an idea, a ticket is filed, or an incident is surfaced via an alert see Stage 6: Maintenance . When a person has an idea, they brainstorm with Claude and produce a markdown proto-spec. In the traditional SDLC, the same person must then convince a member of the product team to write the idea up with them or on their behalf. The proto-spec generated by Claude is human readable, version-controlled, and immediately consumable by the next stage. The proto-spec is saved as an intent.md. Regardless of whether the intent originates from an event trigger or an agent, the same steps apply: the product owner reviews and corrects the agent-written intent.md before it is committed. TraditionalAn idea passes through backlog entries, user stories, story points, and refinement meetings before anyone can act on it. Ownership transfers at each handoff, so what reaches engineering is several steps removed from what the originator meant. AI-nativeThe originator brainstorms with Claude and writes the result down as intent.md, a proto-spec in the originator's own terms. The artifact contains what is wanted, why, and under which constraints. Repeat processes are encoded via skills. Getting started Prerequisites None. Infrastructure Claude access for people who are not engineers claude.ai or Cowork ; an agreed intent.md template; a shared, version-controlled home for intent that the product owner watches. For a single product the simplest home is an intent/ folder in the product repo. This setup keeps the artifact chain next to the code derived from it. A dedicated intent repo is only worth the overhead when intent spans many repositories, and in a monorepo it is a directory. The Stage 3: Build sidebar covers how this home relates to a Jira or requirements tool that already holds the record. Setting this up is a one-time task for the platform or engineering team. A technical team member needs to stand up the intent home and decide who can write to it, since many contributors will come from across the organization. Once the repository exists, contributors without git experience don't need to use git directly. Instead a connector to the version-control system e.g. GitHub lets Claude commit markdown files on their behalf from claude.ai or Cowork. How to execute it The originator describes the problem to Claude in their own words. The originator may describe what they cannot do today, who is affected by the idea, what better looks like, or what is out of scope. No formal language is required. Brainstorm until the idea is concrete. Claude asks the questions an analyst would ask: scope, users, constraints, and what success looks like. Ask Claude to write the result as intent.md using the organization's template, which can be encoded as a skill set up by a technical team member and signed off by a lead. This can cover the problem, proposed outcome, affected users and systems, constraints, and open questions. The originator corrects anything Claude misunderstood. Commit intent.md to the shared home. Author and timestamp join the record, and the product owner picks the idea up from there. Intent: claims status self-service Author: J. Ortiz claims operations . Status: draft. Problem Customers phone the contact center to ask where their claim is. Handlers spend roughly a third of call time on status-only queries. Proposed outcome Customers see claim status, next step and expected date in the portal. Affected users and systems Claims handlers, portal team, claims-core API. Constraints No new PII in the portal session. Existing authentication only. Open questions Do third-party loss adjusters need access too? Governance considerations The evidence is the committed intent.md, which lists the author, the timestamp and the full revision history. It's logged in the git history of the intent home. The product owner approves, and the accept or reject decision that sends the intent into Stage 2: Design is recorded as the merge or the closing review. How to measure it Leading indicator Time from first conversation to a committed intent.md, read from git history on the intent home, which records author and time stamp. The expectation is to fall from a multi-week elicitation and refinement cycle to hours. Lagging indicator The survival rate, or the share of intent.md files that the product owner accepts into Stage 2: Design rather than closes. The accept or reject decision is recorded as the merge of the artifact or the closed review. Additionally, the number of changes made to the intent.md that are made after the first spec.md commit for the same change. 02 Design Requirements and design collapse into one session. Policy is applied while the spec is written, not discovered in a review weeks later. Requirements and design Once approved by the product owner, Claude takes the accepted intent.md and produces a requirements and design spec. This is guided by the organization's skills for brand, security, compliance, and UX. The product owner reviews that spec, but doesn't write it. The goal of this process is to create a spec the engineering team can plan against, with flagged areas of concern. Front-end work is the clearest example. Once the intent.md is accepted, the product owner mocks the design up in Claude Design beta from the intent.md, iterates on the mock, and then exports it to Claude Code to build. TraditionalRequirements and design are separate phases run by separate teams. Analysts formalize the idea into requirements and designers then parse those back into a design. The separation exists for accountability, but it is slow and lossy. AI-nativeBoth phases happen in a single prompted session. Claude takes intent.md and produces a requirements and design spec, constrained by the organization's skills, with areas of concern flagged. Getting started Prerequisites Write an intent.md file, with brand, security, compliance, and UX policies written as skills. Infrastructure A product owner with Claude access. No engineering skill is required. How to execute it The product owner opens a session with the organization's skills available and attaches the intent.md. The product owners prompt points at the intent.md, names the constraints, and demands flagged concerns. Run it by hand at first, then codify it as an organization-level slash command. From there make the acceptance of intent.md in the intent home the trigger, with a non-interactive job that fires on the merge, run the pass with the organization's skills loaded, and commit spec.md as a pull request the CI/CD play in Stage 5: Deploy covers the plumbing . From that point the product owner's first involvement is the review. The same product owner reviews the spec against the idea. Does the spec solve the stated problem, and are the open questions from intent.md answered or carried forward? Work through the flagged concerns first as they are the points an analyst would have escalated. The product owner resolves each one with its policy owner before engineering sees the spec. Commit spec.md alongside intent.md. The file pair records what was asked for and what was decided. The product owner decides whether the spec and intent progress to build, consulting a technical lead for anything the organization classes as higher risk. A human team mate always makes this call, and accepting the spec is what starts the plan mode play in Stage 3: Build. What it looks like the prompt Read the attached intent.md and produce a requirements and design spec for integrating it into our existing codebase. Apply the skills available to you so the plan conforms to our brand guidelines, security policies and UX standards. Document the spec fully as spec.md, ready to hand to the engineering team. Describe clearly any areas of concern, especially where you cannot satisfy contradicting policies. Governance considerations Instead of being discovered in a review weeks later, the live policy is read and applied while the spec is written. The organization's skills are applied as constraints on the spec. The spec, the prompt that produced it, and the skill versions in force are all logged in version control. The product owner signs off the spec, and routes flagged concerns to the named policy owners. How to measure it Leading indicator Elapsed time between the intent.md commit and the spec.md commit for the same change two git timestamps , compared with the old requirements-plus-design cycle. Lagging indicator Requirements rework after build starts. Count spec.md commits dated after the first plan.md commit for the same change. Git log will give this directly. 03 Build Nothing is implemented without an accepted plan. Institutional knowledge becomes files the agent reads, and the guardrails run as code rather than as habits. Claude Code plan mode as the default starting point Engineers start Claude Code sessions in plan mode, give Claude the approved spec.md from Stage 2: Design, and let it interview them, iterating on the plan until the engineer is happy with it. TraditionalAn engineer reads the design and starts writing code. How the change will be made, down to which files and which tests, stays in the engineer's head or at best a ticket comment. Nobody else can review it. The first thing a reviewer sees is the finished diff, and by then rework is slow. AI-nativeWork starts with a written plan that Claude produces in plan mode, where it can read the codebase without changing anything. The engineer corrects the plan before code is written, and the approved version is committed as plan.md for later stages to check against. Getting started Prerequisites The intent artifact intent.md or spec.md if one exists, and the CLAUDE.md file helps. Infrastructure Claude Code with access to the repository. How to execute it The engineer starts the session in plan mode with Claude. The engineer gives Claude the intent.md and the spec.md and asks for an implementation plan that names the files that change, the order of the work, and the tests that prove it. Interrogate the plan by asking what the change could break, which step is most risky, and what other options Claude chose not to do. Iterate until an engineer who has never seen the conversation could implement the change from the plan alone. Commit the approved plan as plan.md. The plan joins the audit trail, and the PR review play Stage 5: Deploy checks the eventual diff against it. Accept the plan and let Claude implement. With a solid plan, the implementation is often a single pass. When implementation departs from the plan, update plan.md in the same commit. Consider using a hook to enforce synchronization between the two. What it looks like plan.md Plan: claims status self-service from intent.md 2026-06-02 Files that change portal/src/claims/StatusPanel.tsx new , claims-api/routes/status.py, claims-api/tests/test status.py Order of work 1. Add the status endpoint behind existing auth. 2. Panel against the endpoint. 3. Wire into the portal nav. Risks The claims-core API rate-limits at 50 rps; the panel must cache. Proof test status.py covers the four claim states; screenshot matches the approved mock. Governance considerations Design review happens before any code is generated, when changing course is still a matter of editing a document. Plan mode enforces this itself, since Claude cannot edit files until the engineer accepts the plan. The plan and its revisions are logged along with who accepted it. Routine changes are approved by the engineer, and anything the organization classes as higher risk goes to a tech lead or architect. How to measure it Leading indicator Share of changes that merge from the first implementation pass, and time from plan approval to merged PR with the required data within the PR metadata. Lagging indicator Rework cycles per change, again from the PR metadata, and how often the merged diff still matches the committed plan.md. Claude Code on auto mode Claude Code can also run in auto mode, where the engineer approves the plan and, once happy and iterated upon, Claude applies each change without a per-edit prompt. As the guardrails from the later plays mature a tuned CLAUDE.md, skills that encode policy, hooks that block unsafe actions, and a test suite Claude can run , auto-accept becomes the default for routine work: a tight spec.md, a small blast radius, and code the tests already cover. The shift is now away from the user watching the agent make the edits and reviewing actions, towards the review of artifacts after longer autonomous sessions. Auto-accept mode further enables parallelism across individuals and the team when used with worktrees and is fundamental to running the SDLC autonomously and closing the loop as described in Stage 6: Maintenance. Sidebar Legacy systems and the source of truth Applies to every artifact the process produces. Existing SDLC processes likely already track artifacts, just not in markdown files. Work items may be in Jira, requirements in a tool with regulatory traceability built in, designs in Figma, and change approvals with a change board. Those systems are hard to displace because auditors and regulators already accept them and other teams depend on them, so the AI-native SDLC has to fit around what exists. When transitioning to the AI-native SDLC, for every artifact the process produces, name one system as the source of truth, with everything else holding a copy or a link to the original. The configurations below can be set up to have one source of truth, with the choice differing per artifact: The repo as the source of truth. The markdown artifacts are the authoritative record and the legacy system references files within commits. This can be one of the cleanest configurations for engineering-led organizations, as all records live in one tool with one timestamp authority. The legacy system as the source of truth. Jira, ServiceNow, or the requirements tool holds the authoritative record and the markdown artifacts are working copies. Claude reads the record at the start of the session and writes the outcome back through an MCP connector in the same session that produced the spec or the plan. Linkage as the minimum bar. All artifacts note the record ID and all legacy records contain the commit SHA of the markdown file. Linkage is a good place to start when transitioning to the AI-native SDLC, accepting that there are two sources of truth. Both the legacy system and the markdown-first system can coexist, so long as there is a link between the two or one is declared the source of truth. The CLAUDE.md CLAUDE.md gives Claude the context a new joiner would need, covering conventions, commands, architecture, and the mistakes the team sees most often. Knowledge that used to sit in people's heads and on wikis becomes a file the agent reads at the start of every session, maintained by the whole team and iterated on whenever a mistake is made. Getting started Prerequisites None. Infrastructure A repo, Claude Code installed, and one engineer who knows the codebase well. How to execute it Run /init in the repo. Claude generates a starting CLAUDE.md from what it finds. Cut the generated file down to what a new joiner would need on day one. Keep the build, test and lint commands, the conventions that matter, and the things Claude keeps getting wrong. Check CLAUDE.md into git at the repo root so the whole team shares one version and changes are reviewed like code. A working rule helps here. When Claude makes a mistake twice, the correction goes into CLAUDE.md. Keep it under a page, because Claude reads all of it at the start of a session and anything stale is taking up context for no benefit. What it looks like CLAUDE.md Payments service Commands - Build: make build - Test: make test unit , make itest integration, needs docker - Lint: make lint runs in CI; fix before pushing Conventions - Java 21, Spring Boot 3. No new Lombok. - Money is always BigDecimal, never double. - Every endpoint needs an integration test in src/itest. Architecture - api/ holds REST controllers, core/ holds domain logic, adapters/ talks to external systems. - Kafka events are defined in schemas/; never edit generated classes. Things Claude gets wrong - Do not bump dependency versions; the platform team owns them. - The legacy v1/ package is frozen; changes go in v2/. Governance considerations CLAUDE.md is version controlled, so the instructions the agent works to are reviewable and auditable. Team conventions are applied through the file, changes to it are logged in git history, and code owners approve those changes in PR review. How to measure it Leading indicator How often Claude repeats a mistake CLAUDE.md should have caught. The corrections or changes to the CLAUDE.md should be tracked within the git history. Lagging indicator Time to first merged PR for a new member of the team from PR history. Skills as institutional knowledge Skills are how an organization makes its institutional knowledge operational. The instructions are explicit, version-controlled, applied broadly, and updated centrally when policy changes. The rule of thumb: write a skill for institutional knowledge that must be applied consistently; don't write a skill for components that belong in CLAUDE.md or a prompt. Getting started Prerequisites None required. Having a CLAUDE.md helps, because it keeps the agent's working knowledge in the repo, but a skill does not depend on it. Infrastructure One policy with a named owner and a written source of truth. How to execute it Pick one piece of knowledge that is enforced inconsistently today. This could be a security standard, an API design convention, or a brand rule. Write it as a skill, a folder containing a SKILL.md whose frontmatter says when it triggers and whose body says what to do. An engineer writes it from the policy owner's source of truth, using Claude to help. Put the skill in the repo at .claude/skills/<name / so it ships with the code, or distribute it organization-wide through a plugin. Test that the skill triggers. Ask Claude to do the relevant task in different ways and confirm the skill loads each time. When the policy changes, change the skill and have the policy owner sign off the change. Engineers pick up the new version automatically in their next session. What it looks like .claude/skills/secure-api-review/SKILL.md --- name: secure-api-review description: Apply the API security standard. Use whenever creating or modifying an external-facing endpoint, reviewing API code, or generating an OpenAPI spec. --- Secure API review When you create or change an API endpoint: 1. Authentication: every endpoint requires the gateway JWT; no anonymous routes outside /health. 2. Input validation: validate request bodies against the OpenAPI schema and reject unknown fields. 3. Audit: every state-changing endpoint emits an audit event with actor, action, entity and timestamp. 4. Data classification: fields tagged pii in the schema must never appear in logs or error messages. Run scripts/check-endpoints.sh and include its output in your summary. Governance considerations A skill is a control, though an advisory one. It makes Claude likely to apply the policy while the code is written, and nothing forces a session to comply with it. A policy that must always hold needs something deterministic behind the skill, such as a hook that blocks the action or a review pass that re-checks the policy at the PR. The skill makes violations rare and the hook makes them close to impossible. Skill invocations are logged in session traces, and the policy owner reviews skill changes like code. How to measure it Leading indicator Time from the policy owner approving a policy change to the updated skill merging, taken from the PR on the skill folder. Lagging indicator PR reviews findings that cite the policy, which should fall towards zero once the skill is applying the policy while the code is written. Where the findings don't fall towards zero, either the skill isn't triggering or its text has drifted from the official policy. Hooks as build-time guardrails A skill is an advisory control while a hook is the deterministic layer behind it. Most of Claude's actions are file edits and shell commands during implementation, so the build phase is where hooks can end up firing most often. Build-phase hooks can: Block edits to protected paths such as generated classes or a frozen package; Run the formatter and linter after file edits so drift never accumulates; Keep credentials out of the diff. Back any skill whose policy has to hold without exception. A hook runs on each action that matches it, so build-phase hooks should be fast and scoped to the file that changed. Heavier checks such as the full test suite belong at the commit or the PR. A hook that asks a human for approval belongs with the gates in Stage 5: Deploy, because an approval prompt during the build puts a person back on the critical path of all the sessions running in parallel. Parallel sessions and subagents One engineer can drive several streams of work at once. A parallel session is another full Claude Code instance, working a separate task in its own git worktree. Each independent session knows nothing about the others, and the engineer steering them is the only thing they share. A subagent runs inside a single session as a scoped helper with its own context window and tool limits and suits jobs that recur in multiple tasks such as verifying the app runs as expected. Parallel sessions raise the number of tasks an engineer can have in flight, while subagents keep each session focused on its own task. The engineer's job is steering and reviewing all of them. TraditionalOne engineer works one task at a time and spends a significant portion of their day or week on builds, tests and reviewers. Switching between tasks while waiting is possible, but the context switch is tiring enough that few people choose to. AI-nativeOne engineer runs several Claude sessions at once, each in its own worktree on its own task. Repeated jobs become subagents with their own context and tool limits. The engineer's job shifts to orchestrating, and eventually, to building and monitoring loops. Getting started Prerequisites The CLAUDE.md, since all sessions read the file. The feedback loop Stage 4: Test also helps here, because less supervision from the engineer is needed when a session can verify its own work. Infrastructure A git repository, since isolation comes from worktrees and permission settings tuned so sessions are not waiting on approval prompts for commands the organization considers safe. How to execute it The engineer splits the work into tasks that touch different files, using the plan from the plan mode play Stage 3: Build to see where the work is independent. Tasks that share files run in a single session, one after another. Each parallel task gets its own worktree, for example claude --worktree feature-auth in one terminal and claude --worktree fix-rate-limit in another. A worktree is a separate checkout on its own branch, which stops sessions colliding on files. Two or three sessions is a sensible starting point. The practical ceiling is how many streams one person can review properly, so add sessions only while review is keeping up. Turn repeated jobs into subagents, as defined in markdown files in .claude/agents/, each with a name, a description of when to use it, and the tools it may touch. Examples include a code simplifier that strips needless complexity after the main agent finishes, a verifier that runs the app and checks behavior, a researcher that explores the codebase and reports back without flooding the main context. Check the definitions into git so the whole team shares them. What it looks like .claude/agents/verifier.md --- name: verifier description: Runs the app and checks the change works before the session reports done tools: Bash, Read --- Start the app with make run. Exercise the changed behavior and the two nearest neighboring flows. Report what you ran, what you saw, and any behavior that does not match plan.md. Do not fix anything; report only. Governance considerations More sessions means more output, so the controls have to come from configuration in the repo. Hooks and permission settings there apply to all sessions, and what a session does is logged and attributed to the engineer who ran it. How to measure it Leading indicator Concurrent sessions per engineer while review quality holds, counted from the OpenTelemetry export, and the share of the day spent steering rather than waiting. Lagging indicator Changes merged per engineer per week read alongside the rework rate as determined per the PR history. 04 Test Every session checks its own work before a human sees it, and the configuration that steers the agent gets regression-tested like the code it writes. Give Claude a feedback loop Always give Claude a way to verify its own work, whether tests, a build, or a screenshot diff. A session checks its own work and fixes its own mistakes before an engineer sees them. The feedback loop should not be confused with a verifier subagent Stage 3: Build . The feedback loop runs through the whole task as many times as the work. The verifier subagent, on the other hand, is one way to package the final check by running a fresh context window once the session believes the work is done. This way the verdict is not colored by the assumptions that produced the code. TraditionalThe signal that code works arrives late. CI minutes later, a tester days later, production weeks later. With an agent producing the code, a late signal means a person has to check all of its output, and that person becomes the bottleneck. AI-nativeThe session is given a way to check its own work before a person sees it. Run the tests, run the build, take the screenshot. Claude iterates until the check passes, so what reaches the engineer has already passed it. Setting the loop up falls to the engineer running the session, and the steps below are written for them. Getting started Prerequisites None. Infrastructure A test suite and a build that run locally with one command each. For the UI work, a way for Claude to see the result is crucial, either a browser tool or a screenshot utility wired in via MCP. How to execute it If checking the work today takes a sequence of commands and some environment knowledge, wrap it in a single target such as "make test" or "npm test" that exits non-zero on failure. In the CLAUDE.md's Commands section, list each command with an example of a healthy output. State a target and make it quantifiable so Claude can check the work without asking you, for example: "All tests in test status.py pass," "the screenshot matches the attached mock," or "the endpoint returns 200 with the new field". For bug fixes, write the failing test first. Ask Claude to reproduce the bug as a test, run it, and confirm it fails for the reason you expect. Commit that test. Only then ask Claude to make it pass without editing the test, with the test-file hook from the final step enforcing the restriction. A test that existed before the fix, and that the agent couldn't rewrite, is proof the bug is gone. For UI work, close the loop with a visual check. Give Claude a browser or screenshot tool, give it the mock, and let it iterate. Implement, screenshot, compare, and adjust. Two or three rounds is normal, and the result should improve with each one. Make verification part of "done." Instruction lives in CLAUDE.md. Run the tests before reporting a task complete, and show the output. Finally, the loop itself needs protecting, because an agent fixing code must not be able to weaken the check on that code. A hook that blocks edits to test files during a fix task does this. The alternative is to check the diff in review and reject any change that touches a test. What it looks like CLAUDE.md verification block Verifying your work - Build: make build must finish with "Build succeeded" - Test: make test all green; never skip or delete a failing test - Lint: make lint zero warnings Run all three before reporting any task complete, and paste the output. If a test fails, fix the code, not the test. Governance considerations What is enforced Verification before a task is reported done, and the block on the agent editing test files during a fix, both implemented as hooks where the organization wants them guaranteed. What the evidence is The literal output of "make test," the build log, or the screenshot diff that Claude ran and pasted, so the evidence comes from the toolchain. Where it is logged In the session transcript, which the OpenTelemetry export forwards to the organization's observability stack, and in the PR's check run, where the reviewer and any later auditor can both see it. Who approves The code owner reviewing the PR, who can concentrate on intent and risk because the mechanical evidence is already attached. How to measure it Leading indicator First-pass CI success rate for agent-written changes, which the CI system already supports. Lagging indicator Review time per PR from the PR metadata , which should fall once the tests catch what reviewers used to catch, and the change failure rate from an incident tracker. Continuous evals in CI Evals are the AI-native equivalent of stage-gate QA. In practice that means a suite that runs whenever the agent's configuration changes. When a new model is swapped in or a prompt is rewritten, the eval suite says whether the agent still does the work to the same standard. The evals should be seen as a live suite. As models improve, cases that once discriminated stop doing so and new ones must be added that arise from ongoing monitoring. Depending on the use case, some teams may prefer to run these evals offline on a set cadence rather than on every change. The steps below are for continuous evaluations. Getting started Prerequisites The CLAUDE.md and feedback loop Stage 4: Test . Infrastructure CI that can run Claude Code non-interactively, and an API key with budget for eval runs. How to execute it The platform engineer collects 20 to 50 real tasks from recent work with its expected/accepted outcome. Write each task as an eval, meaning the prompt plus the checks that define acceptable tests pass, lint clean, behavior unchanged, policy followed . The suite runs non-interactively in CI on a schedule and on any change to CLAUDE.md, skills or hooks, since that configuration steers the agent and deserves the regression testing that code gets. Gate configuration changes on the results. A skill change that drops the pass rate gets reviewed before it merges. Each production incident gets an eval, written by the team that owned the incident, and stays in the suite as a regression test. What it looks like .github/workflows/agent-evals.yml name: Agent evals on: pull request: paths: 'CLAUDE.md', '.claude/ ' schedule: - cron: '0 2 ' jobs: evals: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - run: npm install -g @anthropic-ai/claude-code - name: Run eval suite env: ANTHROPIC API KEY: ${{ secrets.ANTHROPIC API KEY }} run: | for eval in evals/ .json; do claude -p "$ jq -r '.prompt' $eval " \ --allowedTools "Read,Edit,Bash make test " \ --output-format json result.json ./evals/check.sh "$eval" result.json done Governance considerations Evals give QA a gate that keeps up with agent output. The pass-rate threshold is enforced as a merge check, runs are logged so results can be compared over time, and the team that owns the configuration change approves it. How to measure it Leading indicator The eval pass rate over time, reported by the suite on every run, and how long a production incident takes to become a permanent eval. Lagging indicator Regressions caught in CI compared with regressions found in production derived from the incident tracker. 05 Deploy Review runs in both directions, and governance is enforced as the agent acts. The agent does everything up to the production gate and nothing past it. AI in the PR review loop Claude both gives and receives reviews. It reviews incoming PRs against the organization's policies and addresses review comments on its own PRs. This allows engineers to focus on behavior in their PR review, which boils down to judging intent and risk. TraditionalReview capacity was planned around human output. A PR waits for a reviewer to read all of it, review quality varies with the reviewer's load, and the author chases while the backlog grows. AI-nativeAll PRs get an identical set of review passes, with findings ranked by severity. Human attention moves up a level, to whether the change does what the plan intended and whether the risk is acceptable. Getting started Prerequisites An updated CLAUDE.md file from Stage 3: Build; skills if the review passes enforce written policies, defined subagents. Infrastructure A repo with the Claude integration installed, either the managed Code Review research preview service enabled by an admin or the claude-code-action running in your own CI, with model calls through AWS Bedrock, Google Vertex or Microsoft Foundry where needed the CI/CD play covers the deployment options . Branch protection policies that require a code owner's approval are also worthwhile. How to execute it The managed Code Review service is the fastest start. An admin enables it and selects repositories. Run the review in your own CI with the claude-code-action when you need control of the pipeline or want API calls routed through your own cloud agreement the CI/CD play covers that plumbing . The tech lead writes the review policy as REVIEW.md at the repo root, divided into the passes the organization cares about: bugs and logical errors; security and vulnerabilities; compliance against the spec spec.md from the requirements play , the implementation plan plan.md from the plan mode play and design principles. REVIEW.md also defines what counts as Important as opposed to a Nit, and what to skip. The tech lead sets the human threshold. Findings do not approve or block a PR on their own, and branch protection still requires approval from a code owner. A platform engineer who wants to gate merges on findings can read the severity counts that the check run publishes as a machine-readable tally. When a reviewer or the author tags @claude on a review comment, Claude addresses the comment and pushes the fix. The PR thread records both the request and the change. This fix loop runs through the claude-code-action. In the managed service, commenting @claude review requests a fresh review instead. For PRs Claude opened, go further and let Claude babysit the PR to merge. Teams wrap the loop in a custom slash command that sweeps the unresolved review comments and failing checks on the PR, addresses them and pushes the fixes, until the PR is green and waiting only on code owner approval. Review findings feed back into CLAUDE.md. When a review flags a mistake for the second time, the correction goes into CLAUDE.md as part of that review, and because review reads CLAUDE.md the mistake is caught from the next PR onwards. Review also flags when a change has made CLAUDE.md outdated. Once a month the tech lead tunes the setup by rating findings so the reviewer improves and by capping Nit volume in REVIEW.md. Generated paths and anything CI already enforces are excluded. What it looks like REVIEW.md Review instructions Passes Run three passes and tag each finding with its pass: - Bugs: logic errors, broken edge cases, subtle regressions - Security: injection risks, authentication gaps, PII in logs - Compliance: the change matches spec.md, plan.md and our design principles What Important means here Reserve Important for findings that would break behavior, leak data or breach a policy. Style and naming are nits. Cap the nits Report at most five nits per review; summarize the rest as a count. Do not report Generated files under src/gen/ and anything CI already enforces. Governance considerations Separation of duties is preserved, because the agent that wrote the code has no way to approve it. The review policy in REVIEW.md is applied to all PRs, and findings, fixes, ratings and approvals are logged in the PR history, so the PR is the audit record. Approval comes from a human through branch protection, informed by the findings. Time to first review, which should fall to minutes, and the share of review comments resolved without a human touching the branch with data stored directly on Git. Lagging indicator Defects and vulnerabilities caught before merge set against those escaping to production, from the PR history and the incident tracker. Hooks as approval gates The build phase used hooks as guardrails, allowing or blocking actions with no human involved Stage 3: Build . A hook can also ask, pausing the action until a specific person approves, which is what release gating needs. The play sits in Stage 5: Deploy because the release gate is the clearest case, but hooks are not deploy-specific: they run wherever Claude acts. For example, hooks can block edits to migrations and infra without a change ticket during Stage 3: Build, and stop the agent editing test files during a fix task in Stage 4: Test. Getting started Prerequisites None. Infrastructure A written list of the approvals the change process requires. How to execute it Engineering leadership, with change management and compliance, lists the human approval gates that must survive, such as change management sign-off, release authorization, and edits to protected paths. The platform engineer expresses each gate as a hook, a script that runs before Claude acts that can allow, ask, or block. Team hooks go in .claude/settings.json in git, and non-negotiable hooks go in managed settings owned by the platform or IT admin, where individual engineers cannot switch them off. A block should explain itself, so when a hook stops an action the reason and the route to approval appear in Claude's output. What it looks like .claude/settings.json { "hooks": { "PreToolUse": { "matcher": "Bash", "hooks": { "type": "command", "command": "${CLAUDE PROJECT DIR}/.claude/hooks/production-gate.sh" } } } } And the gate itself .claude/hooks/production-gate.sh bash /bin/bash Production deploys require a named release authorization cmd=$ jq -r '.tool input.command' < /dev/stdin if "$cmd" == "deploy" && "$cmd" == "production" ; then if -z "$RELEASE APPROVAL" ; then echo "Production deploys need a release authorization." &2 exit 2 exit 2 blocks the action; the message goes to Claude fi fi exit 0 Governance considerations Hooks are the approval gates. The gate condition is enforced every time, for everyone. Allow and block decisions are logged with a timestamp. The gate also defines what counts as approval, whether that's an approved change ticket or the release manager's sign-off. Worked example Managed settings for a regulated enterprise Deployed by the platform team via MDM or the admin console; engineers cannot edit or override any of it. permissions.deny keeps secrets out of the agent's context and blocks arbitrary network egress through tools; permissions.allow pre-approves the safe inner loop so the deny list doesn't turn into prompt fatigue. disableBypassPermissionsMode plus allowManagedPermissionRulesOnly means no engineer, project file or command-line flag can widen the rules. sandbox closes the gap permissions cannot. A tool-level deny on WebFetch doesn't stop a shell command reaching the network; the OS-level domain allowlist blocks egress outright. failIfUnavailable and allowUnsandboxedCommands make the sandbox a gate: Claude Code refuses to start when the sandbox cannot initialize, and a command that fails inside the sandbox cannot be retried outside it. credentials closes the gap the deny rules leave open. permissions.deny governs Claude's file tools, but a sandboxed shell command could still read ~/.ssh or ~/.aws/credentials by default; this block denies those reads and strips the named secrets from the environment of every sandboxed command. allowManagedHooksOnly means the approval gates from this play are the only hooks that run; nothing local can add to or replace them. disableSideloadFlags and strictKnownMarketplaces mean every skill, agent, hook and MCP server on an engineer's machine arrived through the organization's approved plugin marketplace, never from a home directory. allowManagedMcpServersOnly makes the agent's tool surface an allowlist owned by the platform team. requiredMinimumVersion refuses to start on a version below the approved floor, so the controls are enforced by a build the organization has actually assessed. Consider the above a starting point to tailor, rather than a recommendation to copy. Every deny trades against capability, and the right balance depends on the data classification of the repo. The settings reference documents every key, including the managed-only ones: code.claude.com/docs/en/settings How to measure it for the hooks themselves Leading indicator Time spent waiting on each approval gate. Every hook decision is written to the OpenTelemetry export with a timestamp and an allow or block verdict, so the wait is visible per gate. Lagging indicator Gate violations reaching production before and after hooks from the incident tracker. CI/CD integration and deployment Run Claude Code non-interactively inside the CI/CD pipeline, sandbox the execution so long-running agents run safely, expose deployment through MCP integrations, and rehearse the rollback paths before the agent ever needs them. TraditionalPipelines run deterministic scripts, and anything that needs judgment waits for a human. For example, triaging the flaky test, writing the changelog, or working out why the build broke. Deployment and rollback are runbooks a human follows under pressure. AI-nativeClaude runs non-interactively inside the pipeline for the judgment steps, in a sandbox with scoped credentials. Deployment tooling is exposed to the agent through MCP, so the workflow that wrote and tested the change can also ship it and roll it back, inside gates the organization defines per environment. Getting started Prerequisites Claude in the PR review loop and hooks as approval gates, because the gates must exist before automation accelerates anything through them. Infrastructure A CI platform with the claude-code-action installed, or any runner that can call claude -p; model access through the API, or Bedrock, Foundry, or Vertex where traffic must stay on the organization's cloud agreement; MCP servers for the deployment targets; a sandbox profile for agent jobs with no standing production credentials. How to execute it The platform engineer starts with read-only judgment steps. Use claude -p in a pipeline job to triage a failed build, summarize a flaky test, or draft the changelog. Add write steps behind the existing gates for jobs like fixing lint, updating generated docs, or addressing review comments via the @claude mentions. Anything the agent writes arrives as a PR through branch protection, and the agent has no route to push to main. Execution is sandboxed. Agent jobs run in containers under a network policy with short-lived scoped tokens, and hold no production credentials by default. Expose deployment through MCP. Deploy, status, and rollback become tools, scoped per environment, so the agent's deployment powers are an allowlist rather than a shell script with credentials. Tier the autonomy by environment. In development, the agent deploys freely. In production, the agent prepares the release and the release manager authorizes it, and a hook enforces the production gate. Staging sits somewhere in the middle. Rollback should be the most rehearsed path in the pipeline, a single command that the agent can run and that is exercised regularly in staging. The closing the loop play Stage 6: Maintenance calls this rollback when a control band is breached, so it has to be proven in advance. What it looks like pipeline step - name: Triage failed build if: failure run: claude -p "Read the build log at out/build.log. Identify the most likely cause, say whether the failure looks flaky or real, and write a three-line summary for the PR thread." triage.md Governance considerations The governing principle is that the agent may act up to the production gate and cannot pass it. The controls below enforce this principle. Branch protection turns anything the agent writes into a PR, with no direct path to main. The production deploy hook blocks the release until a named release manager authorizes it. Each non-interactive run acts under the agent's own identity, so the pipeline log separates what the agent did from what the engineer who triggered it did. Per-environment permission tiers set how much the agent may do on the way to the gate. How to measure it Leading indicator The share of pipeline failures triaged without paging a human taken from the CI/CD pipeline logs. Lagging indicator DevOps Research and Assessment DORA measures, which the CI system and deployment tooling already emit. 06 Maintain The loop closes. A trigger invokes Claude with no person in the invocation path, and what it finds re-enters the pipeline as intent.md. Maintenance and closing the loop So far, we've discussed how to add Claude to each stage of the SDLC process, with each stage requiring a human to launch the initial steps. This stage, however, shifts the focus to autonomous running of Claude to close the loop. For example, a continuously running monitoring agent could, off the back of a bug ticket being raised, create an intent.md, and flow through the requirements, plan, build test and review phases. Stage 6: Maintenance runs headless, with an independent confidence gate between stages, a deterministic check or an adversarial reviewing agent, deciding whether the previous stage's output continues or is escalated to a human. TraditionalMaintenance is a reactive phase. All tickets or incidents wait on a person to act on it and restart the process. An alert fires at 3 a.m. and can be missed, a ticket can sit in the backlog until someone picks it up, and post-mortem actions may not reach the codebase at all if another fire starts first. AI-nativeA trigger such as a control-band breach, a ticket, a channel message or a schedule invokes Claude without a person in the path. Claude diagnoses, acts only through gated routes, and writes what it finds as intent.md, which then goes through the stages described above. People triage and review that work, and no longer have to start it. Closing the loop A deterministic script watches production and invokes Claude when a control band is breached. Monitoring of a breach is a helpful example of the pattern for the loop running autonomously, while the Claude Tag public beta section at the end of the stage covers work arriving through different channels. Getting started Prerequisites Intent.md which gives the loop a structured output to restart. Claude accelerated PR reviews, hooks as an action boundary, and a rollback path for CI/CD which the highest autonomy tier invokes . Infrastructure A metrics store the detection script can query Prometheus, the CI system's API, or equivalents , read access to the repository, a way to run Claude Code non-interactively in CI, or the Agent SDK for a service that receives webhooks. How to execute it The service owner or platform engineer picks one metric with a stable rolling baseline, such as CI test failure rate, post-deploy 5xx rate, or PR cycle time. They write the detection script, typically mean and standard deviation over a rolling window with rules Western Electric or similar so the bands catch slow drift as well as spikes. The script is version controlled and unit tested, and detection stays entirely deterministic, with no model involved. Response tiers are defined in version-controlled config bands.yaml below . At 1σ the script only logs, at 2σ it invokes Claude read-only to diagnose, and at 3σ Claude may act, though only by opening a PR into the review gate or triggering a pre-approved runbook. The trigger layer can be a scheduled workflow in GitHub or GitLab, a webhook from the existing monitoring stack, or a Cron Job inside the network. Claude runs stateless, either as a non-interactive step on a CI runner or as an Agent SDK service in a sandboxed container, and the CI/CD play covers the deployment and model-access options. Because the run is stateless and non-interactive, a loop can begin and end without anyone starting it. The agent writes its diagnosis as intent.md in the Stage 1: Plan format, covering the anomaly and its evidence, a proposed outcome, the affected systems and any open questions. From there the finding goes through the pipeline like anything else. The service owner or on-call engineer triages the queue, routing product-facing findings to the product owner. Fix now, schedule, or dismiss. Dismissals tune the bands and help to reduce noise. When a fix ships, add an eval for the incident the continuous evals play to ensure that such issues are protected against going forwards. What it looks like for example, a bands.yaml monitoring CI test failure rate metric: ci test failure rate baseline: rolling 30d rules: western electric tiers: 1sigma: { action: log } 2sigma: { action: diagnose, tools: "Read,Grep,Bash gh run view " } 3sigma: { action: propose, routes: pull request, runbook:rollback-deploy } Governance considerations The tier boundaries are enforced from version-controlled config, with permissions and managed settings denying production access. Invocations, findings and triage decisions are logged with a timestamp. A service owner triages and approves findings, resulting changes go through the normal PR review gate, and the runbooks the agent may trigger were approved in advance. How to measure it Leading indicator Time from band breach to an intent.md in the triage queue, against the old time from incident to post-mortem action. The detection script's log has the breach timestamp and tier of incident. Lagging indicator The share of findings that become merged fixes triage queue against actual PR history , and repeat incidents of the same class, which should fall as the fixes add cases to the eval suite. Examples When the CI test failure rate breaches 3σ, the agent quarantines the flaky test or opens a revert PR, and the review gate decides. When the post-deploy 5xx rate breaches 3σ with a deployment in the window, the agent triggers the existing rollback pipeline. When PR cycle time trips a drift rule, the agent writes a report for engineering leadership, which shows the harness works for process metrics as well as production ones. Detection stays deterministic. Claude is invoked once a band is breached, and the tier sets what it may do. Recurring codebase scans A security scan is a point-in-time statement about a codebase under a particular model, and both halves go stale: the code changes every week, and each model generation finds vulnerabilities the previous one missed. The AI-native answer is to run the scan on a schedule, without a human in the invocation path, and to send what it finds through the same gates as any other change to the codebase. Claude Security is the hosted form of scheduled scanning. Connect a GitHub repository, and scans run on Claude Mythos 5 in Anthropic's infrastructure, with each finding validated before it is reported and a confidence rating attached. Suggested patches are reviewed and applied in Claude Code on the web. The organization gets the findings without needing access to the model itself. TraditionalSecurity scanning is an event with a scan launched before a release or an audit. The report goes to a tracker, and the backlog is worked down by hand until the next event. Code written in between is covered by whatever the PR review caught. AI-nativeScans run on a schedule against every connected repository, on the most capable model available, with findings validated before anyone reads them. Each finding is handled the way a breached control band is: a fix that fits in one PR goes through the review gate, and anything larger becomes an intent.md. Coverage is dated from the last run, not from the first Getting started Prerequisites The PR review gate and hooks as approval gates Stage 5: Deploy , so that findings go through review like any other change. The intent.md format from Stage 1: Plan for findings too large for a single PR. Infrastructure Claude Security is available to Claude Enterprise organizations in public beta. It needs the Anthropic GitHub App installed on the target repositories cloud-hosted github.com , Claude Code on the Web enabled, Extra Usage turned on with a spend limit set, premium seats for the people who run scans, and the feature switched on by an admin at claude.ai/admin-settings/claude-code. Scans are billed on consumption at Mythos 5 rates, so the spend limit should match the size and number of repositories. How to execute it The security lead connects the repositories and organizes them into projects by repo, service, or team, so ownership of findings is clear from the start. Run a first full scan of the most critical repositories, including ones that have been scanned before by other tools or by earlier models. Treat the first scan as the baseline. The first scan will likely surface findings in code that was considered clean. Set a schedule per project. Weekly is a sensible default for actively developed services; scope scans to a directory or branch where a repository is large or mixed. Triage findings with the confidence rating in hand. Dismiss with a reason, so the dismissal is recorded and the same finding does not return as new on the next run. For a bounded finding, open the suggested patch in Claude Code on the Web, review it, and send it through the PR review gate like any other change. The agent that proposed the fix has no route to approve it. For anything wider than one patch, such as an architectural weakness or a pattern repeated across services, write it up as intent.md in the Stage 1 format and start it at Plan. When a fix is released to production, add an eval for the vulnerability class to the suite from the continuous evals play, so the configuration that steers the agent is tested against that class from then on. Export findings as CSV or Markdown, or use webhooks, to keep the organization's existing tracker and audit systems as the system of record where auditors already expect them. Governance considerations The scan runs under the organization's admin controls meaning what repositories are connected, who holds a scan seat, and the spend limit are all set centrally. Every finding has a validation result and a confidence rating, and every dismissal has a reason, so the scan history is an audit record of what was found, fixed, and consciously accepted. Fixes reach production through the PR review gate and branch protection rather than from the scan itself. Claude Security augments existing static analysis and dependency scanning. The deterministic checks stay in CI, and the model-driven scan covers the context-dependent vulnerabilities those checks are not built to find. How to measure it Leading indicator Share of connected repositories on a schedule, and time from a finding being reported to its patch entering the PR review gate, read from the scan history and the PR metadata. Lagging indicator Vulnerabilities found by the scheduled scan set against those found in production or by external report, from the incident tracker; and the trend in findings per scan on repositories that have been through several runs, which should fall as fixes and evals accumulate. Claude on call with Claude Tag Incidents can also arrive via other means such as workplace communication apps, like Slack or Teams. Incidents can look like a 10pm Slack message for an urgent fix on an incident channel and can now be actioned immediately. Claude Tag public beta currently available in Slack makes Claude a member of those channels under its own identity, so each new incident gets a first responder and the response itself becomes part of the loop and memory for future incidents. The conversation and institutional knowledge stay in the channel, with anyone in the channel able to guide and action the response. Any team member can test hypotheses, explore new options and investigate in real time with the channel history adding to the auditability. Through access to MCP Claude verifies the metric is back at baseline and confirms it in the thread, writes the post-mortem to a version-controlled lessons file that future investigations can read. Incidents are not the only work Claude Tag picks up. Tagged on a ticket over MCP or asked in the channel, Claude triages the work the same way. A small, well-bounded fix arrives as a PR through the review gate, and anything larger is written up as intent.md for Stage 1: Plan, at which point the loop starts feeding itself. See: how Claude Tag runs on-call for CI/CD at Anthropic. Closing thoughts Models and harnesses have become more advanced, allowing organizations to not just transform how they produce code, but the entire software development lifecycle. This transformation keeps human judgement central to the process and considers the governance and regulation requirements of large enterprise organizations. This guide consolidated many of the real best practices our Applied AI team executes on a daily basis for our customers, and we hope you found it a practical and actionable resource. The loop keeps running. Human judgement stays above it. Resources and acknowledgments The documentation below is what a platform team needs to set those controls up, in roughly the order you would roll them out. Thanks to Jim Blackhurst, Will Steuk, and Jamal Arif for their contributions to this guide, which was inspired by and built on much of their previous work.