Have Claude verify its own output before submitting — tests, builds, and screenshot diffs all run to completion before the task is marked done. Engineers receive code that's already passed verification, not code that 'might be correct.'
Evals are the AI-native equivalent of stage-gate QA — collect 20–50 real tasks as test cases, run them automatically whenever CLAUDE.md, skills, or hooks change, and block the merge if the pass rate drops. Every production incident becomes a permanent eval.
Plug Claude into the CI/CD pipeline — start with read-only build failure triage, gradually add write operations behind existing gates, expose deployment tooling through MCP, and tier autonomy by environment. The governing principle is one sentence: 'The agent may act up to the production gate and cannot pass it.'
Anthropic's Claude Academy offers a free 14-lesson course that takes AI-assisted coding from 'individuals using Claude Code' to 'an organization-wide development lifecycle.' Four core concepts — intent.md, CLAUDE.md, Skills, and Hooks — wire together into a complete AI-native SDLC.
Cloud routine outcomes config creates a feature branch, so the agent commits and pushes there instead of main. Fix: remove outcomes + add explicit git checkout main in skills. Also hit a list API pagination bug (cursor never advances) along the way.
Promptfoo combines prompts, providers, test cases, and assertions in YAML to produce repeatable local and CI evaluation matrices, with red teaming against the same targets. It lowers the testing barrier but does not remove output variance, LLM-judge bias, or hosted data-flow concerns.
Speakeasy records OpenAPI, Overlays, targets, and generator versions in `.speakeasy/workflow.yaml`, enabling local or CI generation, compilation, and publishing of multi-language SDKs.
Not everyone should use a coding agent to modify code directly. AI Native teams need interface specs, test-first development, monorepo, security guardrails, human-in-the-loop, and token budget controls. Building an agent platform layer on top of coding agents and clearly redefining developer roles is the right path forward.
Put Claude Code into GitHub Actions with anthropics/claude-code-action: /install-github-app sets everything up in one command, @claude in a PR or issue comment gets bugs fixed, branches pushed, and PR creation links returned; Bedrock/Vertex/Foundry backends switch via one input with OIDC and no stored keys; the GitLab CI/CD integration (beta) mirrors it as a single .gitlab-ci.yml job where every change flows through a merge request.
GitHub Actions is the lowest-friction CI/CD tool available today, ideal for small-to-medium projects. The key to monorepos is using path filters so only affected apps trigger a build.
Use OpenSpec to break requirements into engineering tasks, Claude Code to implement them, hooks to auto-format and protect, local review before committing, three AI reviewers running in parallel on PR, and auto-deploy after merge. This entire workflow lets one person maintain quality across six sub-projects.
Hooks are automated safety nets (blocking bad commits), Skills are interactive workflows (running checks + auto-fixing), and instruction files (CLAUDE.md / AGENTS.md) are behavioral guidelines. Each layer operates independently, but together they enable an AI agent to automatically run lint, typecheck, and build checks before every commit.