Skip to main content

Development Workflows

Agentic workflows that cover the full development lifecycle: generating code, writing tests, pre-reviewing pull requests, and validating deployments. The goal is not "an AI that writes code" but a set of narrow, trustworthy workflows that each do one job well and slot into the existing pipeline.

Engineers adopt workflows that save them time on work they dislike and trust the output of. Start there. Automated test generation and PR pre-review earn trust fast because the human still has the final say — and because a wrong suggestion is cheap to reject.

Each workflow is an agent loop (ReAct or Plan-and-Execute) grounded in the context layer and gated by guardrails.

Structure​

The workflows chain, but each is independently useful. You don't need the whole pipeline to get value from any one stage.

The Workflows​

Code generation — scoped implementation from a ticket or spec. Grounded in the context layer so generated code matches existing conventions, imports the right internal libraries, and respects architectural boundaries. Best constrained to well-specified, well-tested areas of the codebase.

Automated test creation — generate unit and integration tests for new or changed code. A high-trust starting workflow: tests are verifiable (they run), low-risk (a bad test fails loudly), and address work engineers often skip. Use Reflection so the agent runs its own tests and iterates until they pass.

PR pre-review — an agent reviews the diff before a human does, flagging bugs, missing tests, convention violations, and security issues. It doesn't replace human review; it removes the trivial findings so humans focus on design. Pairs with Guardrails and the LLM-as-Judge pattern.

Deployment validation — post-merge, an agent checks that a change is safe to ship: did the right tests run, do canary metrics look healthy, does the change match its stated intent. The last gate before vibe deployment.

Key Characteristics​

  • Narrow beats general — one workflow that reliably writes tests beats a "do anything" agent that's right 60% of the time. Trust compounds; one bad surprise sets adoption back months.
  • Human-in-the-loop by default — the engineer approves, edits, or rejects. Authority is earned per workflow, expanded only after the eval data justifies it. See Human-in-the-Loop.
  • Grounded in conventions — generation that ignores your codebase's patterns creates review burden, not leverage.
  • Verifiable output — favor workflows whose output can be checked automatically (tests run, types check, linters pass). Verifiability is what lets you safely increase autonomy.
  • Pipeline-native — these live in CI/CD and the PR flow, not a separate chat window. Meeting engineers where they already work is most of adoption.

When to Use​

  • The team has a consistent, well-tested codebase the context layer can ground against.
  • There's a clear, repetitive lifecycle stage engineers would happily hand off.
  • You can verify the workflow's output automatically before a human sees it.

Pitfalls​

  • Boiling the ocean — shipping a "full autonomous engineer" before nailing one workflow. Sequence the Agent Sprawl instead of inviting it.
  • Untested generated code, fast — speed without the test and review gates is just faster regressions. The Happy Path Mirage is the failure mode.
  • No attribution — every agent commit, suggestion, and approval must be traceable. Accountability gaps are how trust dies.