Code & Integration Agent

What a code agent actually is

A code and integration agent is a system that takes a structured engineering task — "generate a typed TypeScript client for this OpenAPI spec," "map fields from system A to system B with these transformations," "build a small internal tool to view this Firestore collection with filters and sort" — and produces working, tested, reviewable code.

It is not:

• Autocomplete in your editor (that's Copilot / Cursor / Claude Code; we use those, but they're the substrate).
• An AI that "writes your whole app." That's a marketing fiction.
• A senior engineer replacement. It is a force multiplier for senior engineers, not a substitute.

It is a defined workflow with: scoped task definition, eval/test suite, structured diff, CI pipeline, human approval for risky changes, and observability.

Where it earns its keep

The work software teams routinely lose weeks on:

• API client generation. OpenAPI exists, but the typed client doesn't, or it's out of date, or the codegen tool you used got abandoned three years ago.
• Schema mapping. System A has fields customer_id, total_eur. System B wants customerRef, amount.value, amount.currency. Six pages of mappings, all boring, all important to get exactly right.
• Internal tools. "I just need a page that lists pending approvals with sorting and filters and a one-click action." A senior engineer can build that in a day; the senior engineer is busy. The agent ships it in 90 minutes.
• Migration scripts. Old schema → new schema with data transforms. Manual is risky; the agent generates with tests against fixture data.
• Documentation. OpenAPI spec from real traffic. README from code. Architecture diagrams from imports. Boring, valuable, never done.

Anatomy of a working code agent workflow

[Task definition (structured)]
      ↓
[Context gathering: read relevant code + docs + schemas]
      ↓
[Plan: list of files to change with rationale]
      ↓
[Implementation: generate diffs file-by-file]
      ↓
[Tests: generate + run; iterate on failures]
      ↓
[Self-review: agent re-reads its own diff against the task]
      ↓
[Open PR with summary, plan, tests run, manual checklist]
      ↓
[CI: lints, types, tests, security scan]
      ↓
[Human review (mandatory for risky classes)]
      ↓
[Merge + deploy]

The shape is similar to how a careful junior engineer would work — small diffs, mandatory tests, approval gates. The difference is throughput.

Risk classes and autonomy

We tune the autonomy dial per organisation based on test coverage, change tolerance, and how much trust you've built with the agent.

Stack we tend to reach for

How we use code agents inside our own builds

Honest answer: every engagement we ship now uses code agents internally. They write the API clients, the schema validators, the test fixtures, the migration scripts, the internal admin tools, the OG image variants, the boilerplate around the actual interesting work. A 12-week production build today ships closer to what would have been 20 weeks two years ago, at the same headcount.

We do not use code agents to write the interesting parts — product UX, novel architecture, performance-sensitive systems. Those are human-driven with the agent as an accelerator for the boring bits.

Our The AI Development playbook post walks through how this changes our internal process.

Cost and timeline

Most of our clients access this capability as a force multiplier on their existing engagements with us, not as a standalone product.

Where it pairs

Code agents commonly chain with:

• Document processing agents when documents drive code (e.g. generate client SDKs from a vendor's API documentation PDF).
• Workflow orchestrators when generated code is part of an automation pipeline.
• Research agents when the code task starts with "figure out how this third-party system works."

If you have an engineering bottleneck on the kind of boring-but-important code we described, drop us a note. One paragraph is enough.