Agent-ready codebases
Context files, module boundaries, fast deterministic tests and scripts an agent can run end to end without guessing.
Practice 01 — AI-native engineering
Rebuild how software gets made — around agents.
Giving engineers a coding assistant is the easy part. We redesign the codebase, the workflow and the review process so agents can do real work safely — and your team ships more of what matters.
The problem
Most teams bolt AI onto a process designed for humans typing every line — and get faster typing, not faster delivery.
The shift
AI-native teams write specs, context and tests first. Agents produce the code; engineers design, review and own it.
What we put in place
Spec-driven development
Work starts as a reviewable spec with acceptance criteria — the contract both humans and agents build against.
Review pipelines that scale
Automated first-pass review, risk-tiered approvals and clear ownership, so more output doesn't mean more bugs.
CI with eval gates
Any change that touches a prompt, model or agent runs its eval suite before merge, just like unit tests.
Background & parallel agents
Safe sandboxes, permissions and task queues so agents can run long jobs while engineers keep their focus.
Team enablement
Pairing, workshops and new rituals — planning, estimation and code review rewritten for an agent-assisted team.
The AI-native loop
Humans decide. Agents execute. Tests arbitrate.
Every change moves through the same loop, whether a person or an agent wrote it. Understanding comes before code, and the agent never grades its own work — tests run separately, and a claimed fix only counts once they pass.
- 01Research the codebaseagent
- 02Spec & acceptance criteriahuman
- 03Plan, then approveagent → human
- 04Implement in bounded sessionsagent
- 05Verify independentlyCI
- 06Review & mergehuman
- 07Observe in productiontelemetry
Built with the same discipline we measure
Our Wharf benchmark runs coding agents through thousands of independently verified attempts — the same loop, applied to the agents themselves.
Typical engagement
We start with one team and one codebase, prove the model, then scale it across the organization with your engineers leading.
What you get
- 01
An agent-ready repo
Context files, clear module boundaries and one-command tests on your pilot codebase.
- 02
Eval-gated CI
Changes to prompts, models or agents run their eval suite before merge.
- 03
Your team playbook
Conventions for specs, review and agent permissions — written down, in the repo.
Done when
Your engineers run the loop on the pilot codebase without us, and are ready to take it to the next team.