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.
- 01Spec & acceptance criteriahuman
- 02Plan & implementagent
- 03Tests & evalsCI
- 04Review & mergehuman
- 05Observe in productiontelemetry
Typical engagement
We start with one team and one codebase, prove the model, then scale it across the organization with your engineers leading.
- DURATION
- [WEEKS]
- TEAM
- [FRONTIER ENGINEERING ENGINEERS]
- YOU GET
- Agent-ready repo, eval-gated CI, team playbook
- INVESTMENT
- [YOUR PRICE]