AI Coding Agents
AI coding agents are the most immediate productivity multiplier in software today — and most orgs are getting a fraction of the value, because they handed out licenses and called it a strategy. Sound familiar? It's the deployment gap again, this time inside your engineering org.
We do the actual deployment: codebases made agent-ready with the context files, conventions, and guardrails agents need to work safely; CI-integrated agents doing first-pass review and test triage; workflows redesigned around delegation rather than autocomplete; and measurement, so "it feels faster" becomes throughput data your CTO can defend. We run our own delivery this way — it's a large part of how a small senior team ships at the pace we do.
Claude Code rollout
Configuration, permissions, and conventions for coding agents across your engineering org.
Agent-ready codebases
Context files, verification loops, and repo structure that make agents productive and safe.
CI-integrated agents
Automated first-pass code review, test generation, and failure triage in your pipeline.
Governance for generated code
Review policies, provenance, and security scanning tuned for agent-scale output.
Adoption measurement
Cycle time and throughput baselines so the productivity claim survives scrutiny.
Our engineers already use Copilot. What's different?
Autocomplete accelerates typing; agents accelerate delivery. A coding agent takes a ticket, explores the repo, writes the change, runs the tests, and opens the PR. Different tool, different workflow, and an order of magnitude different ceiling — if the org is set up for it.
Is agent-written code safe to ship?
As safe as your verification makes it — same as human code. We put the gates where they belong: tests, static analysis, security scanning, and human review on the paths that matter. Provenance is tracked, so you always know what was generated and what was reviewed.
Where do teams go wrong with coding agents?
Buying licenses without changing anything else. Agents need context — documented conventions, good test coverage, clear repo structure — to work well. The prep is unglamorous, which is why it gets skipped, which is why results disappoint.
Put AI Coding Agents to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →