Safe AI coding agents
in real codebases.

A coding agent that works on a 50-line script and a coding agent that works in a 500k-line monorepo are not the same animal.

Once an agent has the keys to a real codebase, the failure modes change. Bad refactors don't just compile and run — they pass tests in surprising ways, regress invariants nobody wrote down, and ship to production through PRs that look fine to a tired reviewer at 4 PM on a Friday.

Safe means: the agent is allowed to be wrong, and the system around it catches that wrongness before customers do.

• Your team is already using Claude Code, Cursor, Aider, or Copilot — and the question has shifted from "will this work?" to "how do we let it ship without burning the on-call rotation?"
• You have a non-trivial existing codebase. Institutional knowledge lives in heads, conventions, and load-bearing comments.
• You don't want vendor lock-in — you want patterns and tooling you can run yourself.

• Eval harnesses. Concrete, repeatable scenarios that score whether a change made the system better or worse. Holdout sets so the agent can't memorize the answer.
• Merge-policy guardrails. Automated gates that decide whether an agent PR is safe to land — calibrated risk, paper trail, no vibes.
• Review loops. Agent-on-agent and agent-on-human review with explicit rubrics.
• Sandboxing. Tight, recoverable runtime boundaries so an agent can experiment without breaking the workspace.
• Observability. Every agent action traced, replayable, and comparable across runs. Answer "what did the agent actually do?" without reading 800 lines of chat log.