Most development teams write code with AI, and many report large productivity gains. The cost shows up later. But a growing body of evidence, and hard-won practical experience, reveals a paradox: without systematic guardrails, AI tools can generate technical debt faster than they generate features. In the 2026 Stack Overflow developer survey, “plausible but wrong” code was the most common cost of AI mistakes.
Our Approach
We help you move from scattered experiments, or AI used as advanced autocomplete in a few teams, to an engineering practice where agents do real work under rules you can audit.
We deploy a layered defense architecture, with the developer’s AI agent running in a sandbox, code ruthlessly checked against specifications and testable conditions, as well as quality checks and vulnerability checks. Findings are automatically evaluated and fixed, and evidence of correctness is produced for every change.
The agents are governed like any other part of your platform. Each has its own identity and a human owner, reaches models, tools and other agents only through a gateway that enforces policy and logs every call, and never holds the keys and secrets it uses.
What We Deliver
A working scaffold
Pre-commit hooks, CI pipelines that run the full set of checks on every change, agent instructions for spec-first work, and quality tool configuration. It plugs into your golden paths, or you can apply it to existing projects right away. This is part of the proving ground all code has to go through.
A governed platform for agents
Agent identities, delegation so an agent acts for a person only when that person allows it, one control point for model, tool and agent-to-agent traffic, cost per team, and an audit trail of every call. Developers’ coding agents run in sandboxes, on their own machines or in the cloud. Each commit records which AI assisted it and who reviewed it, the kind of record the EU AI Act and the new Product Liability Directive expect.
The specification is the source
Engineers and domain experts agree on what must be true, with examples and invariants, and approve it before any code exists. Agents write the code, and every change has to prove itself against tests, type checks, mutation testing and your own domain rules before anyone is asked to merge it. Reviewers read the spec and the evidence, which scales far better than reading every diff. On our own benchmark, this approach fixed 95% of findings without regression and to about an eighth of the cost.
Agents that pay down technical debt
After each pipeline run, agents pick up findings from tools such as SonarQube, fix what they are allowed to, and prove each fix before opening a merge request. When a fix needs a new test, one agent writes the test and another reviews it. Findings in tests, specs or CI configuration go to a person, and a person decides every merge.
Legacy modernization with proof
We capture what an existing system actually does as executable checks, then rebuild it and require the new version to give the same answers. We used this method to rewrite our own code-fixing agent from Python to Rust, one component at a time.
Local and on-premise AI development
Small language models run locally behind the same gateway, so code and data stay in your network. On our benchmark a local model fixed fewer findings than a hosted one, and none of the fixes it delivered broke anything, because the same checks apply.
Vulnerability triage that reads your code
Scanners list every known vulnerability in your dependencies. Our agent works out which of them your code actually reaches, shows you the line, and turns the list into an upgrade order: upgrade now, upgrade next, or can wait. It adds reachability on top of classic dependency scanning, links every claim to the evidence behind it, and sends every model and tool call through the governed gateway, where each call is traced.