AI agents and agent harnesses. Useful AI, with the controls to match.

Start with a workflow, not a model.

We start where automation has a clear purpose, accessible data and a result you can evaluate; some steps are better served by plain software. The harness defines what the agent can do: scoped tool access and permissions, state and task orchestration, timeouts and retries, and human approval for consequential actions. Before anything expands, representative tasks and adversarial inputs reveal failure modes. In production you get execution tracing, model cost monitoring, sensitive-data controls and versioned prompts.

Is this right for your team?

For teams with repetitive work that touches several tools, or an agent prototype that needs evaluations, permissions and observability before real users rely on it. Our published work includes evaluations of a read-only AI SRE and a guarded text-to-SQL analyst.