Platform

Modus Sentinel: agent evaluation and improvement infrastructure.

Modus Sentinel observes, evaluates, and improves coding and research agents across real repositories and messy workflows.

Evaluation loop

From agent activity to measurable improvement

Watch agent sessions and repository activity
Capture prompts, tool calls, diffs, test output, failures, and retries
Build a timeline of what happened and why
Score task completion against real evidence
Produce failure taxonomies and improvement notes
Feed the next run, training loop, or product decision

Audience

Built for teams working near the frontier

AI labs building coding and research agents
Developer-tool companies measuring agent reliability
Internal AI platform teams
Research groups evaluating long-horizon model behavior

Why it matters

Agents need measurement before they can improve.

Repo-level work is long-horizon, stateful, and failure-prone. The useful signal is not only the final answer; it is the sequence of decisions, tools, edits, tests, failures, and recoveries that led there. Sentinel turns that sequence into evidence that can drive product decisions, training loops, and governance.