AI agents that
learn from every run.
Most agents make the same mistake twice. Ours don't. ClosedLoop turns every execution into training signal — observe, evaluate, improve, redeploy — so your agents compound in capability instead of drifting.
Open-loop AI is a dead end.
Today's agents ship once and decay. They fail silently, forget every lesson, and need a human babysitter to catch regressions. That's not autonomy — that's liability.
Same mistake, twice
An agent hits an edge case, fails, and nothing changes. Next week it fails on the exact same input, because nothing fed the failure back into the system.
Silent drift
Models update, data shifts, prompts rot. Without continuous evaluation, quality degrades quietly until a customer notices before you do.
Humans as glue
Teams burn engineering hours reading traces and hand-tuning prompts. The feedback loop exists — it's just made of people, and it doesn't scale.
We close the loop.
ClosedLoop wraps your existing agents in a continuous improvement cycle. Four stages, fully automated, with humans approving what matters.
Act
Your agent runs in production as usual — support tickets, code changes, ops workflows. ClosedLoop sits alongside it, not in its way.
Observe
Every run is captured end-to-end: inputs, tool calls, reasoning traces, outcomes, and real-world results — did the fix merge, did the ticket resolve.
Evaluate
An eval engine scores each run with LLM-as-judge rubrics, hard metrics, and outcome signals. Failures are clustered into patterns, not left as noise.
Improve
Prompts, tool policies, and memory are updated automatically. Candidates are regression-tested against your eval suite before promotion. Then the loop repeats.
Everything the loop needs, built in.
One system that takes your agents from "deployed and hoping" to measurably better every week.
Outcome capture
Full-fidelity traces plus the signal most tools miss: what actually happened after the agent acted.
Eval engine
LLM-as-judge rubrics, programmatic checks, and outcome-based scoring — versioned and auditable.
Automatic optimization
Prompt and policy candidates generated from failure clusters, tested offline, promoted only when they win.
Regression gates
No update ships unless it beats the incumbent on your eval suite. Improvement without roulette.
Distilled memory
Lessons from thousands of runs compressed into knowledge your agent can actually use on the next one.
Human-in-the-loop
Sensitive changes route to review. You set the autonomy dial — the loop respects it.
Put your agents on the loop.
We're onboarding early design partners. Tell us what your agents do and where they break — we'll show you what closing the loop looks like on your stack.