Agents That Get Better While You Sleep: Self-Improving Loops Explained
Most AI deployments degrade slowly: models stay fixed, edge cases accumulate, and nobody notices until customers complain. We build agents around the opposite premise — every cycle should leave the system smarter than it found it.
The loop
Each agent runs plan → act → observe → improve, and appends every cycle to an append-only loop log:
{"cycle": 3, "plan": ["serve workload", "apply lesson: expand KB coverage"],
"observations": {"ok": true}, "lesson": "", "token_cost": 110}The improve step writes a lesson when something goes wrong; the next cycle's plan reads prior lessons first. Lessons persist across restarts.
Measuring improvement, not vibes
Because logs are structured, quality isn't a feeling — it's three numbers our eval harness computes nightly per project:
| Metric | Gate |
|---|---|
| Resolution rate | ≥ 80% |
| Handoff rate | ≤ 20% |
| Lesson-application rate | trending up |
When a gate trips, the report flags it before a client ever has to.
Why this compounds
Traditional automation has a flat cost-of-improvement curve: a human must notice, diagnose, patch, redeploy. Loop-native agents collapse that into observe → lesson → applied-next-cycle. Over weeks the gap between a static bot and a self-improving one stops being incremental — it becomes categorical.
That compounding is the product. Everything else is plumbing.