Self-improving Agents
Every audit result, human correction and production failure feeds back into the system. An always-on agent turns that signal into specific recommendations that your team can decide to ship.
Book a demoProactive monitoring
An always-on agent watches the deployment, flags behavioral regressions and spots anomalies in audit pass rates, with alerts baselined against a rolling twelve-week window rather than a fixed threshold someone guessed at.
Recommendations, not just alerts
Observability signals are turned into specific, actionable recommendations (a prompt adjustment, a new northstar, or a regression test to lock a correction in) grounded in how the platform actually works.
The loop that already compounds
A failed session converts to a regression test instantly, so the issue cannot reappear. A thumbs up or down on any audit remark becomes a calibration example on the northstar it failed. Accuracy improves with every session reviewed.
“That repair usually runs about £180.”
Caller asks for a price
Runs on every release
Negative, on Pricing Disclosure
Active from the next audit
Validated against live traffic
A change can be split across production traffic and measured on defined metrics before any broad rollout, so a prompt or tool change is proven against real interactions.
Version 83 against Version 84
6 days, 8,412 live sessions
FAQs
Four things that already run: an always-on agent monitoring the deployment for regressions and anomalies, observability signals turned into specific recommendations, resolved failures locked in as regression tests, and human feedback on audits calibrating the standards future audits grade against. Changes are proposed and validated; your team applies them.
No. Recommendations surface for review: a prompt adjustment, a new northstar, a regression test. Someone on your team accepts or rejects each one, and nothing modifies a production workflow on its own.
Configuration changes, in the same artifacts your team already edits: an adjustment to a prompt, a new northstar to hold the agent to a standard it was missing, or a regression test that locks a correction in so the issue can't return. Recommendations are grounded in how the platform works: nodes, patterns, variable syntax, rather than generic advice.
A/B testing across versions. Define control and challenger, split real production traffic between them, and measure resolution rate, transfer rate and audit pass rate under identical conditions, so a change is validated against live behavior before it rolls out broadly. You can read more about A/B testing in our technical blog.
No. Everything the loop uses comes from your own deployment, inside your own isolated instance. Customer data is never used to train our models or our providers' models without explicit consent, and is never shared across tenants.
