Product updates, engineering notes, and ideas on building agents that learn.
Behavioral drift in self-improving AI agents can result from stale, conflicting, harmful, or overly broad learned rules. Prevent it by tracing and testing learnings, monitoring behavior, and editing, limiting, or rejecting unsafe lessons without retraining the model.
Self-improving AI agents need risk-based governance over learned rules: teams must review, scope, trace, test, and revoke lessons that become unsafe. Reflexio supports this learning layer, while permissions, sensitive actions, and human approval remain controlled by the wider system.
Production AI agents can repeat failed actions and falsely report success, so teams should trace each run, find the root cause, and distinguish system faults from behavioral mistakes. Repeated mistakes can become focused, tested reusable lessons, with outcomes measured for recurrence, recovery, false completion, and regressions.
The article explains the difference between continuous evaluation, which tests agent performance, and continuous learning, which turns past experience into reusable lessons that change future behavior, and describes how the platform Reflexio combines both approaches to help AI agents avoid repeating mistakes.
A step-by-step guide on measuring AI agent behavior improvement, covering how to establish a baseline, select task-specific metrics such as success rate and retries, compare results before and after changes, and verify whether learning leads to lasting gains without regressions.
Learn when AI agents should act, request approval or escalate, and how human corrections can become reusable, controlled behavioural learning.
Explore how production outcomes, successful actions and failed paths can provide evidence for improving AI agent behaviour.
Learn how AI agents can turn user corrections into evaluated, reusable behavioural guidance without retraining the underlying model.
A nightly fine-tune and a one-line note appended to a markdown file are both sold as agents that learn from experience. They are the same loop with different parts swapped out. This is a map of the field: what changes, what decides, and when — and why the second question is the one that separates methods that compound from methods that only look like they do.
Dreaming makes an agent's memory better. Only measurement makes the agent better. Anthropic's Dreams, ChatGPT, Codex, Gemini CLI and OpenClaw all rewrite agent memory offline — and every gate they document checks whether the rewritten memory is tidy, not whether the agent improved.
Creao used Reflexio to turn successful agent behavior into reusable guidance—cutting median model calls by 75%, reducing task failures by 36%, and showing positive impact from retrieved learnings in 47% of evaluated cases.
AI memory and agent self-improvement sit in the same slot, use the same storage, and are sold against the same failure — which is why they get confused. The difference is what has to happen before the system writes anything down. One writes when something is said. The other writes when something is judged.
Loop engineering is how we get agents to iterate toward a goal on their own. Self-improvement is the next stage — a loop wrapped around those loops, so each pass makes the next one better. This is a field note on how the two ideas connect.
Memory remembers what happened. Learning changes what the agent does next time. LGRO — Learn, Generalize, Reflect, Optimize — is a practical framework for non-parametric, self-improving AI agents.