# Reflexio: Reduce task failure by 36% while saving token by more than 50% > Source: > Published: 2026-08-28 17:56:50+00:00 Learning platform for AI agents # Learn from real interactions.Improve behavior. Stop repeating mistakes. Reflexio turns user corrections, failed paths, and successful outcomes into behavior changes your agents reuse — each one visible, and revocable. “There's a $49.99 charge on my card I don't recognize.” Missed the second charge “I've refunded the $49.99 charge.” The user came back 10 minutes later. “There's also a $9.99 one.” 2 conversations Caught both charges at once “I found two unfamiliar charges — $49.99 and $9.99. Refund both?” Nothing. The user was already done. 1 conversation - 1Search the full window of recent charges before resolving any single one. - 2Present everything unfamiliar in one message, and ask whether to refund together. ## Let your coding agent integrate Reflexio. Start with the portable skill, or wire the same retrieve-and-publish loop through Python, REST, or the CLI. Give this prompt to Codex, Claude Code, or Cursor. ``` Follow this skill to integrate Reflexio into my agent:https://github.com/ReflexioAI/reflexio/blob/main/skills/integrate-reflexio/SKILL.md ``` Run this from your agent application's repository. The skill inspects the existing lifecycle, implements the Reflexio loop, and verifies the changed path. [Use integrate-reflexio](https://github.com/ReflexioAI/reflexio/blob/main/skills/integrate-reflexio/SKILL.md) ## Static agents, made self-improving. The lessons are already in your logs. Four things Reflexio does with them. Self-improvement loop ### It keeps learning, not just once. Every conversation your agent has feeds back in. Reflexio notices what keeps going wrong, turns the repeats into a learning, and retires older learnings once newer conversations contradict them. When your policy or your product changes, the agent changes with it instead of staying stuck on what was true the day you set it up. Refunds allowed within 30 days Confirm the order before refunding Refund window is now 14 days The March learning is no longer used Self-tuning learnings ### Every learning is tuned by the evidence it produces. Reflexio watches how a learning actually performs once your agent starts using it — the sessions it improved, and the ones it did not — and revises it from those cases. It is a continuous, research-backed optimization process, so a learning gets better the more it is used rather than staying whatever it was when first captured. Where the learning helped Where it fell short Rewritten from those real cases Validated before it counts Evaluation & impact ### Know whether it actually helped. Improvement is measured against what matters to the people using your agent: was the problem solved, did they have to correct it, did it end up with a human. You define what success means, Reflexio scores conversations against it, and every result traces back to the learnings behind it. Was the user's problem solved? Did they correct it, or need a human? Which learnings did the work The methodology stated, so you can check Review & control ### Every learning is auditable, and under your control. Open any learning to see what it holds and the evidence behind it. Rewrite it, approve it, reject it, or delete it — a rejected learning stops being used straight away. Want your agent to use only what you have signed off? That is one setting. Check all recent charges first Traceable to the evidence behind it Rewrite it, or approve it as is Reject or delete, and it stops being used ## What you are wiring into. One loop. Your agent publishes what happened, Reflexio extracts what to do differently, and the next run reads it back. Nothing is retrained. ### Your Agent AI-powered assistant Learning & evaluation ### Learning Store Persistent context ### Your Agent ### Learning Store ### Your Agent AI-powered assistant ### Reflexio Learning & evaluation ### Learning Store Persistent context - Simple integration - Wrap your existing LLM calls with a lightweight SDK — no agent rewrite needed. - Actionable signals - Triggering conditions and actionable feedback are extracted from user corrections automatically. - Evolving intelligence - Learned behaviors consolidate, and conflicts between them resolve, over time. - Precise context injection - Only the relevant signals are retrieved at the moment of inference — which keeps token cost down. ## Why Reflexio? Built different from the ground up to create agents that actually learn. - Autonomy through reflection - Agents think back on their performance and optimize their own logic — not just retrieve stored facts. - Full extraction control - Tunable extractors that look for business-specific signals — a churn signal in customer service, a syntax error in a coding agent. - Safe behavioral evolution - Every learned behavior is scored against a control response before you rely on it, and rejecting one revokes it from retrieval immediately. - Low-cost learning extraction - When a user corrects your agent's tool usage or process, Reflexio extracts that as actionable feedback for all future similar scenarios. - Data rights - Your users can have their data exported or permanently erased on request. Bring your own storage or cloud to meet your privacy requirements. - Conflict resolution - A background process de-duplicates and resolves conflicting learning signals, preventing behavioral drift and learning rot. ## Not another memory layer. | Traditional memoryWhat memory tools store | ReflexioBehavioral learning platform | |---|---| Stores what users said | Learns how the agent should act | You read the logs to find the problem | The correction and its trigger are captured together | Facts a model may or may not retrieve | Rules you can read, in a queue you control | No way to tell whether a memory helped | Responses scored against the un-augmented one | No way to undo a bad memory | Reject one and it drops out of retrieval | "What did the user say?" | "How should the agent behave differently next time?" | ## Your data, your keys, your cloud. Different teams draw the line in different places. Reflexio runs anywhere from fully managed to fully self-hosted — and the API your agent calls never changes. - Managed - Nothing to operate. We run the service and your organization gets its own isolated schema — the right default when infrastructure is not where you want to spend the year. - BYOK - Use your own provider credentials: OpenAI, Anthropic, DeepSeek, Qwen, xAI and more, or a custom endpoint. For teams with negotiated model contracts, their own rate limits, or a policy about which providers may see their traffic. - Your database - We run the service; the learning data sits in a Supabase project or Postgres instance you own. For teams that need to query, back up and retain it under their own controls. - BYOC - Reflexio runs inside your own AWS, GCP or Azure account. For regulated environments where the requirement is simply that nothing crosses the account boundary. - Self-host - You run all of it, single-tenant, against a database you own, with no connection back to us. For air-gapped deployments and teams that need to be independent of a vendor's uptime.