Your agents repeat mistakes.Reflexio makes sure it doesn’t happen twice.
Every correction a user gives your agent disappears once the conversation ends.
Reflexio saves it, turns it into a rule, and your agent uses that rule next time.
“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.
Impact at scale
What that looks like at scale.
> 36%
Execution failure
Eliminate repeat tool errors and invalid execution paths on the first attempt.
> 50%
Token usage
Prevent redundant reasoning and tool calls by retrieving the rules your agent already learned.
> 20%
Execution latency
Give your agent relevant context before it answers, instead of repeating corrections.
Let your coding agent integrate Reflexio.
Start with the portable skill, or wire the same retrieve-and-publish loop through Python, TypeScript, 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.mdRun this from your agent application's repository. The skill inspects the existing lifecycle, implements the Reflexio loop, and verifies the changed path.
Use integrate-reflexioHow it works
Three steps.Higher agent success rate.
Inspect the learning loop, review extracted rules, and reject learnings you don’t want your agent to retrieve.
- 1
Data ingestion
Two doors in, one engine.
Connect an instrumented tracing platform or publish directly from your application. Reflexio ingests the execution paths, tool calls, and user corrections that help your agent learn.
- 2
Intelligence engine
Turns failure logs into verified learnings.
Instead of burying edge cases in dashboards, Reflexio extracts recurring failures and synthesizes actionable execution rules. Your team retains complete learning and retrieval control.
- 3
Runtime orchestration
Retrieved before every answer. The learning never stops.
Before the next execution, your agent retrieves relevant personalized rules. Feedback from real responses keeps improving the learning loop, helping it avoid repeated mistakes and use its tools more reliably.
Not another memory layer
Memory remembers.Reflexio changes behavior.
Memory tools
- Stores what the user said
- You read the logs to find the problem
- No way to tell if a memory helped
Reflexio
- Learns how the agent should act differently
- Captures the correction and its trigger together
- Measures the relevance and impact of retrieved learnings
“What did the user say?” vs. “How should the agent behave next time?”
Why Reflexio
Enterprise-gradegovernance, built in.
Complete Controllability
Inspect learned rules and review, edit, or reject them in the portal.
Visible Learning
Inspect extracted profiles, playbooks, and evaluation results in the portal.
Instant Retrievability
Your data, your rules. Export, delete, or search learned intelligence, never locked in.
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
Cloud
Fully managed service. No infrastructure to run. Start in minutes.
BYOK
Cloud + Your Own Keys
Keep managed compute with your own model-provider credentials.
BYOC
Bring Your Own Cloud
Deploy inside your VPC. Your network, your perimeter, your compliance boundary.
FAQ
Questions people askbefore trying it.
Is this just another memory tool?
Memory stores what happened. Reflexio learns how your agent should behave differently, then retrieves those learnings for the next interaction.
Does it rewrite my model?
No. Reflexio supplies reusable learned context to your agent. It does not change your model’s weights.
Can I see what it learned?
Yes. Inspect the extracted profiles and playbooks, their evidence, and evaluation results in the portal.
What if it learns something wrong?
Reject the learning to exclude it from retrieval. You can inspect and edit it in the portal.
What happens if two learnings conflict?
Reflexio consolidates and resolves conflicting learning signals. You can inspect the resulting playbooks and reject a rule you do not want used.
Where does my data live?
Your choice: managed Reflexio, your own database, or inside your cloud. Choose the deployment boundary that fits your team.
Can a learning change my agent’s behavior without me knowing?
Pending learnings are retrievable by default. They remain visible and revocable in the portal; rejecting a rule excludes it from retrieval.
Do I need to change my whole agent to use this?
No. Integrate the retrieve-and-publish loop through the SDK, REST, CLI, or the portable coding-agent skill.
Stop watching youragent repeat itself.
Start free, or see it working on a real example first.