Reflexio makes AI agents better with every interaction. When users correct an agent, a path fails, or something works particularly well, Reflexio turns that experience into behavior the agent can reuse next time. Instead of leaving valuable lessons buried in logs, your agent continuously learns what to repeat and what to avoid — with every learning visible, testable, and reversible. Reduce task failure rate by more than 30%, while saving tokens by more than 60%.
Hey Product Hunt 👋 I'm Yi, co-founder of Reflexio. Before starting Reflexio, I was tech lead in Meta and adjunct professor at University of Washington teaching ML and business applications.
Today we're launching Reflexio: a learning platform that makes your AI agents fail less and burn fewer tokens, by learning from what actually happens in production.
Here's what got us started: people use AI agents every day, but agents never actually get better with use. Even with memory, an agent that failed a task yesterday will fail the same way today, across different users — because nothing connects what happened in production back to how the agent behaves next time. The online learning loop just isn't there.
We learned firsthand that closing that loop manually — reading traces, spotting failures, rewriting prompts — is a painful, never-ending job. Reflexio autonomously observes your agent's live traces, learns from successes, failures, and user corrections, and continuously optimizes behavior. No manual tuning.
The results? In our case studies, agents with Reflexio:
🎯 Cut task failure rate by 36%
💸 Reduced token usage by 57%
📈 Improved response quality in 47% of interactions, with negligible regressions
Try it today: sign up free at reflexio.ai and get 30 days of Pro on us.
Congrats on the launch. The 30% failure drop and 60% token savings numbers are strong, how did you actually measure those? Was it an A/B between agents with and without Reflexio on the same task set, or before/after on the same production traffic?
interesting approach - the generalized-rule part is what gives me pause though. if one user's "correction" is actually bad advice (they misunderstood the task, or just pushed the agent toward a wrong answer confidently), and that gets rolled up into a rule applied to every user, how do you catch that before it spreads? is there some confidence threshold or does every generalized rule need a human to sign off before it goes from "one user's pattern" to "everyone's default behavior"?
Would love to look under the hood to see how the agents are learning. Part of me thinks it's just a loop with markdown files haha, well that's at least how I've got my agents learning.
Yi, congrats on the launch. I know closing the online feedback loop for agents in production can be a real headache manually sifting through traces and adjusting prompts can get tedious pretty quickly.Just a quick question: when Reflexio optimize behavior is it dynamically tweaking the prompt or few shot context or is it fine tuning the models behind the scenes?
That 57% reduction in tokens with fewer failures is impressive! Wishing you all the best with the launch
Congratulations @yilu ✌️ Question, How can this be integrated into existing stacks like LangGraph or CrewAI?
Token savings aside reducing repeated failure seems like the bigger win to me. Fewer retires means less frustration for users and less wasted compute.
I like the reversible part. Letting teams are exactly what the agent learned makes continues learning feel a lot less like a black box.
Congrats @yilu BTW, I am curious like, how does Reflexio decide which correction actually becomes the shared learning?
i wonder how it handles conflicting feedback from different users. does it learn a general rule or keep the behavior context specific?
The reversible part is important. Can teams review a learning before it starts affecting the agent?
How much control do developers get over which lessons the agent keeps?
Can I review and approve learnings before they go live, or is it completely autonomous?
The journey of building Reflexio started from a painful lesson Yi and I learned firsthand.
At our previous company, we worked on the personalization service and memory infrastructure powering AI agents at very large scale, and saw how hard it is to make agents actually learn from experience. It wasn’t just about storing user facts. Teams spent huge amounts of time reviewing production conversations: where agents failed, where users corrected them, which tools were called incorrectly, and how those lessons could be turned into better behavior through metrics, evaluations, and experiments.
That kind of learning infrastructure is powerful, but it takes serious engineering investment — the sort only a handful of companies can afford. Most agent builders and startups don’t have a dedicated platform team of that size behind them.
That became our “aha” moment: as AI agents become more useful, every team will need a way for agents to learn continuously from real interactions, changing environments, and user feedback.
Earlier this year, we left to build Reflexio: a learning platform for AI agents. We prototyped it locally, offered it as a cloud service, integrated it with coding agents like Claude Code and Codex, and worked closely with design partners to refine the product through real usage.
Today, we’re excited to launch Reflexio on Product Hunt. It’s already being tested with design partners and enterprise customers, but this is just the beginning. If you’re building AI agents and believe they should get better every time they interact with the world, we’d love for you to try Reflexio, share feedback, and join us on the journey.
nice，can I export or delete all learnings if I decide to move off the platform
I think I got the concept.
How do you measure "negligible regressions"? Is there an eval harness that runs before a learning gets applied?
Congrats! Does Reflexio learn per-user, per-agent, or globally? Curious how you separate a correction that's specific to one person from one that applies to everyone.
About Reflexio on Product Hunt
“Behavioral learning that makes AI agents better over time”
Reflexio launched on Product Hunt on September 5th, 2026 and earned 282 upvotes and 42 comments, earning #2 Product of the Day. Reflexio makes AI agents better with every interaction. When users correct an agent, a path fails, or something works particularly well, Reflexio turns that experience into behavior the agent can reuse next time. Instead of leaving valuable lessons buried in logs, your agent continuously learns what to repeat and what to avoid — with every learning visible, testable, and reversible. Reduce task failure rate by more than 30%, while saving tokens by more than 60%.
Reflexio was featured in SaaS (44k followers), Developer Tools (518.8k followers) and Artificial Intelligence (477.8k followers) on Product Hunt. Together, these topics include over 256.4k products, making this a competitive space to launch in.
Who hunted Reflexio?
Reflexio was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Reflexio stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
fmerian
Hey Product Hunt 👋
I'm Yi, co-founder of Reflexio. Before starting Reflexio, I was tech lead in Meta and adjunct professor at University of Washington teaching ML and business applications.
Today we're launching Reflexio: a learning platform that makes your AI agents fail less and burn fewer tokens, by learning from what actually happens in production.
Here's what got us started: people use AI agents every day, but agents never actually get better with use. Even with memory, an agent that failed a task yesterday will fail the same way today, across different users — because nothing connects what happened in production back to how the agent behaves next time. The online learning loop just isn't there.
We learned firsthand that closing that loop manually — reading traces, spotting failures, rewriting prompts — is a painful, never-ending job.
Reflexio autonomously observes your agent's live traces, learns from successes, failures, and user corrections, and continuously optimizes behavior. No manual tuning.
The results? In our case studies, agents with Reflexio:
🎯 Cut task failure rate by 36%
💸 Reduced token usage by 57%
📈 Improved response quality in 47% of interactions, with negligible regressions
Try it today: sign up free at reflexio.ai and get 30 days of Pro on us.