Handit.ai receives high praise for its ability to enhance AI agent performance through prompt engineering, data augmentation, and experimentation. Users appreciate its ease of use, even for non-engineers, and its seamless integration with projects like N8N. The product is noted for addressing a significant market need by ensuring AI agent consistency and reliability, allowing users to focus on other tasks without worrying about erratic responses. Despite potential challenges from competition and integration, Handit.ai's comprehensive approach is seen as a potential standard-setter in AI operations.
Handit.ai
Hi @jramr7
Great approach to a real challenge in production AI. Making agents more reliable and helping them improve over time is key. Moving from just observing to actually fixing issues feels like a smart and needed step.
A couple of questions came to mind:
1. You mention it auto-generates prompts and datasets to improve performance. Could you explain how that process works and how it learns from the agent’s decisions?
2. Since it’s stack agnostic, like LangChain and RAGs, what’s the typical learning curve or effort to integrate it into an existing agent already in production?
Wishing you the best with the launch. And, Happy hacking!
Handit.ai
@pastuxso Hey! love the questions,
How does it auto-generate prompts and datasets?
We evaluate a subset of all the decisions your agents make using the actual production logs—looking at accuracy, cost, latency, or even business KPIs, depending on what you want to evaluate (you can configure your own evaluators).
When drift or underperformance is detected, Handit automatically proposes fixes: this could be a better prompt, a rerouted model, or a more relevant few-shot dataset. These suggestions are based on patterns observed in failing vs. successful outputs.
Then HandIt A/B tests them to see what really improves performance by running the same evaluators on the same subset of production data. Once a fix wins, it's ready to ship with one click. You stay in control; the system handles the grunt work.
We're working on a self improving memory, where all this edge cases and errors are stored and can be retrieved as context for your even better prompts to never fail again in the cases it has failed.
What’s the integration effort like?
To be honest? Pretty low. We’re stack-agnostic—LangChain, custom RAGs, just plain python, js, http calls, whatever—as long as you can send JSON logs (we have libraries and http calls for that), you’re in. Most teams get it running in under an hour with our integrations team in a call. From there, you’ll start seeing evaluations and improvements almost immediately.
Happy to show it in action if you’re curious! you can get in a call with our integrations team here: https://calendly.com/cristhian-handit/30min
—José
Thanks so much for the detailed response.
It really helps clarify how you handle the auto-generation of improvements, especially the use of real production data and A/B testing to validate solutions. It sounds solid and results-driven.
Also great to hear that integration is pretty low-effort and stack-agnostic. That really lowers the barrier for teams already running agents in production.
Thanks again for sharing these insights.
Handit.ai
@pastuxso No problem at all, hope to see you soon using handit to make your AI improve itself automatically!!
Handit.ai
@reid_crooks Hey great question! it relies on past performance logs + the evaluations that those logs had + an enriched insight generation of what went wrong and what could potentially fix it. Then we do an A/B testing system that validates that this problems are solved agains the same evaluators and production data. And finally you just get a better prompt with a comparison of the actual metrics calculated with your evaluators. See how it looks like:
(And now we're implementing something that's still being tested, called self improving memory to store all of this errors as context for your prompts to not only fix your prompts but give them context of their past errors so they never make them again!)
Handit.ai
@reid_crooks sure, I'll be in X then!
My AI's hallucinate more than my uncle during his collage days. Looking forward to using this!
Handit.ai
@yakuraapp HAHAHAHA, you made my day with this one, will start using in AI conferences. If you want our team to help you setting it up, feel free to book a call with us https://www.handit.ai/ just press demo, and schedule a call!
This is something new! Love the direction! Congrats on the launch @cristhian_camilo_gomez_neira @jramr7
Handit.ai
@cristhian_camilo_gomez_neira @lakshya_singh Thank you! we really think self improving AI it's gonna be the next big leap in AI, that's why we open sourced it, to get everyone in the next wave!
Exactly what I was looking for. Gonna pull your docs today and feed it to Claude code. What's your pricing model?
Handit.ai
@rajiv_poddar we're open source, so you can start using us right away without cost in our own cloud, once you get past 1 agent or 10k events a month, you can upgrade depending on how much events you're handling!
Smoopit
Really impressive! Love the idea of not just catching agent issues but actually fixing them automatically. How customizable are the evaluation criteria if a team has really niche KPIs? @jramr7
Handit.ai
@rachitmagon Hey! great question, You can setup your own evaluators depending on the KPI's and specific needs of you company, and LLM use cases, if you need a hand, our setup team can help you guys setting the right evals for ya'll. Hope to see you in Handit this week!
Smoopit
@jramr7 That's awesome, custom evaluators for specific KPIs make total sense, thanks for offering to help with the setup.
Felicitaciones José M y Cristian que gran herramienta han desarrollado y sobre todo la gran utilidad que tiene excelente trabajo
Handit.ai
@carlos_lince Gracias!!!
Handit.ai is a game-changer—an open-source engine that smartly boosts your AI agents automatically. Simple, powerful, and effective!