BentoLabs AI (YC P26)

Other tools show you the known failures. We show you the unknowns as well. But we don't stop at detection. We help prevent them from repeating. Knowledge carries forward. Successful patterns get reinforced. Failures get avoided. Knowledge compounds over time!

Why we call it 'learning infrastructure': Traditional monitoring: Log → Alert → Debug → Patch → Repeat Learning infrastructure: Capture → Learn → Apply → Improve → Compound The difference between reactive maintenance and proactive improvement.

If you've ever noticed your production AI agents ignoring instructions mid-run and spent hours debugging it. Here's why it might be happening and how you can fix it. bentolabs.ai/blog/why-ai-ag…

We just hired our first ever AI employee at @BentoLabsAI. He doesn't ask for equity. He doesn't need a desk. He won't eat your lunch from the office fridge. (We're remote anyway, but still.) Within an hour of joining, Thomas already had his first task. And before we could follow up, he already was. Thomas helps us with: → Building & fixing our landing page → Frontend/UI issues → Integrations, debugging, code reviews → Writing docs → Even drafting posts (not this one of course) The deal is simple: give him a repo, tell him the outcome, give him the constraints. He delivers a first pass, we iterate and ship. The team's off to a great start with him. Well I didn't think onboarding an AI would feel this... normal. Welcome to the team, Thomas. Don't hallucinate on us.

Why you can't vibe-code your way to a better production agent Vibe-coding finds a fix for the 30 trajectories you pasted in not the 999 you didn't. Read the full breakdown here👇🏻 bentolabs.ai/blog/vibe-codi…

We ran a controlled experiment on ARC-AGI-3 Same agent. Same harness. Same budget. One change: self-learning enabled. Which resulted in: - 2.6× higher score, - 3 first-ever solves, - 34% drop in cost per successful outcome. Full breakdown👇🏻 bentolabs.ai/blog/self-lear…

→ bentolabs.ai

Where monitoring catches every failure across your production traces before a support ticket pops up. And our self-learning layer extracts what worked from every run and carries it forward. So you can ship agents that compound with every release instead of restarting from zero.

We're on X now! A monitoring and learning infrastructure that improves your AI agent.