BentoLabs AI (YC P26) (@BentoLabsAI) / X

BentoLabs AI (YC P26)

53 posts

BentoLabs AI (YC P26)

@BentoLabsAI

The monitoring and learning layer for long-running agents

bentolabs.ai

Joined April 2026

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BentoLabs AI (YC P26)
@BentoLabsAI
Jun 1
We are live on @ycombinator ! Building the monitoring and learning layer for long-running agents.
Y Combinator
@ycombinator
Jun 1
.@BentoLabsAI is the monitoring and learning layer for long-running agents. Their learning layer gives agents model-jump gains: Sonnet 4.5 went 42.2%→52.4% on TB2 (Internal). Congrats on the launch, @Abhinavv_soni & @kacppian! ycombinator.com/launches/Qcw-b…
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BentoLabs AI (YC P26) reposted
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 12
Did you see 'Harness Engineering' on your feed too? A term that suddenly blew up everywhere. It didn't have a name until February, now Hashimoto has posted on it and OpenAI has proven it at a million lines. So what does the work actually look like? Reading a hundred
Harness Engineering: the underrated discipline of production AI
From bentolabs.ai
104
BentoLabs AI (YC P26)
@BentoLabsAI
9h
When your agent regresses, what/who do you reach for first?
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BentoLabs AI (YC P26) reposted
Abhinav Soni
@Abhinavv_soni
Jun 12
One of the most important thing I couldn't have justified before: trust your instinct on prompt and agent design. You'll feel something is wrong in a prompt or a trajectory long before you can explain why. That feeling is real signal. It's built from reading hundreds of runs,
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BentoLabs AI (YC P26)
@BentoLabsAI
Jun 12
Did you see 'Harness Engineering' on your feed too? A term that suddenly blew up everywhere. It didn't have a name until February, now Hashimoto has posted on it and OpenAI has proven it at a million lines. So what does the work actually look like? Reading a hundred
Harness Engineering: the underrated discipline of production AI
From bentolabs.ai
104
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 12
Agents fail silently in production Traditional monitoring misses agent-specific issues Teams debug same problems repeatedly No learning from the runs ✅ BentoLabs fixes all of this. Ready to stop debugging blind? →
Bento — Self-learning infrastructure for AI agents
From bentolabs.ai
47
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 11
The problem with patching long-running agents through a chat session: Three weeks later, a similar pattern recurs. Nobody remembers what was tried last month. Six months later, an auditor asks why the agent handles this category of cases this way. Nobody can produce a coherent
Bento — Self-learning infrastructure for AI agents
From bentolabs.ai
46
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 11
Catch the decision, not the symptom. By the time the output is wrong, the agent made the wrong call several steps earlier. Watch the trajectory, not just the final answer.
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BentoLabs AI (YC P26)
@BentoLabsAI
Jun 10
The scariest agent bugs are the polite ones. No stack trace. Just an answer that is confidently a little bit wrong, a few thousand times, before anyone notices.
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BentoLabs AI (YC P26) reposted
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 9
Hot take! Adding another model to your stack will not make your agent reliable. It will give you two things to debug and zero memory of either.
134
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 9
Hot take! Adding another model to your stack will not make your agent reliable. It will give you two things to debug and zero memory of either.
134
BentoLabs AI (YC P26)
@BentoLabsAI
Jun 8
Friday night fix. Monday morning regression. Sound familiar? 30 traces in your clipboard. A brilliant fix. You shipped it. The 999 trajectories you never looked at had other plans. That's the problem. You can vibe-code a fix. You cannot vibe-code an improvement program. Full
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BentoLabs AI (YC P26)
@BentoLabsAI
Jun 6
What's your system for handling this in production?
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