BentoLabs AI
Monitoring and learning layer for long-running agents
About BentoLabs AI
BentoLabs is the monitoring and learning layer for long-running agents. We detect when agents silently fail or drift from the user's goal, system prompt, or tool contracts, show affected users and root cause, and suggest the prompt, skill, or harness fix. As more teams deploy agents, keeping them reliable in production becomes mission-critical. Bento sits directly in the production loop and gives teams the operational leverage required to scale agent ecosystems without scaling human firefighting alongside them. The result is a system that turns opaque agents into agents that can be monitored, debugged, and improved continuously. The founders learned this problem at Emergent (YC S24), where they built and operated production coding agents used by 5M+ users. Abhinav was hire #1 and helped Emergent hit SWE-Bench #1 and scale from $0 to $100M ARR in just 8 months. Kaushik was hire #2, led full-stack engineering at Emergent, and was key to building the infrastructure that made production agents reliable, observable, and debuggable. Bento's self-learning engine has also lifted ARC-AGI-3 (internal) by 2.6x and Terminal-Bench 2.0 (internal) from 42.2% to 52.4% pass@1 with the same model, tools, and budget.
Public traction evidence
Each signal links to the public source used for attribution.
- LinkedIn
BentoLabs AI Introduces Issues for Production Problem Tracking | BentoLabs AI (YC P26) posted on the topic | LinkedIn
[Skip to main content](https://linkedin.com/posts/activity-7472186741601361920-E0or#main-content)[LinkedIn](https://linkedin.com/?trk=[redacted-public-param]) * [Top Content](https://www.linkedin.com/top-content?trk=[redacted-public-param]) *...
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And we are live! Quote 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! https:// ycombinator.com/launches/Qcw-b entolabs-ai-monitoring-and-learning-layer-for-long-running-agents … 0:12 / 1:58
And we are live! Quote 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 &...
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Almost didn't document the day, my team made sure we did. Some days you just feel the shift. We booked a studio, brought in a team, and spent the day trying to capture what @BentoLabsAI actually is right now. Where we started, where we are, and where we're going. It's one thing Show more Kaushik and BentoLabs AI (YC P26)
Almost didn't document the day, my team made sure we did. Some days you just feel the shift. We booked a studio, brought in a team, and spent the day trying to capture what @BentoLabsAI actually is right now. Where we started, where we are, and where we're going. It's one thing...
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We’re introducing Issues by @BentoLabsAI. Issues are recurring production problems Bento surfaces directly from your agent’s traces, logs, and runs. Instead of treating every failed run as a one-off, Bento groups similar agent failures into clear, trackable Issues into a single, prioritised view of what's breaking in production, how often, and which runs or users were affected. This helps teams see what keeps breaking, where it surfaced, how often it happened, and what needs attention now. And when an Issue is to be fixed, ‘Apply Fix’ closes the loop: Bento investigates the problem against your connected repo, proposes a code change, and opens a reviewable PR. The outcome is simple: fewer silent failures, fewer repeated mistakes, and agents that keep improving continuously. Talk to us and get onboarded on bento to experience the fastest way of fixing your drifting agents → Link in the fi
We’re introducing Issues by @BentoLabsAI. Issues are recurring production problems Bento surfaces directly from your agent’s traces, logs, and runs. Instead of treating every failed run as a one-off, Bento groups similar agent failures into clear, trackable Issues into a...
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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 Show more
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 Show...
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Article What I learned building AI agents at a company scaling 0→$100M ARR. TL;DR: I led AI agents at Emergent for ~2 years, through the 0→$100M ARR stretch. Worked on everything agent-related: system prompts, tools, subagents, skills, evals, the lot. Consolidating...
Article What I learned building AI agents at a company scaling 0→$100M ARR. TL;DR: I led AI agents at Emergent for ~2 years, through the 0→$100M ARR stretch. Worked on everything agent-related: system prompts, tools, subagents, skills, evals, the lot. Consolidating...
- LinkedIn
Harness Engineering Explained | BentoLabs AI (YC P26) posted on the topic | LinkedIn
[Skip to main content](https://linkedin.com/posts/bentolabs-ai_harness-engineering-the-underrated-discipline-activity-7471415687144701952-c9Sk#main-content)[LinkedIn](https://linkedin.com/?trk=[redacted-public-param]) * [Top...
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We are live on @ycombinator ! Building the monitoring and learning layer for long-running agents. Quote 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! https:// ycombinator.com/launches/Qcw-b entolabs-ai-monitoring-and-learning-layer-for-long-running-agents … 0:12 / 1:58
We are live on @ycombinator ! Building the monitoring and learning layer for long-running agents. Quote 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...