Abhinav Soni
Founder of BentoLabs AI, Monitoring and learning layer for long-running agents
Company
Abhinav Soni is listed as a founder of 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.
Founder traction evidence
Public posts and activity attributed directly to this founder.
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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 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...
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Stop fine-tuning your model. Your harness is broken. The model was fine.
Stop fine-tuning your model. Your harness is broken. The model was fine.
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Article The middle of your system prompt is an attention graveyard. Most people write top-down because that's how humans read. But LLMs follow an Attention U-Curve they prioritise the beginning and the end, while the middle is a "dead zone" where attention drops...
Article The middle of your system prompt is an attention graveyard. Most people write top-down because that's how humans read. But LLMs follow an Attention U-Curve they prioritise the beginning and the end, while the middle is a "dead zone" where attention drops...
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Article Stop treating prompts like code. They're not. They're prose. When I started working with agents two years ago, I thought prompt engineering was a kind of programming. Because that's how everyone talked about it. You write prompts to specify behaviour, handle...
Article Stop treating prompts like code. They're not. They're prose. When I started working with agents two years ago, I thought prompt engineering was a kind of programming. Because that's how everyone talked about it. You write prompts to specify behaviour, handle...
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Hot take A model upgrade isn't a library bump. It's a personality transplant. Stop shipping them same-day.
Hot take A model upgrade isn't a library bump. It's a personality transplant. Stop shipping them same-day.