BentoLabs AI is the monitoring and learning layer for long-running AI agents. The platform detects when agents fail silently or drift from their goals, identifies root causes, and suggests fixes. It captures learnings across runs so agents improve continuously rather than rediscovering solutions each time. The company is based in San Francisco and is part of YC's Spring 2026 batch.
BentoLabs AI
ActiveThe monitoring and learning layer for long-running agents
Total raised
$500K
1 round
Stage
Seed
Jan 2026
Team
1-10
since 2026
Pricing
—
Founded
2026
San Francisco, United States
Agent-ready
—
Monitoring layer for long-running AI agents
Silent failure detection
Goal-drift detection for agents straying from objectives
Root-cause analysis of agent failures
Suggested fixes for identified issues
Learning capture across agent runs
Continuous improvement so agents avoid rediscovering solutions
Observability across multi-step, long-horizon agent tasks
12/100
Early
MCP server
Public API
Webhooks
OAuth 2.0
SDKs
No public agent surfaces detected yet.
Jan 2026 Seed $500K ● Y Combinator
Capital network
$500K raised ·1 backer·10 network links
- Backers1
- Shared portfoliocompanies these backers also fund
- Extended networkfunds that co-invest alongside them
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- What is BentoLabs AI?
- BentoLabs AI is a monitoring and learning layer for long-running AI agents that detects silent failures and goal drift, finds root causes, and captures learnings so agents improve.
- What problem does it solve?
- Long-running agents can fail quietly or drift from their goals; BentoLabs surfaces these issues, identifies causes, and suggests fixes before they compound.
- How does it help agents improve?
- It captures learnings across runs so agents retain solutions rather than rediscovering them each time, increasing reliability over time.
- What types of agents is it for?
- It is designed for long-running AI agents executing multi-step tasks where silent failures and drift are hard to catch manually.
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