BentoLabs AI
BentoLabs AI provides a closed-loop production infrastructure platform for AI agents, enabling teams to monitor, evaluate, and improve agentic systems in one place. The platform trains custom regression signals from plain-English failure descriptions, captures OpenTelemetry-native traces, and detects behavioral drift to help teams ship self-improving agents. It also groups alerts into incidents and judges each as real or benign to reduce alert fatigue.
- Artificial Intelligence
- Developer Tools
- AI Agents
- Software Only
- Data & Analytics
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Building AI agents is easier than ever, but trusting one in production isn't. Prompt changes, model swaps, and new tools reach production as silent regressions—caught by customers rather than dashboards. When teams do find a regression, context is scattered across traces, evals, and fixes in separate tools, so learnings don't compound across releases.
Solution
Bento is a closed-loop production infrastructure platform for AI agents that lets teams monitor, evaluate, and improve agentic systems in one place. Users describe any failure mode in plain English, and Bento trains a custom signal on their own traces, fires it in real time, and backfills the entire history to show how long the issue has been occurring. The platform captures OpenTelemetry-native traces from every framework, enabling teams to jump into span trees or follow signal badges straight to the call that broke. Bento also groups alert fires into incidents and judges each one real or benign, so teams only get alerted for actual drift. The platform detects behavioral drift by pinpointing exactly what shifted in agent behavior. This closed-loop approach helps teams ship self-improving agents that compound learnings across releases.
Target Audience
Primary customers are engineering teams building and operating production AI agents who need to detect regressions, maintain trust, and systematically improve agent performance over time.
Features
- Regression signals: describe failure modes in plain English; Bento trains custom signals on real traces, fires in real time, and backfills history to show issue duration
- OpenTelemetry-native tracing: captures every span from any framework with full span tree exploration
- English-language alerts: write alerts in plain English; Bento groups fires into incidents and judges each as real or benign
- Behavioral drift detection: pinpoints exactly what shifted in agent behavior over time
- Full-path evaluation: monitors the journey, not just the destination, catching trajectory corruption that output-only evaluation structurally misses
- Task-slice tracking: breaks metrics down by task type and user cohort to expose regressions hidden in blended accuracy numbers