Carrotlabs provides a continuous‑learning reliability platform for production AI agents, creating sandboxed evaluation environments that mirror real workflows and automatically measuring latency, correctness, tool success, and business‑aligned quality metrics. The system uses these metrics to trigger prompt tuning, retrieval updates, policy refinements, and optional fine‑tuning, ensuring agents remain performant and aligned with domain rules over time. A unified dashboard and API integrate monitoring, alerts, and retraining pipelines into existing observability and CI/CD tools for enterprise AI teams.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying AI agents in production often face unpredictable latency, output quality drift, misalignment with domain-specific business rules, and fragile tool/function calling, making “good enough” performance a liability.
Solution
Carrotlabs offers a continuous‑learning reliability loop for production AI agents. It creates evaluation environments that mirror real workflows and measures key performance indicators such as latency, correctness, tool success rate, and business‑aligned quality metrics. Based on these measurements, the platform automatically applies prompt tuning, retrieval enhancements, tool‑policy refinements, and targeted fine‑tuning to improve agent behavior. Ongoing monitoring detects data drift and model changes, triggering retraining to keep agents performant over time. The results are presented in a unified dashboard that integrates with existing systems, enabling teams to maintain reliable, high‑quality AI services at scale.
Target Audience
Primary customers are enterprise AI product teams, data science and DevOps groups, and large organizations that rely on autonomous agents for business processes, customer support, or internal automation.
Features
- Workflow‑mirroring evaluation sandbox that runs real‑world tasks against AI agents
- Automated metric collection for latency, correctness, tool success, and custom quality scores
- Continuous optimization engine that performs prompt tuning, retrieval updates, and policy adjustments
- Optional fine‑tuning of foundation models based on drift detection and performance gaps
- Real‑time monitoring and alerting with automated retraining pipelines
- Centralized dashboard and API for integration with existing observability and CI/CD tools
- Support for custom model distillation and deployment to reduce latency while preserving accuracy