HoneyHive

What is HoneyHive?

HoneyHive is a specialized observability and evaluation platform designed to help teams build reliable AI applications by providing deep visibility and control over AI agents throughout their lifecycle. It enables developers and domain experts to test, debug, monitor, and optimize complex AI systems, including multi-agent workflows and retrieval-augmented generation pipelines. HoneyHive supports continuous evaluation using custom benchmarks, human feedback, and automated metrics, while integrating with existing monitoring infrastructure via OpenTelemetry standards. The platform bridges development and production by capturing real-world failures and converting them into actionable test cases, facilitating faster iteration and improved AI system reliability.

Key Features

• End-to-End AI Observability

  Logs detailed AI application data with OpenTelemetry, providing full traceability of agent interactions and decision steps for faster debugging.

• Custom Evaluation Framework

  Enables creation of tailored benchmarks and evaluators using code, LLMs, or human review to measure quality and detect regressions continuously.

• Production Monitoring and Alerting

  Monitors AI agent performance and quality metrics in real time, detecting anomalies and failures across complex multi-agent pipelines.

• Collaborative Artifact Management

  Centralized versioning and management of prompts, tools, datasets, and evaluation criteria, synchronized between UI and code for team collaboration.

• Flexible Deployment and Compliance

  Offers multi-tenant SaaS, dedicated cloud, and self-hosted options with SOC-2 Type II, GDPR, and HIPAA compliance to meet enterprise security needs.

Use Cases

• AI Agent Reliability Testing : Run structured tests and benchmarks on AI agents to identify and fix performance regressions before deployment.
• Production AI Monitoring : Continuously observe AI applications in production to detect failures, analyze root causes, and improve system robustness.
• Multi-Agent Workflow Debugging : Trace and debug complex AI pipelines involving multiple agents, retrieval systems, and tool integrations.
• Collaborative AI Development : Enable cross-functional teams to manage and version AI assets and evaluation datasets for consistent quality assurance.
• Compliance and Auditability : Maintain detailed logs and version histories to support regulatory compliance and system audit requirements.

FAQs