HoneyHive is an enterprise-oriented AI agent observability and evaluation platform, designed to help organizations track, evaluate, monitor, and continuously improve AI agents in production environments, covering use cases across teams, technology stacks, and sensitive data environments.
Purpose and Positioning
HoneyHive's core positioning is "production-grade AI starts with observability." As the agent observability platform behind mission-critical AI, it helps enterprises observe, evaluate, and trust their running agents in production environments, turning production behavior and domain expertise into better system performance.
Main Capabilities
Observability and Debugging
- Inspect every trace: replay any run step by step, view each tool call, prompt, and decision in the order they occurred, and jump directly to the span where behavior is abnormal.
- Debug long-running agents: track trajectories spanning hours or days, including retries, loops, and handoffs between sub-agents, to find the specific step where things started going wrong.
- Monitor production behavior: evaluate live traffic and correlate scores with real user outcomes, making quality measured by the production environment rather than the test suite.
- Alert before users notice: set thresholds on any score, detect drift, and receive notifications via email or webhook when thresholds are breached.
Evaluation and Experimentation
- Run experiments and discover regressions: compare agent versions on the same dataset and clearly see improvements, regressions, and whether changes are releasable.
- Build datasets from real traces: automatically turn production failures into test suites, so every future version is tested against the use cases that previously caused production issues.
- Evaluate with LLMs or code: score LLM outputs, tool calls, A2A interactions, and full trajectories using LLM judges, custom code, or composite evaluators.
- Bring in expert participation: automatically route flagged traces to annotation queues, where domain experts judge what actually happened.
From Failure to Fix
- Root cause localization: give coding agents access to traces, evaluations, and production context to investigate failures and identify their source.
- Fix at the source: make fixes, version prompts, rerun the evaluation that captured the issue, and redeploy within the same workflow.
- Release gates: run automated evaluations on every release in CI, blocking any change with regressions from reaching users.
- Close the loop on agent operation: use any coding agent with dedicated Skills at each step to run the full optimization loop.
Standardization and Governance
- Can be defined onceEvaluate and monitor standards, and apply them consistently across every agent and application.
- Provide developers with reusable agent skills for instrumentation, setting up evaluations, investigating failures, and more.
- Manage evaluators, datasets, prompts, and alerts as code, with Git version control, CI workflow automation, and accessibility to coding agents.
- Define fully custom roles across dozens of fine-grained permissions, scoped to the organization, workspace, or project.
- Standardize telemetry from OTel, coding agents, and enterprise platforms through unified semantic conventions.
- Export traces, evaluations, and metrics to a data warehouse or lakehouse in open formats.
How to Use
The platform is designed for complete cross-functional teams, from the engineer instrumenting the first agent to the risk officer who ultimately signs off on deployment. It offers the following ways to get started:
- Custom agents: Automatically instrument over 100 models and frameworks via SDK, trace LLM outputs, tool calls, and handoffs, score results, compare versions, and monitor production behavior without changing how the team builds.
- Coding agents: Bring coding agent sessions into the same observability standard, tracking cost, latency, tool usage, and outcomes across repositories, teams, models, and vendors.
- No-code platforms: Observe agents within enterprise systems such as ServiceNow, Microsoft Copilot, and Salesforce, and centrally manage quality signals, approvals, and audit evidence.
Use Cases
- Engineering teams: Debug full traces, test changes on real production use cases, and catch regressions before customers do.
- Domain experts: Review real outputs, define scoring criteria, and make expert judgment reusable across every agent and version.
- Risk and compliance: View traces, evaluation results, and human reviews in the same defensible record for internal and regulatory review.
Data Security and Deployment
Agent traces carry rich and highly sensitive inputs and outputs that traditional observability cannot handle. HoneyHive is purpose-built for AI traces, centralizing visibility without centralizing sensitive data, with each deployment isolated by design.
- Virtual data plane: HoneyHive operates two planes and isolates a virtual data plane for each tenant.
- Bring your own cloud: Store traces and run evaluations in the user's cloud, while HoneyHive manages the rest.
- Self-hosted: Both planes run in the user's infrastructure, deployed and managed via Kubernetes.
- Compliance: SOC 2 Type II audited, compliant with GDPR and supports EU data residency, with HIPAA BAA available for healthcare.
- Identity and permissions: Supports Okta, Azure AD, Google, and Ping, with JIT provisioning, enforced MFA, and session policies managed by the IdP.
- Auditing: Audit logs can be streamed to Splunk, Datadog, or any SIEM, and every access, change, and export can be audited upstream.
Limitations
The materials do not provide details such as specific pricing, billing models, free trials, or platform limitations; this information is not currently available.
