What HoneyHive is
HoneyHive is a platform for keeping agents honest after they ship. It tracks events and behaviour, supports continuous evaluation of agent quality, and provides prompt management and monitoring so teams can see when an agent drifts rather than discovering it from user complaints.
What you can do with it
- Track agent events and behaviour
- Run continuous quality evaluations
- Manage prompts and experiments
- Monitor production behaviour
- Compare versions before release
Who it is for
- AI engineering teams
- Platform teams running agents
- Enterprises scaling agent deployments
What to watch out for
- Evaluation is only as good as the test sets and metrics you define; generic scores mislead
- Recorded traces contain user inputs, so redact personal data and set retention
- Monitoring without alert thresholds produces dashboards nobody watches
- Continuous evaluation costs compute, which needs budgeting
Pros & cons
✓ What we like
- Continuous rather than one-off evaluation
- Prompt management alongside monitoring
- Built for production scale
! What to watch out for
- Metric design decides usefulness
- Trace data needs redaction
- Ongoing evaluation cost
FAQ
What does it evaluate?
Agent behaviour and quality over time, using tracked events and evaluation runs.
Is it useful without test sets?
Much less. Define representative cases and scoring rules first.
What data does it hold?
Traces of agent runs, which may include user inputs needing redaction.
Last reviewed: 2026-09-18
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