Keep AI experiences reliable as your startup scales
Honeycomb gives AI-native teams end-to-end visibility into what their agents and models do in production, from the prompt through every tool call and downstream span, in one trace. You send telemetry by conversation, model, tool, or customer, and investigate it with Canvas and MCP.
AI is a black box in production
Building with generative AI and agents is unpredictable. A single prompt or model change can shift behavior, cost, and reliability, and traditional monitoring was not built for non-deterministic systems.
Fast iteration creates unpredictable failures
Small changes in prompts or models introduce unexpected issues, and long multi-turn conversations make it hard to pinpoint when and why an agent went wrong.
AI costs become harder to control as usage grows
Token spend, tool calls, and retries add up fast. Without per-request visibility, the margin on an AI feature erodes as usage grows.
Sampling decides what you and AI can see
AI systems produce more telemetry than any team can afford to store, which forces teams to make a tradeoff. Cut too much and the trace you need is gone. Keep everything and the volume runs past budget.
Failures are hard to trace beyond the model
Dedicated AI tools often watch the model but not the services around it, so a bad answer is hard to trace to the tool call, query, or system behind it.
See the full AI system, not just the model
Honeycomb brings application observability, infrastructure telemetry, distributed tracing, and AI visibility together in one place. Engineers can investigate any production issue without switching tools or deciding ahead of time which data they'll need.
AI Native companies trust Honeycomb
Hear from industry voices
Hear how Intercom operates Fin, its AI customer agent using a time-to-first-token signal, cost per interaction on the same traces tracking both speed and cost.
Thanks to Honeycomb, SLO-based monitoring has proven superior for LLM reliability than traditional metrics. Given AI’s unpredictable nature, SLOs help us catch and investigate anomalies without triggering a flood of noisy alerts.
Related features
Discover the features AI-native teams reach for when an agent misbehaves, cost climbs, or a release regresses.
Want to know more?
Talk to our team to arrange a custom demo or for help finding the right plan.