LLM observability for AI apps that don't behave the same way twice
Understand how LLM applications and AI agents behave in production. Connect prompts, model calls, tool use, cost, failures, and downstream services so teams can debug issues and improve performance with real data.
AI is a black box in production
Building with generative AI and AI agents is an art. A single prompt tweak can lead to wildly different responses, making debugging difficult.
Testing Limitations
Traditional testing methods are ineffective for AI-driven systems, which are inherently non-deterministic, requiring new approaches to evaluate and understand performance in production.
Unpredictable Failures
Small changes in prompts or models can introduce unexpected issues and unpredictable failures.
Complex AI Agent Conversations
AI agents carry on long-running conversations with multiple turns, making it challenging to pinpoint when and why errors occur.
Powerful, granular observability into LLMs
Honeycomb shows you exactly what AI agents are doing, helps pinpoint issues, and improves the quality of AI-powered applications. Understand system behavior as a whole, identify performance bottlenecks, and optimize your AI agents with real-world data.
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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.
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