Monitoring AI Agent Behavior in Live Chats
Reading chat transcripts isn't monitoring. Discover how to implement true behavioral analysis and observability for your production AI agents.
The Limits of Traditional Log Monitoring
Most engineering teams approach AI observability the same way they approach server monitoring: they look at latency, error rates, and raw text logs. But an AI agent can return a 200 OK status code while simultaneously destroying your brand trust with a hallucinated, off-tone response.
Assay shifts the paradigm from systems monitoring to behavioral monitoring. By running continuous evaluations against your specific Brand Canon, Assay acts as a definitive quality assurance layer for every live chat.
The Behavioral Observability Framework
Move beyond simple uptime metrics and basic CSAT scores. True observability requires analyzing the <em>behavior</em> of the agent across thousands of interactions.
Automated Rubric Scoring
Every live chat should be automatically scored against a deterministic rubric covering brand voice, accuracy, and safety.
Quiet Regression Alerts
If the underlying LLM provider updates their model, your agent's behavior might change invisibly. Your observability tool must catch this instantly.
Negative Space Guardrails
It's not just about what the agent says; it's about what it shouldn't say. Monitor for breaches into restricted topics or competitor mentions.
Cross-Platform Benchmarking
If you switch from OpenAI to Anthropic, how does the behavior change? True observability allows you to benchmark models against the same rubric.
Implement these checks automatically.
Don't build this observability pipeline from scratch. Assay provides out-of-the-box behavioral monitoring and rubric scoring for any AI agent.
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