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Research and analysis
6 articles in this topic.
AI agents are harder to monitor than chatbots for one simple reason: there is much more happening between the user's request and the final answer. A chatbot might receive a question, call an LLM, and return a response. An agent can interpret the task, retrieve information, choose a tool, call an API, inspect the result, …
Choosing an LLM observability platform is not just a matter of comparing dashboards. One vendor may give you detailed traces. Another may be stronger at evaluations. A third may have better integrations with your existing stack. The problem appears when the demo looks good but the platform cannot answer the questions your engineering, security, procurement, …
Your AI application may be running inside your own cloud account, but that doesn't necessarily mean your observability data is. A trace can contain much more than latency and token counts. Depending on how the application is instrumented, it may include prompts, model responses, retrieved documents, tool calls, user identifiers, metadata and other details from …
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