When AI agents move from demonstration to production, the first thing teams often encounter is not 'the model is not smart enough', but 'when there is a problem, it cannot be understood'. An Agent task will go through multiple stages such as intent understanding, tool selection, parameter generation, tool execution, and result judgment. Any error in any stage may cause the final result to deviate, while traditional logs can only see scattered request records.
To improve the observability of agents, it is recommended to start from four levels.
Firstly, record by "task" instead of "request". To generate a unique ID for a complete task, multiple model calls and tool calls must be linked together to answer the question of 'why did this task fail in the end and at which step did it fail'.
Secondly, record the complete context of the tool call, including which tool was called, what parameters were passed in, what results were returned, and whether there were any errors. The root cause of a large number of Agent accidents is that the tool returned abnormal data, but the model continued to execute it as a normal result.
Thirdly, incorporate costs and delays into monitoring indicators. Agent tasks can significantly increase token consumption, and a failed retry or poorly designed loop can result in several times the cost. Setting upper limits and alerts for the number of tokens and calls for a single task is a fundamental barrier in production environments.
Fourthly, sediment samples that can be replayed and evaluated. Organize online failure cases into a review set, and run regression testing after upgrading the model or adjusting the prompt words to avoid "fixing one and damaging another".
Observability is not a remedial measure after going live, but a prerequisite for the long-term stable maintenance of Agent applications. Without it, the team can only rely on "trying again" to get lucky when encountering problems.
[Reference source] Comprehensive compilation of publicly released industry information and official documents of mainstream Agent frameworks (Anthropic tool calling document, Model Context Protocol official website, etc.).