Ai Agents Troubleshooting Guide
AI agent troubleshooting in 2026 requires logging every step of the agent loop: the prompt sent to the LLM, the LLM's response (including any tool calls), the tool call inputs and outputs, and the final response. Without this logging, debugging is guesswork.
Why This Happens
- Configuration gaps between tools or services
- Missing integrations or manual workarounds that weren't designed to scale
- Changes in vendor behavior, pricing, or API that weren't communicated clearly
What To Check First
- Verify your current setup matches the vendor's latest documentation
- Look for recent changes — platform updates, new team members, configuration drift
- Check if the problem is consistent or intermittent (different root causes, different fixes)
When To Escalate
- The problem is costing you money or customers per week
- You've spent more than 2 hours on it without progress
- A vendor quoted you more than $500 and you're not sure if it's necessary
Dealing with this right now?
The diagnostic path: (1) Is the LLM responding at all? Test with a direct API call outside the agent framework. (2) Is the LLM calling tools correctly? Log the raw API response and check for `tool_use` content blocks — if the model is not calling tools, the tool descriptions or system prompt need work. (3) Are tool calls returning useful data? Log tool inputs and outputs separately — a tool might execute correctly but return empty or malformed data. (4) Is the agent looping correctly? Add step count logging to catch infinite loops early. (5) Is the final response being generated? Check whether the agent terminates after a fixed number of steps or only when the model returns a non-tool response.
