Debugging Agentic AI starts with treating it like a distributed system, not like a black-box model. When an agent fails, the question is rarely “why did the model say this?” and more often “which step in the loop went wrong?” Effective debugging requires full visibility into the agent’s state, decisions, tool calls, and retrieved context at every step.
A practical debugging approach is to log each iteration of the agent loop: the current goal, the reasoning summary, the chosen action, the tool inputs, the tool outputs, and the updated state. When an agent retrieves context from memory, such as prior incidents or documents stored in a vector database like Milvus or Zilliz Cloud, log exactly which records were retrieved and why. This makes it possible to see whether the agent failed because it retrieved the wrong context, misunderstood the result, or chose an inappropriate next step.
Beyond logging, replayability is critical. You should be able to replay a failed run with the same inputs and observe the same behavior. This allows you to refine prompts, tool schemas, or retrieval filters in a controlled way. Many teams also introduce “reasoning summaries” at each step so failures are easier to interpret without inspecting raw prompts. Debugging Agentic AI is less about fixing the model and more about tightening the system around it.