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Getting an agent demo to run is different from being able to explain why it behaved as it did. To understand a run, follow the evidence through the whole loop: the input, the model’s proposed action, tool execution, the returned observation, the next model request, and the program’s decision to continue or stop.
That is the practical gap described by liuyuyan6100, who identifies as Aiclaw, in a DEV Community article posted September 28. The exercises below are proposed next steps, not experiments the author reports having completed. The goal is to make one execution path understandable and test a specific expectation—not to assume that a fluent answer or a “test passed” label proves the system worked.
What to inspect in an agent run
Start with a single run and locate each point where information changes hands. In the article’s simplified serial-gateway example, the path is:
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- The model proposes a tool name and arguments.
- The execution program sends an HTTP request to a gateway.
- The gateway uses pyserial to send a command to a device and read its reply.
- The tool result is included in a later model request.
- The model proposes another action or produces a final report, while the program decides whether to continue.
This is one example, not a universal agent architecture. Other systems may use different interfaces, tools, or hardware—or no physical hardware at all. The useful idea is to trace the boundaries, not assume a particular implementation.
Line up the task input and prior results with the requested tool name and arguments, the outgoing request, the gateway response, the next model input, and the final report. Then ask: where does the first discrepancy appear? A trace narrows the investigation; it does not automatically identify a root cause. If the only evidence for success is “test passed,” find the response or observation that supports that label.
How to find where a tool call went wrong
Debug at the boundary where the evidence first stops matching expectations. These are diagnostic clues, not guarantees that a symptom has only one cause.
- The command is wrong: inspect the model’s requested tool name and arguments, and check how the execution program turns them into a command.
- The request never reaches the gateway: inspect the execution environment, request construction, and interface between the program and gateway.
- The gateway returns no device reply: check the serial communication path and the gateway’s handling of the device response.
- The final report is incorrect: verify which result reached the model in its later request, then trace how the report’s conclusion follows—or fails to follow—from that result.
Keep the records sanitized. When possible, first replay a saved response or use a mock interface, especially before scheduling tests against real hardware.
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A reviewer’s response does not control a program by itself. The program’s stop condition, expected review format, parser, and iteration limit determine how feedback becomes a branch or another action. The observed sequence alone does not establish that there is a bug.
Predict the branches before running
Inspect the actual stopping condition, review format, parser, and iteration cap. Before running the program, write down what you expect it to do for three fixed inputs:
- Approval: does it stop and return the current result?
- A request for changes: does it revise, ask the model again, or take another defined action?
- Malformed or unrecognized feedback: does it stop safely, retry, or follow a fallback branch?
Then compare the observed branch with your prediction. This separates control-flow behavior from model variability: a changed model response may vary between runs, while a parser or exit condition can be tested against fixed feedback.
Test the generated function against its contract
Evaluate the function independently of whether a reviewer praises it. The DEV Community article uses prime generation through n as a starter example and gives these expected cases:
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These examples are not a complete test suite. Use them to begin checking whether the implementation satisfies the stated contract, and compare its behavior with the reviewer’s specific claim. A more positive review is not proof of improvement.
How to inspect graph state and retrieval failures
In a graph workflow, state can make the path through nodes easier to inspect, but fields passed between nodes do not by themselves establish persistent storage or long-term memory. For the example’s search_query, search_results, and step fields, map which nodes write each field and which nodes read it.
Compare three answer-stage inputs
Hold the question fixed and inspect what the answer node receives in each case:
- Search returns results: can key claims in the answer be traced to retrieved material?
- Search returns no results: does the answer distinguish “not found” from “does not exist”?
- Search returns an error: does the answer disclose that retrieval failed, or answer as if evidence had been obtained?
A convincing final answer does not prove retrieval worked. For questions that depend on current information, decide explicitly whether the workflow should answer after a retrieval failure or explain that it did not obtain evidence.
A repeatable way to learn from one execution
Make a prediction before asking an AI assistant to explain a run. Afterward, record four things: the expected behavior, the observed behavior, the difference, and the next boundary to investigate. That habit makes explanations testable instead of turning them into guesses.
The author proposes a progression of exercises, not completed results: inspect a model request; trace a read-only tool loop; add behavioral tests and explicit exit conditions to the Reflection example; rebuild a small workflow while mapping state and failure paths; and validate the serial gateway with saved replies or a mock interface before testing real hardware.
For each exercise, check whether inputs, tool arguments, outputs, and state are visible; whether stop conditions are explicit and testable; what happens with empty results, malformed feedback, or execution errors; whether a final claim can be tied to the observation supporting it; and whether the run can be reproduced from sanitized logs or mocks. The answers help determine whether you can explain the run—not merely make the demo work.
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