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When an agent build goes wrong, the failure you see first is often not the real one. The final message can read as success while the system state is wrong, or the run can reach its goal through a path you never meant to allow. The patterns below apply to any agent that calls tools across several steps, whatever task it performs. To find the real break, read the execution trace, compare it with the resulting state, and make the failing case reproducible before you call anything fixed.
Judge the run by its end state, not its final message
A final answer is a claim about the work, not evidence of it. Anthropic’s January 9, 2026 engineering article on agent evaluation defines a transcript, also called a trace or trajectory, as “the complete record of a trial, including outputs, tool calls, reasoning, intermediate results, and any other interactions.” Keeping that full record matters because an agent can describe a successful outcome while the record it was supposed to change is untouched, or was changed in a way nobody asked for.
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Anthropic’s article also shows that a verdict can mislead in both directions. In a flight-booking task, Opus 4.5 found a policy loophole that produced a better outcome for the user, and the evaluation as written scored that run as a failure. Your grader therefore has to test the intended outcome and the policy the run must respect, not only the shape of the answer.
What a usable trace must contain
OpenAI’s evaluation guide describes a trace as “the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.” A trace is only useful if it lets you reconstruct each decision. Capture at least the following for every run:
#1 Best Overall
- The exact input, and the system or developer instructions in effect at each model call.
- Each model response, including the tool call it selected.
- The tool’s name, its description and schema as they existed at the time of the call, and the arguments the model produced.
- The raw tool response, including errors, and what the agent did with it next.
- Every handoff to another agent, and the state passed along with it.
- Guardrail decisions, any approvals, and the stopping condition that ended the run.
- The final environment state: the records, files, or external systems the task was meant to change.
Redact credentials and personal data before you share a trace, and keep the unredacted original in your own storage.
Where agent builds break
Several distinct failure layers account for most breaks visible in traces. Each leaves different evidence, so classify the failure before you change code.
Wrong or confusing tool use
Partnership on AI’s report on agents notes that a model can misuse a tool, or select one that does not match the user’s intent, when interfaces are vague or when tool descriptions overlap. Two tools with similar names and overlapping descriptions can produce a call that is plausible, executes without error, and still does the wrong thing. Check the selected action against the description and schema the model actually saw, not against the description you meant to write.
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Loops, limits, and runtime failures
OpenAI’s runtime documentation treats max-turn limits, guardrail exceptions, and tool errors as separate failure classes. The runner keeps calling the model and tools until it reaches a stopping point, so a build can spend its turns on repeated calls without making progress. At the stop, check whether the run returned a partial result, timed out, or presented an incomplete task as finished. The last case is the most damaging because it looks like success to the user and to any downstream code that checks only for a response.
Record the stopping reason as its own field. “Maximum turns reached” and “the model indicated it was finished” should never share a status value.
State continuation and duplicate context
OpenAI documents several ways to carry state from one turn to the next, and advises choosing one strategy per conversation in most applications. Trouble starts when local replay of past messages and server-managed state are both active. The same earlier turns can then reach the model twice, or a resumed run can start from state that no longer matches the environment. If your build resumes after a pause or retry, compare what was persisted, what was replayed, and what the model saw on its first resumed turn.
Unsafe instructions and unintended actions
OpenAI describes prompt injection as malicious content in untrusted text or data that tries to override the agent’s instructions. Its safety guidance also names private-data leakage and unintended actions caused by hallucination, misunderstanding, or ambiguous input. The controls it recommends are clear policy prompts with examples, structured outputs, approvals for tool calls, input guardrails, and trace graders or evals. These reduce risk; none of them is a guarantee. Test them with inputs written to subvert the agent, not only with ordinary requests.
In a trace, the warning sign is a tool call whose arguments contain text copied from a web page, document, or message the agent read earlier, or an action the user never requested. Any tool call that writes, sends, or deletes data deserves the strictest gate in your design.
Rank #4
Evaluation false positives and false negatives
An agent can look broken because the test is wrong. Anthropic’s January 9, 2026 engineering article reports that Opus 4.5 initially scored 42% on CORE-Bench. Before the team identified the problems, the grading rejected “96.12” when the expected answer was “96.124991…”, some task specifications were ambiguous, and some stochastic tasks could not be reproduced exactly. That 42% is Anthropic’s own account of its evaluation, not an independently verified leaderboard comparison.
Three checks prevent most grader errors:
- Write success as an outcome condition, meaning the state that must exist afterward, plus any policy the run must respect.
- When the task allows equivalent answers, state the tolerance explicitly, such as the number of decimal places that count as a match.
- Run stochastic tasks several times and report the spread of results rather than a single pass or fail.
Multi-agent coordination
Anthropic’s June 13, 2025 engineering article on multi-agent systems states that “systems with multiple agents introduce new challenges in agent coordination, evaluation, and reliability.” A second agent adds handoffs, shared state, and new places where information can be lost or duplicated. Do not assume a multi-agent design is more reliable than a single agent until your traces show the handoffs working. Label every handoff with the state that moved across it and the state that was dropped.
Start from the symptom
Use this table to choose the layer to investigate first. It points to the evidence to check, not a verdict.
Best Value
| Symptom | Likely layer | First thing to check |
|---|---|---|
| The final message says the task is done, but the environment state is unchanged | Evaluator/grader or state handling | Compare the final state with the success condition, and confirm the check reads the state rather than the message |
| The action succeeded but changed the wrong object or served the wrong intent | Tool selection or execution | The chosen tool, its description and schema, and the arguments the model produced |
| The run ends with no result or only a partial answer | Runtime or limits | The stopping reason and the turn count |
| A resumed run repeats earlier steps or contradicts the environment | State continuation | Persisted state compared with replayed messages on the first resumed turn |
| An action the user never requested, or untrusted text appears inside a tool argument | Unsafe instructions or unintended action | The tool-call arguments and the source text they came from |
| A correct-looking answer fails the test | Evaluator/grader | The grader’s matching rule and the expected value |
| The failure appears only when several agents are involved | Multi-agent coordination | The handoff records and the shared state at each boundary |
A postmortem that survives review
Use this order so that someone who did not run the build can check the write-up.
- State the task, the environment, and a success condition written as an outcome, including any policy the run must respect.
- Pick one failing run and export its full trace and final environment state.
- Classify the break as task specification, tool selection or execution, state handling, runtime or limits, safety, or evaluator/grader.
- Separate what the trace shows from what you suspect. Use “the trace shows” for direct evidence and “consistent with” for hypotheses.
- Make the smallest change that addresses the classified layer, then rerun the same case.
- Run nearby cases to catch regressions. For stochastic tasks, repeat the trials and report the spread.
- Report whether the environment outcome changed, not whether the final text looks better.
What published agent disclosures do and do not tell you
If you compare your build with published agents, check what each vendor discloses. The MIT AI Agent Index (2025) reports that 135 of 240 safety, evaluation, and social-impact fields had no information available. Among the 30 indexed agents, 25 disclosed no internal safety results, and 23 had no third-party testing information. These figures describe disclosure within that index sample. They do not measure whether any product is unsafe, and they do not predict how an agent will perform on your task.
Check your platform’s lifecycle before you rebuild
OpenAI’s safety documentation, as checked on October 7, 2026, states that Agent Builder is scheduled to shut down on November 30, 2026, and that ChatKit remains available. If your build depends on Agent Builder, confirm that date against OpenAI’s current announcements and plan how you will move your traces and tool definitions before it arrives.
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