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When an LLM workflow fails, trace the run to find the earliest point where it diverged from the intended behavior, then change the smallest responsible component and compare the result on repeatable cases. More agents are not automatically better: use model autonomy where a task needs flexible judgment, and use application code for transitions that are stable and predictable.
First, distinguish a workflow from an agent
A workflow coordinates models and tools through predefined code paths. An agent can dynamically decide how to proceed and which tools to use. Many real systems combine both: code may handle stable routing and limits while a model chooses among tools for an ambiguous task. The useful debugging question is not whether a system is “agentic,” but which decisions genuinely require model judgment.
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Anthropic’s Building Effective Agents, published December 19, 2024, recommends starting with the simplest approach likely to work and adding complexity only when needed. Treat that as an architecture principle, not a claim that agents are always unnecessary: autonomy and coordination can help with some tasks, but they can also add latency, cost, and failure points.
Debug the run before redesigning the system
Write down what the system is supposed to do, then compare that expectation with an inspectable execution trace. In the OpenAI Agents SDK, tracing is enabled by default and can record model generations, tool calls, handoffs, guardrails, and custom application events; the documentation was accessed October 5, 2026. Tracing is unavailable for organizations using OpenAI APIs under a Zero Data Retention policy. Check the current Agents SDK tracing documentation for implementation details, which can change.
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- Specify intended behavior. Record the inputs, acceptable outcomes, allowed tools and actions, stopping conditions, and points where control must return to a person. Separate hard requirements from decisions the model may make.
- Map the path the application actually runs. List each model call, tool, route, handoff, guardrail, retry, state update, and exit condition. Compare that map with the design the team believes it implemented; mismatches between the two are often diagnostic.
- Capture representative traces. Include an ordinary success, a known failure, and a difficult edge case. Inspect prompts, model outputs, tool results, and control-flow events only where policy permits.
- Find the earliest consequential divergence. Identify the first event that steers the run away from the expected outcome. Check whether it is a model output, tool choice or result, handoff, guardrail, state update, retry, or code transition. A downstream error may be only a symptom of an earlier defect.
- Make one local change. Change the component implicated by the trace rather than replacing the whole architecture. Examples include clarifying a tool’s name or schema, fixing a state transition, removing a redundant model call, or making a stable route deterministic in code.
- Replay and compare. Run the same cases against the old and changed versions. Keep enough instrumentation to explain failures that appear later.
Keep trace data safe
Traces may contain prompts, responses, tool inputs, or other sensitive payloads. The Agents SDK documentation says redaction and the destination for exported traces are application-owned; its example is not a universal ingest schema. Apply access and redaction controls suitable for the application before exporting data, and follow provider-specific restrictions. This is an operational safeguard, not a substitute for a broader privacy or legal assessment.
Diagnose common failure patterns
| What the trace shows | Where to investigate | Smallest useful test |
|---|---|---|
| The model repeatedly chooses a tool that cannot resolve the request | Tool descriptions, input schemas, overlapping capabilities, and instructions about when to use each tool | Clarify the tool’s purpose or reduce ambiguity, then replay cases that previously triggered the wrong choice |
| A tool is selected correctly, but the run fails after it returns | Tool result quality, error handling, state updates, and how the next model call receives the result | Check whether the returned value is valid and whether the application passes it along in the expected form |
| The same work happens repeatedly | Retry rules, exit conditions, state persistence, and whether the model can tell that the task is complete | Inspect successive events for an unchanged state or repeated tool call; add or correct a stopping condition where appropriate |
| A specialist returns useful work, but the user-facing answer is incomplete | Whether the manager receives, interprets, and synthesizes the specialist’s result | Follow the result through the manager’s next generation and check which information is lost |
| A run takes an unexpected route despite clear requirements | Whether a stable transition was left to a model decision, or a guardrail or route changed the path | Compare the intended path with the event sequence and move only the stable decision into code if the trace supports it |
Do not assume a loop is caused by the model alone. A repeated call can also come from retry logic, unchanged application state, a tool returning an unhandled error, or a missing exit condition. The event sequence helps distinguish these cases.
Decide whether the workflow is over-engineered
Complexity is a diagnosis to test, not a synonym for multi-agent design. An extra agent, handoff, model call, or dynamic routing decision is a candidate for removal when it adds a failure point or maintenance burden without contributing to an observed requirement. Conversely, traces may show that a specialist boundary or flexible plan is doing necessary work.
OpenAI’s practical guide to building agents recommends beginning with one agent and expanding its tools and instructions incrementally. It notes that a single agent can handle many tasks while keeping evaluation and maintenance simpler. If one agent is failing, first test whether clearer tool names, schemas, or instructions address the problem. Consider splitting when complex conditional instructions or overlapping tools are contributing to failures; multiple agents can clarify responsibilities, but they add coordination overhead.
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Choose control flow to match the task
| Situation | Good starting point | Question to resolve |
|---|---|---|
| A well-defined sequence has stable transitions | Code-driven workflow | Does the model need to choose the next step, or can application logic decide predictably? |
| The task is open-ended and needs flexible planning | Model-directed agent with bounded tools and stopping criteria | Can the model’s choices be constrained enough to meet safety and completion requirements? |
| One agent can meet requirements with clearer tools and instructions | Single agent with tools | Are observed failures caused by tool ambiguity that better descriptions or schemas can resolve? |
| One component must synthesize specialist work and own the final response | Manager calling specialists as tools | Must a central agent remain responsible for combining results and answering the user? |
| Routing should transfer control so a specialist owns the rest of the turn | Handoff | Is a change of control ownership part of the intended behavior? |
| Failures recur in one identifiable branch | Local refactor of that branch | Can the smallest implicated component be changed without disturbing working paths? |
The OpenAI Agents SDK documentation distinguishes a manager that calls specialists as tools from a handoff that transfers control to a specialist. It also describes code orchestration as a way to make outcomes more predictable in speed, cost, and performance. These are different control-flow choices, not a universal ranking; see the current Agents SDK orchestration guide for implementation details.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prove that a refactor helped
A cleaner diagram or a successful demo is not enough to establish that a refactor improved the workflow. When success can be specified, use a repeatable dataset and graders or other explicit criteria to compare versions. OpenAI’s agent workflow evaluation guide describes using evaluations to assess changes and detect regressions.
- Use the same representative cases. Include ordinary requests, known failures, and edge cases that exercise the affected branch.
- Define what counts as success before comparing. Check required content or actions, correct tool use, stopping behavior, and any constraints that must never be violated.
- Compare failure modes, not just pass or fail. Note whether the changed version introduces a new error, shifts the failure earlier or later, or changes which requests it handles poorly.
- Include operational effects that matter. Compare latency, cost, and coordination or maintenance burden where relevant, alongside task outcomes.
- Keep the original cases available for future changes. A repeatable baseline makes it possible to catch regressions rather than relying on a memory of earlier behavior.
There is no universal agent count or single metric that proves an architecture is better. The relevant trade-offs include predictability, handling of ambiguity, coordination and maintenance work, latency and cost, observability and replay, state recovery, tool clarity, and trace-data handling. Choose criteria that reflect the workflow’s requirements, and keep the instrumentation needed to explain later failures.
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