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To debug a multi-agent AI system, trace each run from its initiating request through the orchestrator, agents, tools, and external services, and make every handoff carry enough evidence to identify who passed what to whom, under which permissions, and with what result. Then compare that trace with latency, token and cost metrics, errors, and quality or safety evaluations. A trace can show where behavior diverged; it cannot by itself prove a model’s internal reasoning or that the diagnosis is correct.
What makes a multi-agent handoff diagnosable?
A handoff is a boundary where work, context, or authority moves from one component to another: for example, an orchestrator assigning a task to an agent, or an agent invoking a tool. If the receiving component’s activity cannot be correlated with the work that prompted it, an engineer may see isolated events without being able to reconstruct the run.
Treat one user request or workflow as a single correlated execution, even when it crosses processes or services. Propagate trace context through the orchestrator, each agent, tool calls, and external services. Represent operations as spans with parent-child relationships so a trace view can show their order and where time was spent. Microsoft’s architecture guidance describes using trace and span IDs to follow a request’s path and locate latency spikes, network bottlenecks, and coordination failures.
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What should you record for each run and handoff?
Define a data contract for telemetry rather than assuming a framework will emit every field your incident review needs. Microsoft’s observability guidance recommends capturing request identity, timestamps, run identifiers, user inputs and system responses, retrieval provenance, and tool invocation details. A practical contract can include:
- Run identity and timing: a stable request, conversation, or run identifier; timestamps; and trace and span identifiers that connect the event to its parent operation.
- Handoff participants: the sending and receiving agent identities, plus the orchestrator or service responsible for routing the task.
- Task and context: the handoff’s purpose and references to the relevant input and output. Record enough context to reconstruct what moved, subject to your privacy and retention rules.
- Retrieval provenance: which retrieved sources informed the work, represented in a way that lets an incident reviewer identify the evidence used.
- Tool activity: tool name, arguments or a protected reference to them, the permission or authorization context, and the returned result or error.
- Outcome and status: whether the operation completed, failed, timed out, or handed work onward, with an error or status detail where available.
Keep these fields tied to the relevant spans. For example, a tool invocation should be a child operation of the agent action that initiated it, while the agent’s subsequent response should remain in the same run. That relationship helps distinguish a slow tool from an agent that spent time before calling it.
How do traces, metrics, and evaluations work together?
They answer different operational questions. Traces show the path a particular run took. Metrics reveal patterns across runs and time. Quality and safety evaluations assess whether outcomes met the expected standard. Microsoft’s guidance and AutoGen’s tracing documentation support combining these views rather than treating a trace as a complete operating picture.
- Traces: locate the specific agent, handoff, retrieval step, or tool call associated with a symptom.
- System and model metrics: track latency, throughput, cost, token usage, and tool-call volume so teams can identify regressions or unusual patterns.
- Evaluation and policy records: assess output quality and safety, and record relevant policy decisions. Behavioral baselines and alerts help surface changes that deserve investigation.
A workflow that returns a plausible final answer can still contain a repeated tool call, unnecessary latency, a failed policy check, or an unhelpful intermediate handoff. Conversely, a slow span does not alone establish that the model or agent caused the delay; a downstream service may be responsible. Use the trace to locate the path and the other signals to test what kind of problem occurred.
How do you investigate a wrong answer, repeated call, missing handoff, or delay?
The steps below are a practical diagnostic method, not a standardized root-cause protocol issued by a single authority.
- Start from the symptom. Note what was wrong or unexpected, along with the approximate time and the affected request or workflow. Preserve the run or correlation identifier if it is available.
- Find the originating run. Open the trace for that identifier and follow parent and child spans through the orchestrator, agents, tools, and external services. Microsoft’s architecture guidance describes this use of trace and span relationships to locate where a request traveled and where latency accumulated.
- Inspect the handoff evidence. Check who sent the work, the task and context passed, retrieved-source provenance, the tool action and its authorization, and the result returned. Compare those details with what the receiving agent needed to do.
- Check for missing telemetry before assigning cause. Confirm that the relevant operations are instrumented and that content-capture settings, semantic-convention configuration, and tool or graph-node setup allow the spans you expect to appear.
- Correlate with other signals. Compare span duration and errors with latency, token and cost measures, tool-call volume, and applicable quality, safety, or policy results. This can help separate coordination problems from tool or service failures, missing evidence, and output-quality issues.
- Record the diagnosis and close the gap. Update the instrumentation, data contract, alert, or evaluation baseline that would make a similar incident easier to investigate. Limit access to sensitive trace content to what the incident requires.
How can you tell whether a trace is incomplete?
A trace viewer can display a run while still omitting important operations or content. Microsoft Foundry’s LangChain and LangGraph setup guidance lists disabled message-content capture, missing GenAI semantic-convention opt-in, and uninstrumented operations among possible causes of incomplete spans. It also identifies missing tool binding or a missing graph tool node as possible explanations for absent tool spans. Custom operations may need manual OpenTelemetry spans.
Validate the instrumentation with a known end-to-end run that includes an agent handoff, a tool call, and a retrieval step. Check that the expected spans appear and connect to the same run, and verify that the content you intend to capture is actually available under your configured privacy policy. If an operation is absent, treat that as a telemetry gap until you have evidence that the operation did not occur.
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Framework examples can make the setup more concrete, but they do not establish a universal best choice. Verify configuration against the documentation and versions of the framework and dependencies you have installed.
AutoGen
AutoGen’s stable documentation describes built-in OpenTelemetry tracing for agents and tools, and gives Jaeger and Zipkin as compatible backend examples. It also documents configuring a tracer provider and exporter. Check the installed AutoGen version and dependency requirements before adopting setup instructions.
LangChain and LangGraph
Microsoft Foundry documentation describes an OpenTelemetry distribution setup for tracing LangChain and LangGraph operations. The guidance includes setup and troubleshooting checks, and states that the integration is currently Python-only. Confirm the current requirements and enabled capture settings for your deployment.
Compare the operational fit, not just the trace viewer
When evaluating frameworks or backends, assess the features that affect your own workflow:
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- Trace-context propagation across process and service boundaries.
- Controls for capturing content, including sensitive inputs and tool arguments.
- Privacy, retention, data residency, and access-control requirements.
- How engineers query traces, connect them to alerts, and investigate incidents.
- Export and interoperability with the rest of your telemetry pipeline.
- Operational overhead and cost for the volume and retention period you need.
The cited framework examples do not provide an independent vendor benchmark or demonstrate that one provider is best.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you protect observability data?
Inputs, responses, retrieved material, and tool arguments can contain sensitive information. More captured content may help reconstruct an incident, but it also increases what must be protected and retained. Microsoft recommends using clear data contracts to balance forensic needs with privacy, data minimization, data residency, retention requirements, and legal or regulatory obligations.
Decide deliberately what content is stored, which roles can access it, and how long it remains available. Apply access controls and encryption in line with enterprise policy. If full content is not necessary for routine analysis, consider whether a reference or a more limited representation can meet the diagnostic need while reducing exposure.
What do research tools establish—and what do they not?
Research systems offer ways to examine agent trajectories, but their findings should be read within the experiments described by their authors. The EMNLP 2025 AgentDiagnose paper reports a mean Pearson correlation of 0.57 between its automatic metrics and human judgments across 30 manually annotated trajectories, and a correlation of 0.78 for task decomposition. These are results for that study’s metrics and annotated sample, not a general measure of how reliably observability diagnoses multi-agent systems.
The same paper reports a 0.98 improvement in WebArena success rates in a specified experiment: trajectories filtered from the 46k-example NNetNav-Live dataset were used to fine-tune on the top 6k trajectories. That experimental result should not be read as a general-purpose uplift or converted into a percentage claim without a supported definition of the metric.
The authors of the AgentGraph paper at AAAI describe converting execution traces into interpretable graphs and actionable insights. That framing is research context, not proof that a graph-based approach will resolve every operational incident. Neither paper establishes an industry-wide rate for multi-agent handoff failures.
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