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Use AI to generate and test debugging hypotheses—not to declare a root cause. In a complex system, start with the failing request or workflow, follow its trace across services and operations, correlate the trace with logs and metrics, and verify any proposed fix with a reproducible check. For AI agents, include model, tool, and retrieval steps in that execution trail.
Why complex failures need more than a code explanation
A failure that crosses service boundaries can be hard to reproduce locally. The visible symptom may occur in one component while the cause lies in a downstream call, a delayed response, a missing step, or an interaction among services. A code assistant can suggest plausible explanations, but its explanation is not runtime evidence. The investigation must connect what the system actually did to the behavior that failed.
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OpenTelemetry describes distributed tracing this way: “Distributed tracing lets you observe requests as they propagate through complex, distributed systems.” A trace follows one request through its spans; spans represent work and their parent-child relationships. Logs provide timestamped messages, while metrics summarize system behavior. Together, these signals help move from a symptom to a particular service or operation and then inspect its surrounding context.
OpenTelemetry is a vendor-neutral framework for instrumenting, generating, collecting, and exporting traces, metrics, and logs. Its documentation index, modified August 29, 2025, states that the project is supported by more than 90 observability vendors. That is OpenTelemetry’s dated figure, not an independently verified count of the current market.
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How do I debug a problem that appears across multiple services?
- Define the failure and its boundary. Record the observed behavior, expected result, affected request or workflow, time window, and relevant deployment or configuration context. Keep the investigation anchored to the symptom rather than beginning with an AI-generated diagnosis.
- Find the relevant trace. Follow the request through its spans and identify the first unusual error, delay, or missing step. Use the parent-child relationships to see which downstream operation is associated with the behavior. A trace shows the path represented by the captured spans; it does not by itself prove why an operation behaved that way.
- Correlate logs and metrics. Inspect logs for the implicated service and time range. Compare relevant metrics to determine whether the problem appears isolated or systemic. Use timestamps and available request or trace context to keep the signals aligned.
- Form competing explanations. Ask what evidence would distinguish the likely causes. For example, a slow request might reflect a delayed downstream operation, but the trace, service logs, and relevant metrics should support or rule out that possibility before you change code.
- Test the leading hypothesis. Reproduce the failure if possible, add a focused test or diagnostic, or inspect runtime behavior with an interactive debugger. Debug2Fix describes interactive debugging as complementary to static code analysis, not a replacement for it.
- Verify the change. Check the specific failure condition and adjacent behavior after the change. Record the relevant trace identifiers, hypothesis, check, and outcome so another engineer can follow the reasoning.
Can AI find the root cause from logs and traces?
AI can help inspect bounded evidence, organize possible explanations, and suggest checks. It cannot establish a root cause merely by producing a convincing narrative. No general success rate or quantitative proof that AI is more accurate or faster across complex production systems is established by the sources covered here.
Give an assistant only the code and telemetry relevant to the failure, with sensitive values removed or minimized. Ask it to:
- List multiple explanations consistent with the evidence rather than choosing one without qualification.
- Separate observed facts from assumptions and identify missing context.
- Point to the specific span, log entry, metric, or code path behind each explanation.
- Propose a concrete check that could distinguish competing explanations.
- Suggest a focused test or diagnostic, then wait for observed results before treating a hypothesis as supported.
Keep the evidence trail intact: compare suggestions with the actual execution path, run the proposed check, and update the diagnosis when the result disagrees. An assistant that cannot see relevant runtime evidence may still help reason about code, but it cannot infer unrecorded execution details reliably.
How do I debug an AI agent’s tool calls?
Instrument the orchestration path so a trace can show the sequence of model calls, tool operations, and retrieval steps. OpenTelemetry’s GenAI conventions describe capturing model identity and token counts; prompt and completion content and tool calls or results can also be captured when explicitly enabled. These details let an engineer compare an explanation of the agent’s behavior with the recorded execution path.
Google Cloud’s agent documentation identifies failed API requests, execution loops, and latency bottlenecks as problems that traces can help diagnose. For an agent workflow, inspect where execution diverged from the expected path: for example, whether a tool request failed, a repeated step occurred, or a retrieval operation added delay. Follow the trace and relevant service logs rather than assuming the model’s final response describes every internal step accurately.
Choose instrumentation that captures the missing context
Start with automatic instrumentation where it fits
Zero-code instrumentation can be a useful first pass where the language and libraries are supported. OpenTelemetry describes agent-like installation methods that inject instrumentation and capture common library operations, including requests, database calls, and message-queue calls, without source edits. Coverage and mechanisms vary by language; automatic instrumentation generally does not expose application-specific logic.
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Add code-level instrumentation for application decisions
When a captured library span shows that work occurred but not why the application chose it, add instrumentation around the relevant domain decision or internal transition. For example, capture the point at which the application selects a workflow branch if that choice is necessary to explain the failure. Avoid collecting more detail than the investigation needs.
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Documentation does not establish a universal best platform. Compare options using the characteristics that determine whether evidence will be useful in your system:
| Criterion | What to check |
|---|---|
| Coverage | Supported languages, frameworks, services, databases, queues, and agent components. |
| Context continuity | Whether request or trace context links work across service and tool boundaries. |
| Signal correlation | Whether engineers can move between traces, related logs, and metrics. |
| Instrumentation depth | Whether automatic library spans are sufficient, or application-specific decisions can also be captured. |
| Privacy controls | Defaults for prompt and tool content, selective capture, redaction, access, and retention. |
| Debugging interaction | Whether developers can inspect live or recorded runtime state as well as static code. |
| Portability and maturity | Whether telemetry formats are standard and the conventions or integrations are stable for the chosen stack. |
These are evaluation criteria, not a product ranking. The available sources do not provide an independent head-to-head test.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect sensitive data in AI telemetry
Telemetry that includes prompts or tool data can make an agent failure easier to diagnose, but it can also expose sensitive information. In the 2026 OpenTelemetry walkthrough’s Copilot example, prompt-content capture is disabled by default; opting in can place prompts, system instructions, tool schemas, arguments, and results in telemetry attributes. The details are specific to that example, so check the current documentation for the tool and configuration you use.
- Decide which fields are necessary to investigate the failure, and omit or redact content that is not.
- Limit who can access telemetry containing prompts, arguments, results, or other sensitive values.
- Set a retention period appropriate to the diagnostic need and your data-handling requirements.
- Check the capture defaults before enabling content collection, and verify what is actually recorded.
Telemetry fields can be large as well as sensitive. Treat prompt and tool-content capture as a deliberate operational choice, not a prerequisite for tracing the workflow.
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What the evidence can—and cannot—show
Telemetry-guided investigation and interactive runtime debugging are supported approaches to diagnosing failures. The available sources do not establish a general productivity, accuracy, cost, or resolution-time advantage for AI-assisted debugging across complex systems, nor do they identify a winning vendor. Measure results in your own environment if you need to determine whether an AI workflow improves your team’s debugging outcomes.
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