AI agent tracing

Trace AI agent runs from prompt to tool side effect.

Opswald turns multi-step agent runs into inspectable traces that show what context the agent had, which tools it called, what changed, and where the first unsupported decision appeared.

By Opswald Team, AI agent tracing and debugging specialists • Last updated May 18, 2026

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agent-run.trace

01Prompt + retrieved context captured

02Planner chose tool with incomplete state

03Tool output contradicted the next decision

04Replay pins the first divergent step

Direct answer

What is AI agent tracing?

AI agent tracing is the practice of recording each decision boundary in an agent run: the prompt, instructions, retrieved context, memory, model output, tool call, retry, error, and side effect. A useful trace connects those events into causality so engineers can identify the first unsupported decision instead of only seeing latency or errors.

4 decision boundaries every production agent trace should preserve input, context, model decision, and external effect

7+ evidence types needed to debug tool-using agent runs prompt, context, model response, tool schema, arguments, output, retries, and writes

1st divergent decision to locate before patching a production incident Opswald tracing workflow

OpenTelemetry traces Trace and span model used to reconstruct distributed work across services. OpenTelemetry GenAI conventions Semantic conventions for modeling generative AI operations in telemetry. OpenAI Agents SDK tracing Agent tracing concepts for LLM generations, tool calls, handoffs, guardrails, and workflows.

What breaks

Agent failures are rarely a single stack trace.

They happen across prompts, memory, retrieved documents, tool schemas, model choices, retries, and side effects. Opswald is built to make that chain inspectable instead of asking engineers to reconstruct it from logs.

Logs miss the reasoning path

Application logs usually show requests and errors, not the prompt, retrieved context, tool arguments, model outputs, retries, and branch decisions that caused the behavior.

Tool calls cross service boundaries

A single run may touch MCP servers, internal APIs, vector databases, queues, and customer state. Tracing needs to preserve the whole chain.

Retries rewrite the story

Fallbacks and retries can make the final response look correct while hiding the step where state drift or bad context entered the run.

Provider dashboards are incomplete

Model provider logs rarely include your orchestration code, tool outputs, permissions, and side effects in one root-cause view.

What to capture in an agent trace

A useful AI agent trace preserves evidence at every decision boundary, not just spans and latency.

InputCapture the user request, system instructions, run metadata, permissions, and active workflow state.
ContextStore retrieved documents, memory reads, cache hits, ranking scores, and the exact context passed to the model.
DecisionRecord model outputs, planner decisions, tool choices, validation failures, retries, and fallback branches.
EffectAttach tool arguments, tool outputs, errors, writes, webhooks, and external side effects to the decision that caused them.

agent-trace.json

run_id: refund_agent_0187
context: policy_v4 + customer_state + prior_ticket
decision: selected refund_tool with stale status
tool_output: partial customer record
side_effect: duplicate refund queued
root_cause: missing idempotency guard

Practical debugging

AI agent tracing questions Opswald answers

Why did the agent choose this tool?

See the context and intermediate output that made the tool call look reasonable at the time.

Which step first diverged?

Compare good and bad runs by prompt, context, decision, tool result, and state mutation.

Can we reproduce it?

Turn traces into replayable fixtures with pinned context and safe tool stubs.

Comparison

Opswald vs traditional observability for AI agents

Capability Traditional logs and APM Opswald

Causality Logs and APM show events, errors, and timings, but rarely explain why the agent chose a branch or tool. Connects prompts, context, model outputs, tool calls, and side effects into one decision graph.

Agent evidence Provider dashboards may stop at model calls, while application traces may omit prompts and tool outputs. Keeps model decisions, retrieval, tool schemas, arguments, outputs, retries, and writes attached to the run.

Debugging next step Teams still need manual log archaeology to decide what to reproduce or test. Turns the traced failure into a replay candidate with pinned evidence and safe tool stubs.

Keep reading

Related Opswald guides

AI agent debuggingMove from trace evidence to a practical root-cause workflow.AI agent replayReplay failed traces with pinned context and tool outputs.Debug tool calling failuresInspect the tool arguments, outputs, and side effects behind failures.Why logs are not enoughLearn why agent traces need richer evidence than request logs.

FAQ

Questions teams ask before instrumenting agents

Is AI agent tracing just OpenTelemetry?

OpenTelemetry is useful plumbing. Agent tracing also needs prompts, context, tool schemas, model decisions, retries, and replay state attached to spans.

What should teams trace first?

Start with prompts, retrieved context, model outputs, tool arguments, tool outputs, errors, retries, and writes to customer state.

How is agent tracing different from request tracing?

Request tracing explains service calls and latency. Agent tracing explains the decision chain that selected context, tools, retries, and side effects inside the request.

Which tool-call fields belong in an agent trace?

Capture the available tool schema, selected tool, arguments, validation result, output, error, retry metadata, permission scope, and any external mutation.

How does tracing support replay?

A trace preserves the evidence needed to replay the failed path later: inputs, context, decisions, tool outputs, state, errors, and side-effect receipts.

Debug the next failed agent run with evidence.

Opswald is in early access for teams shipping AI agents that call tools, use MCP servers, or run multi-step workflows in production.

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