Observability & Evaluation · Software component

Trace Collector

Software componentObservability & EvaluationObservability & Evaluationarc:TraceCollector

A telemetry component that captures agent reasoning steps, tool invocations with parameters, latencies, errors, and retries as structured traces.

Responsibility. Captures spans of agent, tool, and LLM activity.

Also known as: Debug logging, Technical view data, Distributed tracing, Execution trace logger, Profiler, Reasoning trace collector, Verbose reasoning log, Profiling decorators, Instrumentation wrappers, Workflow/agent/tool-level instrumentation, Execution tracking, Tool call tracking, Reasoning trace collection, Production trace capture, Structured reasoning logger, OpenTelemetry distributed tracing of inference requests, AgentProfiler wrapper, Framework-agnostic agent instrumentation, Distributed tracing instrumentation, OpenTelemetry tracer, Span instrumentation

Direct neighbourhood (hover for relationship types)

Relationships

is configured by structural

writes dependency

emits telemetry to dynamic

Metrics Collector Ch3.6

receives data from dynamic

Trace Context Propagator Ch4.2 Ch6.5

receives telemetry from dynamic

Agent Controller abstract Ch1.1A Ch1.2 +9
Agent Message Bus abstract Ch1.3
Answer Synthesizer abstract Ch8.1
Confidence Estimator Ch10.5
Context Window Manager abstract Ch3.6
Continuous Integration Runner Ch4.2
Database Connector Ch8.1
Dialogue Flow Manager Ch10.1
Error Presenter Ch1.1A
Escalation Agent Ch10.5
Event-Triggered Agent Ch4.2
Function-Calling Controller Ch2.5
Guardrail Orchestrator Ch9.1 Ref7.03
Inference Server Ch4.5
Intent Router Ch10.1
LLM Inference Service Ch8.1 Ref8.01 +1
Multi-Agent Coordinator abstract Ch2.4
Parameter Provenance Validator Ch3.8
RAG Query Orchestrator Ch6.5
ReAct Agent Controller Ch2.3 Ch3.6 +2
Reasoning Engine Ch2.6 Ch3.6 +4
Response Streamer Ch2.9
Retriever abstract Ch2.9
Retry Handler Ch3.6
Service Mesh Proxy Ch4.3
Supervisor Agent Ch1.3
Tool Error Classifier Ch3.7
Tool Executor Ch1.1A Ch1.2 +7
Tool Protocol Client Ch7.3
Vector Retriever abstract Ch8.1
Web Navigation Agent Ch3.3
Worker Agent abstract Ch1.3 Ch10.5
Workflow Orchestrator abstract Ch1.5A Ch1.5B +1

sends data to dynamic

Design guidance

SHOULD log comprehensively so agent behaviour can be debugged when issues arise.
MUST propagate a correlation ID through every agent, message and event of a request.
SHOULD capture which nodes executed, which routing decisions occurred, and how state evolved at each node.
SHOULD collect full traces for high-stakes decisions and minimal logging for low-stakes ones.
SHOULD timestamp query receipt, retrieval start/end, LLM call, first token, and stream completion separately to locate TTFT bottlenecks.
SHOULD emit the same detailed action traces in production as in offline evaluation.
SHOULD supplement automatic instrumentation with manual spans carrying business metadata such as user_id, session_id, model_version and prompt_template_id.
MUST instrument at workflow, agent and tool levels to allow drill-down fault isolation.
SHOULD instrument transparently via decorators or wrappers rather than invasive code changes.
SHOULD be built in from the start of agent development rather than retrofitted after production deployment.
MUST log every tool invocation with tool name, all parameter values, start timestamp, latency, complete return value and any errors.
SHOULD use formats compatible with distributed tracing so tool invocations are correlated across multi-step and multi-agent workflows via trace IDs.
MUST log each reasoning step as a structured object (premises, conclusions, evidence, reasoning type) rather than unstructured text, using the same schema in development and production.
SHOULD automatically persist traces exhibiting anomaly indicators (very low confidence, user corrections, error conditions).
MUST instrument every service to emit spans with parent-child relationships so cross-service agent interactions can be debugged.
SHOULD also trace CI/CD pipeline runs with one span per stage to reveal where pipeline time is spent.
SHOULD trace per-request queue wait, inference execution, downstream blocking and serialization time to localise bottlenecks.
SHOULD wrap an existing agent without modifying its logic, intercepting tool calls, LLM invocations and state transitions.
SHOULD be framework-agnostic so one profiling methodology survives migration between agent frameworks.
MUST instrument both the whole request (parent span) and each processing step (child spans for retrieval, reasoning, database query, tool execution, response generation) so per-step latency can be attributed.
SHOULD attach semantic attributes (e.g., query length, query type) to spans so latency can be filtered and correlated with request characteristics.
SHOULD capture model calls, tool invocations, retriever operations, token counts, costs, errors with stack traces and custom tags per run (Ref8.01).
SHOULD systematically collect each query, response, user feedback, outcome, time taken, context and performance signals (latency, errors, confidence scores) for improvement analysis.
SHOULD capture confidence scores and policy-evaluation outcomes alongside reasoning steps and tool calls for each consequential decision.
SHOULD capture data sources accessed, decision rules applied, per-step confidence, tools invoked with parameters and outputs, policy checks, and alternatives considered and rejected.

Quantitative guidance

As stated by the sources; verify before use.

Sampled transparency collects full traces for 1-10% of requests (Ch1.8).
Example: a 15 s SEC-filing-analyser latency caused the synthesis coordinator to time out (Ch4.2).
Pipeline trace example: quality 90 s, unit 45 s, integration 4m20s, evaluation 8m15s, build 2m10s, scan 1m5s, staging 3m40s, canary 30 min (Ch4.2).
Example trace: 5ms queue wait, 45ms inference, 200ms waiting on downstream APIs, 10ms serialization (Ch4.5).
Traces retained 1 week hot and 30 days archive (Ref7.16).
Tracing 10,000 requests attributed a 1,300 ms mean to vector search 150 ms (12%), reasoning 300 ms (23%), database query 200 ms (15%), tool execution 400 ms (31%) and response generation 250 ms (19%); P50 1,100 / P95 3,200 / P99 7,500 ms (Ch8.1).
Tracing revealed 3-second database latencies for Erica spending analysis at peak load, fixed by adding read replicas (Ch10.1).

Classification

Patterns: Correlation IDsDistributed tracingSelective transparencySampled tracingConfidence-conditional tracingUser-triggered tracingTool-level and agent-level action tracingParameter provenance annotation in spansDecorator-based transparent instrumentationThree-layer instrumentation (workflow, agent, tool)Structured per-step reasoning loggingTriggered capture on anomaly indicatorsNested parent-child spans per processing step
Technologies: NVIDIA NeMo Agent ToolkitOpenTelemetryPhoenixWeights & Biases WeaveLangfuseAzure AI FoundryOpenTelemetry SDKWeaveJaegerZipkinTempoLangChain AgentExecutorCrewAILlamaIndexLangSmith
Quality attributes: Maintainability (ISO/IEC 25010)Transparency and accountability (NIST AI RMF: accountable and transparent)Flexibility (ISO/IEC 25010)
Risks mitigated: Undebuggable agent behaviourUndebuggable multi-agent black boxesUndiagnosable incorrect outputsUnidentified performance bottlenecksTrace storage cost explosionGuess-driven optimization of the wrong componentWeeks-long manual diagnosis via ad-hoc logging and cross-system timestamp correlation

Sources

Ch1.1A: T. Nguyen, "Designing User Interfaces for Intuitive Human-Agent Interaction," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.1A. ISBN: 9798244538229.
Ch1.2: T. Nguyen, "Core Agent Patterns," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.2. ISBN: 9798244538229.
Ch1.3: T. Nguyen, "Multi-Agent Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.3. ISBN: 9798244538229.
Ch1.5A: T. Nguyen, "Stateful Orchestration - Introduction and Core Concepts," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.5A. ISBN: 9798244538229.
Ch1.5B: T. Nguyen, "Stateful Orchestration - Worked Examples," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.5B. ISBN: 9798244538229.
Ch1.8: T. Nguyen, "Scalability and Production Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.8. ISBN: 9798244538229.
Ch2.3: T. Nguyen, "LangChain Sequential Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.3. ISBN: 9798244538229.
Ch2.4: T. Nguyen, "Multi-Agent Frameworks," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.4. ISBN: 9798244538229.
Ch2.6: T. Nguyen, "Tool Integration and Function Calling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.6. ISBN: 9798244538229.
Ch2.9: T. Nguyen, "Streaming and Real-Time Responses," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 2.9. ISBN: 9798244538229.
Ch3.5: T. Nguyen, "Prompt Optimization, Few-Shot Learning, Fine-Tuning," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.5. ISBN: 9798244538229.
Ch3.6: T. Nguyen, "Trace Analysis and Execution Debugging," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.6. ISBN: 9798244538229.
Ch3.7: T. Nguyen, "Tool Usage Auditing," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.7. ISBN: 9798244538229.
Ch3.8: T. Nguyen, "Action Accuracy Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.8. ISBN: 9798244538229.
Ch3.9: T. Nguyen, "Reasoning Quality," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.9. ISBN: 9798244538229.
Ch3.10: T. Nguyen, "Efficiency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.10. ISBN: 9798244538229.
Ch4.2: T. Nguyen, "Deployment and Scaling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.2. ISBN: 9798244538229.
Ch4.3: T. Nguyen, "Container Orchestration and Edge Deployment," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.3. ISBN: 9798244538229.
Ch4.5: T. Nguyen, "NVIDIA NIM and Triton Inference Server," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.5. ISBN: 9798244538229.
Ch5.1: T. Nguyen, "Chain-of-Thought (CoT) Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.1. ISBN: 9798244538229.
Ch6.5: T. Nguyen, "Production RAG Systems," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 6.5. ISBN: 9798244538229.
Ch7.3: T. Nguyen, "NeMo Agent Toolkit Profiling," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.3. ISBN: 9798244538229.
Ch8.1: T. Nguyen, "Latency Metrics," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 8.1. ISBN: 9798244538229.
Ch9.1: T. Nguyen, "Output Filtering and Content Moderation," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 9.1. ISBN: 9798244538229.
Ch10.1: T. Nguyen, "Conversational UI," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.1. ISBN: 9798244538229.
Ch10.4: T. Nguyen, "Human-in-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.4. ISBN: 9798244538229.
Ch10.5: T. Nguyen, "Human-over-the-Loop," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 10.5. ISBN: 9798244538229.
Ref1.01: NVIDIA, "NVIDIA NeMo Agent Toolkit overview," NVIDIA NeMo Agent Toolkit Documentation, v1.8. Accessed: Sep. 26, 2026. [Online]. Available: https://docs.nvidia.com/nemo/agent-toolkit/latest/index.html
Ref3.07: NVIDIA, "NeMo-Agent-Toolkit," GitHub repository. Accessed: Sep. 27, 2026. [Online]. Available: https://github.com/NVIDIA/NeMo-Agent-Toolkit
Ref3.10: "Powering the Next Generation of AI Agents," unpublished reference note (10-Powering-Next-Generation-AI-Agents.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
Ref7.03: NVIDIA, "Overview," NVIDIA NeMo Guardrails Library Developer Guide. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/nemo/guardrails/about-nemo-guardrails-library/overview
Ref7.07: E. Li, V. Bellotti, R. Kraus, and R. Kao, "Build a retrieval-augmented generation (RAG) agent with NVIDIA Nemotron," NVIDIA Technical Blog, Sep. 23, 2025. [Online]. Available: https://developer.nvidia.com/blog/build-a-rag-agent-with-nvidia-nemotron/
Ref7.16: "Production Monitoring and Operations for Agentic AI," unpublished reference note (16-Production-Monitoring-Operations.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note
Ref8.01: LangChain, "LangSmith observability: AI agent observability platform," LangChain. Accessed: Sep. 27, 2026. [Online]. Available: https://www.langchain.com/langsmith/observability
Ref10.05: "The Data Flywheel: Continuous Improvement Loop," unpublished reference note (05-Data-Flywheel-Continuous-Improvement.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note