Debug AI Agents Like a Pro
Follow instrumented LLM calls, retrievals, tool interactions, and agent steps in one trace.
AI agents are complex, non-deterministic, and opaque. Hierarchical tracing gives investigators a concrete execution record to inspect when something goes wrong.
Trace-level
Investigation Context
The Challenge
Why Debugging AI Agents is So Hard
Traditional debugging tools (logs, metrics, profilers) were designed for deterministic software. AI agents are fundamentally different and require a different approach.
The Black Box Problem
LLMs are black boxes. You can see input and output, but not internal reasoning. When an agent produces a wrong answer, you don't know if it's a bad prompt, bad LLM response, or bad tool call.
Non-Deterministic Behavior
The same input can produce different outputs. This makes it hard to reproduce bugs and understand what went wrong. You can't just run the same input again.
Complex Workflows
Modern AI agents are complex multi-step workflows with LLM calls, tool interactions, and decision points. A single failure can be caused by any step.
Inadequate Logging
Traditional logging is insufficient. You need to see not just what happened, but why. What was the LLM's reasoning? What data did the tool return?
Debugging Without a Connected Trace
When prompts, model calls, retrievals, and tool results live in separate logs, investigators must reconstruct the execution path before they can test a hypothesis.
The Solution
Noveum.ai Tracing: Connected Execution Evidence
Use hierarchical tracing to connect the instrumented operations from an initial input to the final recorded output.
What Gets Traced
Supported integrations capture the callbacks they receive; context managers and custom spans cover application-specific operations
Workflow Tracing
Instrument supported framework callbacks and add custom operations where your application needs more context.
- Instrumented LLM calls with the fields your capture settings allow
- Instrumented tool calls with available inputs, outputs, and timing
- API calls and database queries you wrap as custom operations
- Custom operations you define
Key Benefits
Why Teams Choose Noveum.ai Tracing
Debug faster, understand better, and deploy with confidence.
Reduce Debugging Time
Start from a connected execution record instead of reconstructing an agent run from unrelated logs.
Understand Agent Behavior
Inspect the operations and data your instrumentation recorded; tracing does not expose hidden model reasoning.
Improve Quality
By understanding what's going wrong, you can make targeted improvements. Fix root causes, not symptoms.
Optimize Performance
See which operations are slow. Identify bottlenecks and optimize them for better latency.
Build Confidence
Deploy agents with confidence knowing you can quickly debug any issues that arise.
Reduce Costs
Identify expensive operations and optimize LLM usage. Save on API costs with targeted improvements.
From Signal to Supporting Evidence
Filter to the affected trace, inspect its span hierarchy and raw attributes, and compare it with a healthy execution before deciding what to change.
Getting Started
How Tracing Works
Send a representative trace in three steps, then verify its captured fields before broadening coverage.
- 1
Install the SDK
Install the Noveum.ai Python SDK with a single command.
- 2
Add Callback Handler
Attach the callback handler to the LangChain invocations whose supported events you want to trace.
- 3
View Traces
After an instrumented invocation is ingested, open Explorer and verify its span hierarchy and attributes.
Install the SDK
Install the Noveum.ai Python SDK with a single command.
pip install noveum-traceAdd Callback Handler
Attach the callback handler to the LangChain invocations whose supported events you want to trace.
llm = ChatOpenAI(callbacks=[callback_handler])View Traces
After an instrumented invocation is ingested, open Explorer and verify its span hierarchy and attributes.
noveum.ai/tracesTrace Exploration
The trace viewer exposes the execution context your instrumentation captured. You can:
- 1View the full hierarchy of operations
- 2Click any span to see detailed information
- 3Inspect errors and form a root-cause hypothesis
- 4Compare traces to find differences
Real-World Applications
How Teams Use Noveum.ai Tracing
See how developers use tracing to debug and optimize their agents.
Debugging Production Issues
When an agent fails in production, inspect the captured trace to find the operation and evidence most likely to explain the failure before deciding whether local reproduction is needed.
Example: A support agent gives wrong answers. The trace shows an LLM hallucination in step 3 of a 5-step workflow.
Performance Optimization
An agent is slow. See which operations take the most time and optimize them for better user experience.
Example: A captured trace attributes most measured latency to one tool call. The team changes that call and compares latency on a representative follow-up cohort.
Quality Improvement
An agent produces low-quality outputs. Trace execution to see where quality is degrading and fix the root cause.
Example: Poor outputs traced to context retrieval returning irrelevant documents. Fix the retrieval, fix the quality.
Multi-Agent Debugging
When a multi-agent system fails, instrument the relevant workflow boundaries and interactions so investigators can inspect the captured execution path.
Example: Agent A sends wrong data to Agent B. The trace shows exactly where the miscommunication happens.
Why Noveum.ai
Why Noveum.ai Tracing Stands Out
Purpose-built for AI agents, not generic software monitoring.
vs. Traditional Logging
Logs are text-based and hard to parse. You have to grep through thousands of lines to find issues.
Noveum.ai
Structured, hierarchical traces that are easy to explore and understand visually.
vs. APM Tools (Datadog, New Relic)
Generic APM without AI-specific instrumentation may not retain the prompt, model, token, retrieval, tool, and evaluation context needed for agent investigation.
Noveum.ai
AI-specific tracing with LLM insights, cost tracking, and integration with evaluation.
vs. Custom Solutions
Building your own tracing system is expensive and time-consuming. Maintenance is a constant burden.
Noveum.ai
Battle-tested, proven solution that works out of the box with any framework.
Unique Features
AI-Specific
Designed specifically for AI agents, not generic software.
Hierarchical
See the hierarchical structure of recorded agent operations, not just flat logs.
Lightweight
Measure SDK overhead with your payload, network, batching, and flush settings before production rollout.
Framework-Agnostic
Works with LangChain, LangGraph, CrewAI, and any custom agents.
Integrated
Connects tracing with evaluation and NovaPilot analysis reports.
Recent Trace Access
Inspect traces after ingestion and refresh the selected time window when new traffic arrives.
Stop Flying Blind
Connect agent operations in hierarchical traces, inspect failures with context, and verify changes with evidence.
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