A production agent can finish a workflow without producing results anyone should accept. That raises an important question: How do you tell the difference between a workflow that simply ran and one that actually produced a good result?
Useful monitoring connects the agent’s actions and operating costs to the quality of the final result. Teams can then investigate failures, track changes over time, and decide when a person should step in. A well-designed AI agent monitoring approach can also reveal where performance breaks down and whether changes actually improve the work.
What is AI agent monitoring, and why does it matter?
AI agent monitoring is the ongoing measurement and review of an agent's behavior, results, and operating conditions across deployed workflows. It connects the final outcome to the steps and tools behind it, helping teams determine whether the system ran as expected and produced acceptable work.
Traditional application monitoring remains important for tracking uptime, errors, and infrastructure health. But since agents can make branching decisions and take actions through external tools, teams also need visibility into what happens throughout the workflow. A request can complete without an error even when the result is wrong, or the agent takes an inappropriate action.
Agent performance monitoring helps teams investigate failed work, identify performance changes, and determine when human review is necessary. For consequential workflows, teams can combine objective checks, such as required fields or verified tool results, with human review of ambiguous cases. Mercor's guide on how to evaluate AI agents provides a broader framework for defining and assessing outcome quality.
Key AI agent monitoring metrics
Monitoring is most useful when teams know which signals to watch and how to interpret them. Each metric should connect to a defined outcome, population, and time window. Segmenting results by workflow and release can also reveal changes that an overall dashboard might hide.
Task completion and output quality
A finished attempt doesn't necessarily mean the work met the task’s requirements. Track outcomes such as:
- Accepted completion
- Partial completion
- Failed completion
- Human-corrected completion
- Escalation
Where possible, define task completion using external acceptance criteria rather than relying on the agent’s own “done” status.
Goal or intent alignment
Goal-alignment metrics show whether the agent’s result actually satisfies the original request and its constraints. Relevant signals can include:
- Required task objectives
- User-specified constraints
- Required deliverables
- Missed or conflicting requirements
- Reviewer assessments of goal alignment
A task-specific checklist or consistent reviewer can help assess whether the result meets those requirements. Even a fluent, factually plausible answer can still solve the wrong problem.
Agent steps and decision paths
Decision-path metrics help teams identify inefficient or problematic execution patterns. Useful indicators include:
- Repeated actions
- Unexpected branching
- Stalled loops
- Unnecessary steps
- Recorded state changes
Traces can show these observable actions and state changes, but they don't guarantee access to a model’s private reasoning.
Tool use and actions
Choosing the right tool is only part of successful execution. Monitoring should also show whether the agent: Selected the appropriate tool
- Supplied valid arguments
- Received the expected result
- Used the result correctly
- Produced the intended downstream effect
A technically successful call can still be inappropriate or unauthorized.
Errors and recovery
Assess failures in the context of the final outcome. Useful measures include:
- Failed tool calls
- Retries
- Fallbacks
- Recovery success
- Unresolved errors
- Duplicate or unintended side effects
A successful retry doesn't rule out duplicate side effects from an earlier attempt.
Latency, token usage, and cost
Latency and cost metrics show how efficiently an agent completes useful work. Track:
- Total workflow time
- Time spent in individual steps
- Token usage
- Tool or model costs
- Retry costs
- Cost per completed task
- Cost per accepted outcome
. Compare cost per accepted outcome with the completion rate.
Human intervention
Human-intervention metrics show when an agent needs assistance, correction, or required approval. Distinguish between:
- Planned human review
- Required approvals
- Unexpected intervention
- Correction after an error
A lower intervention rate is not automatically better if the agent is producing more undetected failures.
Safety or policy violations
Safety and policy metrics show when an agent attempts or completes actions that fall outside defined rules. Teams should track:
- Attempted prohibited actions
- Blocked actions
- Completed violations
- Severity
- Review status
Treat detector flags as reasons to investigate rather than definitive proof of a violation or compliance.
Performance changes or drift
Performance monitoring can reveal when agent behavior or results change over time. Make sure to compare:
- Quality metrics
- Task completion rates
- Error or intervention rates
- Latency and cost
- Performance across releases or time periods
Compare these signals against a baseline for similar tasks, accounting for changes in inputs, tools, or dependencies before attributing a decline to a new release.
Multi-agent coordination or handoffs when applicable.
Multi-agent monitoring should show whether handoffs and shared workflows introduce problems between agents. It’s important to track:
- Lost context
- Duplicate work
- Handoff delays
- Failed or incomplete handoffs
- Each agent’s contribution to the shared outcome
Correlated run identifiers can help trace these problems across the workflow. Evaluate the shared outcome alongside each agent’s contribution.
These metrics become more useful when they're connected to the traces, evaluations, and observability data that show what happened during the workflow and why.
AI agent monitoring and observability techniques
Monitoring tracks selected signals and alerts, while AI agent observability provides the context teams need to investigate how and why behavior changed. The two overlap because modern observability can follow an entire agent workflow rather than stopping at individual model calls.
A research agent might produce an incomplete report even though the workflow appears to run normally. A correlated trace can show what happened across retrieval, model, and tool steps, while structured logs provide release and workflow context. A dashboard can reveal whether similar runs have changed over time, while reviewing sampled outcomes against a rubric helps determine whether the reports met the required standard. Together, these approaches help teams understand what happened and assess the quality of the work.
A useful quality loop connects production traces to explicit evaluation criteria. Reviewed failures can then become regression cases, allowing teams to test proposed changes under controlled conditions before returning the updated system to production monitoring.
AI evaluation tools can help scale this process, but model-based judges should be validated against expert judgments. Some production outcomes also lack immediate ground truth. Related approaches include using LLM-as-a-judge evaluation for scalable scoring, alongside understanding how benchmarks support evaluation under controlled conditions.
Mercor’s production rubrics help assess deployed work, while its offline benchmarks allow teams to compare configurations under controlled conditions. Both complement operational tracing, but neither guarantees production reliability.
How to monitor AI agents: Step-by-step monitoring framework
Start by defining the work an agent is expected to complete, then instrument its workflow to connect outcomes to the steps that produced them. The following 9 steps provide a practical framework for setting up AI agent monitoring:
- Define expected behavior: Specify acceptable outcomes, required evidence, and actions the agent must not take.
- Choose metrics and thresholds: Select quality and operational measures that reflect the expected behavior, and define how each is calculated.
- Instrument workflows: Add consistent identifiers and spans around key agent actions, model calls, retrieval steps, and tool use.
- Capture traces and logs: Record inputs, outputs, errors, timestamps, and relevant run context in accordance with your data-handling rules.
- Monitor tools and dependencies: Track tool selection, arguments, execution results, and the status of external services.
- Track quality, errors, latency, and cost: Review these signals together, accounting for retries and resource use per accepted outcome so improvements in one area don't mask problems elsewhere.
- Configure alerts for thresholds and unusual behavior: Direct alerts to someone responsible for investigating them rather than leaving them on a dashboard.
- Review failures: Connect failed outcomes to their traces, classify the failures, and determine whether human intervention was needed.
- Reevaluate after system changes: Recheck affected workflows whenever updates to models, prompts, tools, permissions, or dependencies could alter agent behavior.
An effective monitoring setup lets teams connect a failed outcome to its trace and release, identify an owner, and verify whether a proposed fix worked. Sampling and filtering can control how much production activity receives evaluation, while recorded traces can support repeatable development tests. These practices help teams balance production coverage with the cost and effort required to evaluate every interaction.
Keeping this process reliable over time requires consistent baselines, regular reviews, and change records as workflows and operating conditions evolve.
Implementation best practices for monitoring AI agent activity
Monitoring needs to stay useful as workflows and operating conditions change. The following practices help teams preserve context, identify meaningful performance changes, and review agent activity safely. Thresholds, sampling, and retention settings should reflect the specific workflow rather than follow a one-size-fits-all approach.
- Cover the complete workflow: Connect agent, model, tool, and dependency events across the entire workflow, including the final outcome.
- Set and update performance baselines: Compare similar tasks, releases, and environments before concluding that a performance change is a genuine regression.
- Analyze failures: Classify failures and turn consequential, reproducible cases into regression tests.
- Track changes: Record model, prompt, tool, permission, and workflow versions so teams can make meaningful comparisons.
- Protect sensitive data: Limit captured content, restrict access, and set retention periods according to your data obligations. Verify each platform’s current controls before relying on them.
- Maintain human oversight: Define who reviews uncertain or high-impact outcomes and when the agent must pause for approval.
Version and environment records help teams interpret performance changes, while sampling controls can limit which production runs receive evaluation. Sensitive-data protections are equally important when traces contain prompts, outputs, or other protected information. These are workflow-specific implementation decisions, not universal compliance requirements.
Document the reasoning behind each alert threshold and revisit it when the task or risk changes. Otherwise, adjusting a threshold could mask deteriorating performance instead of triggering an investigation.
AI monitoring tools can support these practices by bringing tracing, evaluation, review, and ongoing monitoring together.
Top AI agent monitoring and observability tools
Choosing an AI agent monitoring tool starts with understanding the workflow you need to observe and the evaluation flexibility it requires. Consider how each tool fits your existing systems, along with its deployment options, data controls, retention policies, and the effort involved in collecting and evaluating traces. The following tools address different monitoring needs, and their order doesn't represent a tested ranking.
Langfuse
Langfuse is an open-source, self-hostable platform that brings together tracing, evaluation, datasets, and experiments. It can help teams use production traces to investigate issues and test improvements during development. Hosting, access controls, and retention are important considerations when choosing how to deploy it.
LangSmith
LangSmith combines production tracing with filtered and sampled online evaluators, helping teams connect agent activity to ongoing quality reviews. Teams can control which runs receive evaluations, making it important to configure sampling around the traffic volume and outcomes they need to assess.
Arize Phoenix
Arize Phoenix provides open-source tracing using OpenTelemetry and OpenInference, alongside evaluations, datasets, and experiments. Teams can inspect recorded runs and use them to test changes through repeatable experiments. Phoenix is distinct from Arize AX, the company's separate managed enterprise platform.
Braintrust
Braintrust connects production traces with scoring and evaluation workflows. Its scoring rules can target individual spans, complete traces, or groups of related traces, helping teams evaluate real interactions consistently. Filters and sampling determine which interactions receive scores, so teams should configure them around the outcomes they need to review.
Datadog
Datadog’s Agent Observability combines agent tracing with operational metrics and quality or safety evaluations. This AI observability platform may suit teams looking to monitor agents alongside their broader operations stack. Availability and data-handling requirements can vary by deployment, so teams should verify these details before implementation.
Galileo
Galileo’s evaluation and observability technology now powers Splunk Agent Observability, which combines tracing, evaluation, and guardrails for agentic applications. Galileo's existing documentation remains available for logging, tracing, and evaluation workflows. Teams considering the platform should confirm which Splunk or Galileo deployment suits their environment.
Because terminology overlaps across AI observability tools, understanding what each platform actually monitors matters more than its product label.
AI agent monitoring vs. LLM observability
AI agent monitoring and LLM observability overlap, and modern observability platforms can trace complete agent workflows. The main distinction lies in what teams focus on: agent monitoring emphasizes task-level outcomes and operating controls, while LLM observability provides detailed evidence about model behavior and the surrounding application. These are overlapping approaches rather than separate categories of tools.
| Dimension | AI agent monitoring | LLM observability |
|---|---|---|
| Primary question | Did the agent complete acceptable work within its boundaries? | What happened during model calls and the surrounding application workflow? |
| Typical scope | End-to-end execution, including decisions, tools, handoffs, and outcomes | Inference, prompts, retrieval, tools, and related traces; may cover the complete agent workflow |
| Quality signals | Task completion, goal alignment, interventions, and policy outcomes | Response quality, model behavior, tokens, latency, and errors |
| Operational evidence | Workflow state, downstream effects, release information, and dependency context | Inputs, outputs, spans, model calls, and application context |
| Best used for | Tracking deployed workflow performance and identifying outcomes that need review | Debugging and analyzing LLM-powered applications during development and production |
Model-level evidence helps teams diagnose individual steps and connect those findings to task-level accountability. Both perspectives matter: operational success doesn't establish output quality, and a model score alone cannot confirm that an agent completed the workflow safely.
Monitor your AI agent performance against expert-built criteria
Turn production activity into evidence of your agents meeting real-work requirements. Mercor's enterprise evaluations help teams test deployed agents against expert-built criteria, comparing configurations through purpose-built evaluations.
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