Trace Datasets for Agentic AI: Structuring and Optimizing Traces for Automated Agent Evaluation

Agentic AI refers to multi-agent systems that plan and execute complex goals, using role-based orchestration and persistent memory. Through trace datasets and automated agent evaluation, enterprise AI leaders, platform and governance owners can manage operational challenges at scale, reducing costs, improving reliability, and ensuring compliance.

Traditional input–output evaluation assumes intelligence is expressed in a single response. However, agentic AI’s intelligence is reflected in the sequence of decisions, actions, retries, and adaptations that lead to an outcome.

The Enterprise Challenge

In agentic systems, this loss of visibility is a material business risk, not a minor evaluation gap. For teams responsible for deploying and governing agentic AI in production, limited insight into agent behavior directly impacts operational cost, incident response time, and regulatory risk. Using traditional evaluation approaches, enterprises cannot understand:

How decisions were made
Where failures originated
Whether systems are reliable, safe, and compliant

Trace datasets and automated agent evaluation together form an enterprise-ready foundation for evaluating and improving agentic AI systems. By converting raw agent execution into a repeatable pipeline from traces to structured datasets to automated evaluation, enterprises gain the observability and governance capabilities required to operate agentic systems with confidence at scale.

This article covers:

What agent traces look like in practice
How the agent traces are structured into evaluation-ready trace datasets
How those datasets enable automated agent evaluation, and
How evaluation results feed into observability and governance workflows

Traditional Evaluation and Its Limits in Agentic Systems

Traditional evaluation assesses the relationship between an input and its resulting output using metrics such as accuracy, relevance, and correctness.

In agentic systems, this approach captures what happened but not how it happened, creating gaps in transparency, accountability, and trust. For example, two agents may produce the same output while following very different paths, with different costs, risks, and failure modes.

Agentic evaluation requires accounting for:

Multi-step reasoning and planning
Tool usage and orchestration
Partial failures mid-execution
Retry and recovery logic
Multiple agents coordinating

Without measuring these behaviors, enterprises cannot reliably diagnose errors, compare agent performance, or enforce policies.

Trace Datasets in Agentic AI Systems

A trace dataset is a structured record of an agent’s behavior across a task. For example, consider this structured trace:

{

“task”: “Customer refund request,”

“agent”: “Customer support AI”,

“trace”: [

{

“step”: “Understand request”,

“action”: “Identify refund intent”,

“result”: “Refund request detected”

{

“step”: “Check eligibility”,

“action”: “Query billing system”,

“result”: “Order not eligible”,

“time_ms”: 420

{

“step”: “Apply policy”,

“action”: “Escalate to human agent”,

“result”: “Escalation triggered”

}

“final_outcome”: “Escalated to human”,

“policy_compliant”: true

}

Key components

task: What the agent was supposed to do.

agent: Which AI handled the task.

trace: Step‑by‑step records of what the agent did, including:

step: Stage or intent within the workflow

action: Attempted behavior

result: Outcome

time_ms: Step duration

This trace becomes a unit of evaluation, capturing the sequence of decisions and actions leading to the outcome. A collection of such standardized traces forms a trace dataset for automated agent evaluation.

How Trace Datasets Differ From Traditional Logs and Datasets

Evaluation-ready trace datasets preserve execution context and decision flow, including:

Decision-making paths

Planning and task decomposition

Tool selection and sequencing

Efficiency, latency, and retries

Safety, policy adherence, and risk

Examples of Trace Datasets:

Agentic trace benchmarks

System performance and latency traces

Observability traces from production systems

End-to-end task execution traces from production customer support or compliance workflows

Why Trace Datasets Matter in the Enterprise

Trace datasets support:

Explainability

Auditing and compliance

Debugging and root-cause analysis

Continuous system improvement

By evaluating and enriching trace data, enterprises can, for example:

Cut mean time to debug failures

Surface recurring policy violations early, and

Demonstrate end‑to‑end decision trails for audits.

Can Your Agentic Traces Support Automated Agent Evaluation?

Agent traces are often unstructured and difficult to analyze or compare.

Before structuring	After structuring
Fragmented logs	Ordered traces
Tool-specific events	Unified fields
Unordered outputs	Comparable runs

Structuring traces and optimizing workflows for evaluation

Preparing for automated agent evaluation requires standardizing trace data:

Standardization converts unstructured logs into machine-readable, evaluation-ready trace datasets, making agent behavior comparable across runs.

Structured traces enable automated scoring, labeling, and analysis.

In practice, this involves defining core fields such as task ID, step ID, action, tool used, latency, outcome, and policy signals.

Once structured, agent workflows can be optimized using these traces by:

Prioritizing high-risk steps

Tuning retry policies

Refining tool selection based on observed agent behavior.

This optimization loop enables continuous improvement without rearchitecting agent workflows.

Consistent trace data formats and standards

Machine-readable schemas (e.g., JSON, OpenTelemetry) capture execution context, sequence events, and link them to outcomes.

Standardized formats improve interoperability across evaluation tools, monitoring systems, and governance platforms, reducing friction as agentic systems evolve and scale.

Enterprise value: With structured trace data, enterprises can compare performance, conduct automated analysis at scale, and integrate the insights into evaluation and governance pipelines.

Automated Agentic AI Evaluation: Measuring Agent Behavior at Scale

Automated agentic AI evaluation measures behavior across tasks rather than judging outcomes in isolation.

Step-level evaluation asks

Were the right tools selected at each step?

How many retries or recoveries occurred?

Where latency or failures emerged in the workflow?

Outcome-level evaluation asks

Did the task complete successfully?

Was the final response correct or policy-compliant?

These metrics are computed directly from individual trace steps rather than inferred solely from the final output. For example, escalation appropriateness can be measured by comparing policy-required escalation steps in the trace against the agent’s actual actions, while efficiency metrics such as cost or latency are computed from cumulative tool calls and step-level execution times within a trace.

Agentic AI Evaluation & Insight

Automated agentic AI evaluation platforms use trace data in live and offline environments to:

Monitor system health

Detect regressions

Identify inefficiencies

Support governance and audits

Labeling and Enriching for Learning and Governance

Labeling and enrichment typically occur at the trace-step level, turning evaluations into reusable training and analytics assets.

Example:

Marking a failed tool call

Annotating a reasoning error

Flagging a policy-compliant escalation

Common trace data labels include:

Success/failure

Safe/unsafe

Correct/incorrect

The resulting labeled and enriched trace data becomes a long-term asset, supporting continuous learning and automated agent evaluation.

Automated annotations add context by

Explaining errors or edge cases

Clarifying intent

Linking traces to gold standards

Connecting agent behavior to business outcomes.

Human-in-the-loop improves reliability and reduces room for critical errors.

Use Case: Customer Support Agent

In high-volume customer support environments, a customer support agent handles thousands of requests per day, generating trace datasets across chat conversations, ticketing platforms, and internal knowledge bases.

Automated agent evaluation uses these traces to assess outcomes such as first-contact resolution rates, escalation appropriateness, average tool calls per ticket, and recovery from partial failures.

Agent workflows are designed to be evaluation-ready, producing clear, structured traces at each step of execution so that agent behavior can be measured consistently over time.

This supports automated agent evaluation at scale while reducing time to diagnose failures and maintaining privacy controls, auditability, and regulatory compliance.

Challenges, Best Practices, and Considerations

Before adopting automated evaluation, it is important to understand the common challenges that can impact evaluation accuracy.

Key challenges to evaluating agentic AI

Sampling strategies: Evaluating every trace is impractical; selective sampling is needed to capture rare but high-impact failures.

Storage and retention tradeoffs: Trace datasets must balance regulatory and audit requirements with storage costs and retention limits.

Step-level privacy redaction: Sensitive information often needs masking at the individual trace-step level, rather than across entire tasks or sessions.

Evaluation drift: As agents evolve, evaluation criteria must remain consistent or be explicitly versioned to maintain meaningful comparisons over time.

Operational and business alignment: Evaluation workflows must balance tooling and process complexity while ensuring alignment with business objectives, risk tolerance, and domain priorities.

These challenges can be made tractable by:

Using trace datasets to drive targeted sampling

Tiered retention and redaction policies

Versioned evaluation criteria, and

Tight alignment between agent workflows and business risk

Best Practices for Agentic AI Evaluation

Trace-based evaluation makes tradeoffs such as speed versus safety or autonomy versus escalation explicit and measurable. By grounding these tradeoffs in trace data, enterprises can tune agent behavior deliberately rather than discovering unintended risk only after failures occur in production.

To enable this,

Define clear, behavior-level evaluation metrics based on an agent’s reasoning steps, tool usage, and recovery behavior, rather than relying solely on final outputs.
Start with high-impact, high-risk agent workflows where evaluation gaps have clear business or compliance consequences.
Combine automated evaluation with targeted human-in-the-loop review for ambiguous decisions, policy edge cases, and high-severity failures.
Align evaluation criteria with business objectives, domain risk tolerance, and operational constraints, in addition to model performance metrics.
Design agent workflows to be evaluation ready from day one by ensuring every decision, tool call, and recovery step produces structured, traceable signals.

Designing Evaluation-Ready Agent Workflows

Agentic AI cannot be governed, improved, or trusted using output-only evaluation.

Trace datasets provide the foundation for understanding and managing agent behavior. Trace-based evaluation ensures that agentic systems continue to operate as intended when embedded within enterprise workflows.

Innodata focuses on creating evaluation-ready trace datasets through trace structuring, step-level labeling, enrichment, and human-in-the-loop workflows. Our work complements existing agent frameworks and observability tools, enabling enterprises to evaluate agent behavior across both development and production environments consistently.

Connect with our experts to explore how trace-based evaluation fits into your agentic roadmap.

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