How to Evaluate AI Agents and Agentic Workflows: A Comprehensive Guide

AI agents have evolved beyond simple question-answer systems into complex, multi-step entities that plan, reason, retrieve information, and execute tools across dynamic conversations. This evolution introduces significant evaluation challenges. Unlike traditional machine learning models with static inputs and outputs, AI agents operate in conversational contexts where performance depends on maintaining state, selecting appropriate tools, and adapting responses across multiple turns.

Enterprise teams building production AI agents need systematic evaluation frameworks that work both before deployment and in production. This guide explains how to implement comprehensive evaluation strategies for AI agents and agentic workflows using offline testing, online monitoring, and human-in-the-loop validation.

Understanding the AI Agent Evaluation Challenge

Traditional ML evaluation metrics fail to capture the complexity of agentic systems. An agent might generate a grammatically perfect response while selecting the wrong tool, losing critical context from previous turns, or hallucinating facts that appear plausible. AI agent evaluation requires measuring multiple dimensions: conversational coherence, tool use accuracy, factual correctness, task completion, and alignment with business rules.

The evaluation strategy must account for the entire agent lifecycle. Pre-deployment testing validates that agents handle expected scenarios correctly. Production monitoring detects quality degradation in real user interactions. Continuous evaluation creates feedback loops that improve agent performance over time.

Offline Evaluation: Testing Before Deployment

Offline evaluation enables teams to test agents against curated datasets and simulated scenarios before facing real users. This approach reduces risk by catching issues in controlled environments where failures have no customer impact.

Agent Simulation for Multi-Turn Testing

Agent simulations represent the most effective method for testing conversational agents. Static input-output pairs cannot capture the dynamic nature of multi-turn interactions. Simulations create realistic conversations between your agent and configurable user personas.

The simulation framework allows teams to define specific user characteristics such as "impatient customer seeking refund" or "technical user troubleshooting integration issue." Each simulation scenario includes the user's goal and expected steps the agent should take to resolve the query. This approach validates that agents maintain context across exchanges, adapt their communication style to different personas, and complete multi-step tasks correctly.

For example, testing a customer service agent requires simulating scenarios where users provide information incrementally across multiple turns. The agent must remember previous details, ask clarifying questions when needed, and execute the correct sequence of actions to resolve the issue.

HTTP Endpoint Evaluation

Teams with externally hosted agents can use HTTP Endpoint Evaluation to test without modifying application code. This method sends payloads to the agent's API endpoint and evaluates responses directly.

Configuration involves connecting the evaluation platform to your agent's URL and mapping test dataset columns to your API's input schema. For instance, mapping a question column to the API's messages body parameter. Multi-turn support enables realistic stateful testing by configuring the endpoint to handle conversation history, ensuring the agent maintains context across sequential interactions.

This approach benefits teams running agents in separate infrastructure or using agents provided by third-party services where direct code integration is impractical.

No-Code and Dataset Evaluation

The No-Code Agent Builder enables product teams to test agentic workflows with datasets and evaluators without writing code. This removes engineering bottlenecks from the evaluation process, allowing domain experts to validate agent behavior directly.

For teams with pre-generated logs from previous test runs or production data, Dataset Evaluation scores existing outputs without re-running the agent. This method accelerates iteration cycles when testing prompt modifications or evaluator configurations against known agent responses.

Online Evaluation: Monitoring Production Performance

Deployment does not end the evaluation process. Online Evaluation monitors agent performance in real-time as actual users interact with the system. Production monitoring detects quality degradation, identifies edge cases not covered in offline testing, and provides data for continuous improvement.

Distributed Tracing for Multi-Level Evaluation

Distributed Tracing captures the complete hierarchy of agent operations, enabling evaluation at multiple granularities:

Sessions represent full multi-turn conversations. Session-level evaluation answers questions like "Did the user achieve their goal?" or "Was the conversation resolved satisfactorily?" This level provides the most meaningful quality signal for conversational agents.

Traces evaluate individual interactions or turns within a session. Trace-level evaluation measures whether specific responses were accurate, appropriate, and helpful within the conversation context.

Spans evaluate specific steps within a trace, such as a Retrieval (RAG) step, a Tool Call, or an LLM generation. Span-level evaluation pinpoints exactly which component of a multi-step workflow caused failures.

This hierarchical approach enables precise diagnosis of agent issues. If session-level metrics show task completion failures, teams can drill down to specific traces and spans to identify whether the problem stems from retrieval quality, tool selection, or response generation.

Real-Time Alerting

Production quality can degrade for numerous reasons: model API changes, shifts in user behavior, or unexpected edge cases. Alerts notify teams via Slack or PagerDuty when quality scores drop below defined thresholds.

Alert configuration includes setting evaluation criteria, defining acceptable score ranges, and specifying notification channels. This enables rapid response to production issues before significant numbers of users experience degraded service.

Evaluator Types and Selection

Comprehensive agent evaluation requires multiple evaluator types, each suited to different quality dimensions.

AI Evaluators

AI Evaluators use LLMs to judge subjective quality dimensions that resist programmatic measurement. These evaluators assess criteria like faithfulness to source material, presence of hallucinations, appropriate tone, and brand voice compliance.

AI evaluators excel at nuanced assessments that require reasoning and context understanding. For example, determining whether an agent's medical advice response appropriately caveats limitations and recommends professional consultation requires understanding both the technical content and communication expectations.

Statistical Evaluators

Statistical Evaluators apply standard ML metrics for text comparison. BLEU, ROUGE, and Exact Match scores measure how closely agent outputs match expected responses in datasets.

These evaluators work well for scenarios with well-defined correct answers where variation indicates quality degradation. However, statistical metrics often fail to capture semantic equivalence—two responses with identical meaning can receive different scores due to phrasing differences.

Programmatic Evaluators

Programmatic Evaluators execute code-based assertions in JavaScript or Python. These evaluators validate specific business rules and format requirements.

Common use cases include JSON schema validation, regex pattern matching, checking for required keywords, or verifying specific data formats. For instance, ensuring that all product recommendations include SKU numbers in the correct format (e.g., "SKU-12345") requires programmatic validation.

Human Evaluators

Human Annotation provides the gold standard for quality assessment, particularly for subjective criteria or high-stakes applications. Domain experts review agent outputs and provide ratings based on specialized knowledge.

Human evaluation becomes essential when automated metrics cannot capture critical quality dimensions or when validating agent behavior in regulated domains like healthcare, finance, or legal services.

Building Custom Evaluators

Pre-built evaluators cover common use cases, but production AI agents often require custom evaluation logic specific to business rules, domain requirements, or proprietary workflows.

Custom AI Evaluators (LLM-as-a-Judge)

Custom AI evaluators assess domain-specific subjective criteria. Teams select a judge model (GPT-4, Claude 3.5 Sonnet, etc.) and define evaluation instructions in natural language.

Scoring options include Binary (Pass/Fail), Scale (1-5), or Categorical (e.g., "Compliant," "Minor Violation," "Critical Violation"). For example, a custom evaluator for a financial services agent might assess whether responses correctly include required regulatory disclaimers and risk warnings specific to the company's compliance requirements.

Custom Programmatic Evaluators

Custom programmatic evaluators implement deterministic, logic-based checks in code. Teams write a validate function directly in the platform using Python or JavaScript.

Example implementation:

def validate(output, expected_output, **kwargs):
    # Fail if forbidden competitive mentions appear
    if "competitor_name" in output.lower():
        return {
            "score": 0,
            "result": "Fail",
            "reason": "Mentioned competitor"
        }
    return {
        "score": 1,
        "result": "Pass",
        "reason": "Clean output"
    }

This approach suits validation of specific format requirements, keyword presence or absence, data structure correctness, or any other rule that code can verify deterministically.

API-Based Evaluators

Teams with existing scoring services or specialized compliance APIs can integrate them as custom evaluators. This maintains a unified quality view within the evaluation platform while leveraging proprietary or external validation systems.

Human-in-the-Loop Evaluation

High-stakes applications require human validation alongside automated metrics. Human-in-the-loop evaluation allows subject matter experts to review agent outputs and provide authoritative quality assessments.

Annotation Workflows

Two primary workflows support human evaluation:

Annotate on Report enables internal team members with platform access to rate entries directly within test run reports. Multiple reviewers can rate the same entry, and the platform calculates average scores. This workflow suits internal quality reviews by cross-functional teams.

Send via Email allows external subject matter experts to review outputs without requiring paid platform access. Reviewers receive secure links to a rating dashboard where they provide assessments. This workflow enables feedback from clients, external consultants, or domain specialists like medical professionals or legal advisors.

Automating Evaluations in CI/CD Pipelines

Continuous Integration and Continuous Deployment practices require programmatic evaluation triggers. The SDK enables automated test execution within CI/CD pipelines.

Example implementation:

from maxim import Maxim

# Initialize client
maxim = Maxim({"api_key": "YOUR_API_KEY"})

# Trigger automated test run
result = (
    maxim.create_test_run(
        name="Weekly Agent Regression",
        in_workspace_id="YOUR_WORKSPACE_ID"
    )
    .with_data_structure({
        "input": "user_query",
        "context": "retrieved_docs",
        "expected_output": "ground_truth"
    })
    .with_data("dataset-id-uuid")
    .with_prompt_version_id("prompt-version-uuid")
    .with_evaluators(
        "Answer Relevance",
        "Tool Call Accuracy",
        "Hallucination"
    )
    .run()
)

print(f"Test Run Completed. Results: {result.test_run_result.link}")

This approach prevents quality regressions by validating every agent modification against comprehensive test suites before deployment.

Best Practices for Agent Evaluation

Curate from Production Data

Data Curation transforms failed production traces into test cases for offline evaluation datasets. This creates continuous feedback loops where production issues directly inform test coverage.

When agents fail in production, the specific conversation context and failure mode become test cases that prevent regression. This approach ensures test suites evolve to cover real user scenarios rather than hypothetical edge cases.

Validate Tool Use Explicitly

For agentic systems that execute tools, the Tool Call Accuracy evaluator verifies that agents select correct tools with appropriate parameters. Tool selection errors often cause cascading failures in multi-step workflows, making this evaluation dimension critical for agentic applications.

Generate Synthetic Test Data

Teams lacking sufficient test data can use Synthetic Data Generation to create datasets covering edge cases and diverse user inputs. Synthetic data generation ensures comprehensive test coverage even when historical interaction logs are limited.

Conclusion

Evaluating AI agents requires systematic approaches that work across the development lifecycle. Offline evaluation through simulation, endpoint testing, and dataset validation catches issues before deployment. Online monitoring with distributed tracing provides real-time quality signals in production. Custom evaluators tailored to business requirements ensure agents meet domain-specific standards. Human-in-the-loop validation provides authoritative quality assessment for high-stakes applications.

Teams shipping production AI agents need comprehensive evaluation frameworks that measure quality at multiple granularities, integrate with development workflows, and enable cross-functional collaboration between engineering, product, and domain experts.

Maxim's evaluation platform provides end-to-end infrastructure for AI agent quality. Get started today or book a demo to see how comprehensive evaluation accelerates AI agent development.