Evaluation

Last updated: 2025-10-21

While observability provides the instrumentation to collect metrics and traces, evaluation analyzes that data to determine how well agents are performing against defined success criteria and business requirements.

Multi-agent evaluation challenges

Multi-agent systems introduce evaluation complexities like:

Path optimization: Agents may reach correct solutions through inefficient routes, making it difficult to assess optimal performance
Error propagation: Upstream failures can cascade through agent interactions, obscuring the root cause of downstream issues
Emergent behavior: Collective agent interactions produce behaviors that cannot be predicted from individual agent analysis
Non-deterministic outputs: The same input may produce different valid responses, complicating traditional success metrics

Evaluation types and methodologies

Code-based evaluation

This approach uses deterministic and programmatic criteria (like unit testing) to validate system behavior, for example:

Evaluation Type	Purpose	Example
API Response Validation	Ensures endpoint responses match expected results.	A `POST /agent/start` call should return HTTP 200 and include `task_id`.
Output Format Verification	Validates structure and data types of responses.	An agent’s reply must include `action`, `result`, and `status` fields.
Performance Benchmarking	Measures response times and resource usage.	The agent must respond within 500ms under 100 requests per second.
Security Compliance Checks	Verifies adherence to security standards.	Ensure tokens aren’t logged and sensitive headers are encrypted.

LLM-as-a-judge evaluation

Unlike code-based evaluation, which uses deterministic rules, this method leverages Large Language Models (LLMs) to assess the semantic, stylistic, and contextual quality of responses—dimensions that are difficult or impossible to capture with hard-coded assertions.

Evaluation Aspect	Purpose	Example
Relevance Scoring	Measures how well the response answers the input.	An agent asked to summarize an article should produce a coherent summary.
Tone and Style Evaluation	Assesses whether the response aligns with expected tone or voice.	A customer support agent should respond politely and professionally.
Bias Detection	Identifies implicit or explicit bias in output.	Flag responses that reinforce gender stereotypes or political bias.
Hallucination Detection	Flags statements that are factually incorrect.	Detect when an agent invents citations or misrepresents known facts.

Evaluation phases

Offline evaluation

Pre-deployment testing using curated datasets and controlled environments:

Benchmark dataset comparison with expected input/output pairs
Regression testing against historical performance baselines
Edge case validation using synthetic scenarios
Load testing for scalability assessment

Maintain comprehensive test datasets that evolve with your system. Regular updates with new edge cases and failure examples ensure evaluation relevance.

Online evaluation

Production monitoring with real user interactions:

Real-time performance tracking
User satisfaction measurement
Anomaly detection and alerting
Continuous feedback collection

Evaluation feedback loop

Adopt an iterative loop that blends offline analysis, live monitoring, and data-driven refinement:

flowchart TD
    A[1.Offline Evaluation] -->  B[Deploy Agent Version]
    B --> C[2.Online Evaluation & Monitoring]
    C --> D[Collect Data & Examples]
    D --> E[3.Dataset Enrichment]
    E --> F[4.Iteration & Refinement]
    F --> A

    style A fill:#e1f5fe, color:#000000
    style C fill:#f3e5f5, color:#000000
    style E fill:#e8f5e8, color:#000000
    style F fill:#fff3e0, color:#000000

Critical evaluation areas

High-risk operations

Systems that modify databases, trigger external actions, or handle sensitive data require enhanced evaluation rigor:

Accuracy validation: Verify correctness of all data modifications
Authorization checks: Ensure proper access control enforcement
Audit trails: Maintain comprehensive logs of all system changes
Rollback testing: Validate recovery mechanisms for failed operations

Security and compliance

Access control: Validate authentication and authorization between agents
Data protection: Test privacy preservation and data handling protocols
Adversarial resilience: Assess system behavior under attack scenarios
Regulatory compliance: Verify adherence to industry standards and regulations

Don't select metrics simply because they're available in your toolbox. Choose evaluation strategies that align with your specific use case and business requirements.

Evaluation strategies by system component

Orchestrator agent evaluation

Intent resolution: Validate correct routing and task decomposition decisions
Plan optimization: Assess efficiency of generated execution plans
Response synthesis: Evaluate quality of final output aggregation
Error handling: Test recovery mechanisms when specialized agents fail

Specialized agents evaluation

Task completion: Measure accuracy and completeness of domain-specific outputs
Tool call: Validate correct function/tool calling and API interactions
Response quality: Assess output relevance, accuracy, and formatting
Boundary handling: Test behavior at capability limits and edge cases

Registry evaluation

Discovery accuracy: Verify correct agent selection for given capabilities
Metadata integrity: Validate agent descriptions and capability mappings
Performance tracking: Monitor agent availability and response times
Version management: Test compatibility across different agent versions

For reference:

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