Evaluating multi-agent systems involves unique challenges compared to single-agent evaluations. This guide provides clear explanations of various architectures, strategies for effective evaluation, and additional considerations.
A multi-agent system consists of multiple agents, each using an LLM (Large Language Model) to control application flows. As systems grow, you may encounter challenges such as agents struggling with too many tools, overly complex contexts, or the need for specialized domain knowledge (e.g., planning, research, mathematics). Breaking down applications into multiple smaller, specialized agents often resolves these issues.
Modularity: Easier to develop, test, and maintain.
Specialization: Expert agents handle specific domains.
Control: Explicit control over agent communication.
Multi-agent systems can connect agents in several ways:
Network
Agents can communicate freely with each other, each deciding independently whom to contact next.
Assess communication efficiency, decision quality on agent selection, and coordination complexity.
Supervisor
Agents communicate exclusively with a single supervisor that makes all routing decisions.
Evaluate supervisor decision accuracy, efficiency of routing, and effectiveness in task management.
Supervisor (Tool-calling)
Supervisor uses an LLM to invoke agents represented as tools, making explicit tool calls with arguments.
Evaluate tool-calling accuracy, appropriateness of arguments passed, and supervisor decision quality.
Hierarchical
Systems with supervisors of supervisors, allowing complex, structured flows.
Evaluate communication efficiency, decision-making at each hierarchical level, and overall system coherence.
Custom Workflow
Agents communicate within predetermined subsets, combining deterministic and agent-driven decisions.
Evaluate workflow efficiency, clarity of communication paths, and effectiveness of the predetermined control flow.
There are a few different strategies for evaluating multi agent applications.
1. Agent Handoff Evaluation
When tasks transfer between agents, evaluate:
Appropriateness: Is the timing logical?
Information Transfer: Was context transferred effectively?
Timing: Optimal handoff moment.
2. System-Level Evaluation
Measure holistic performance:
End-to-End Task Completion
Efficiency: Number of interactions, processing speed
User Experience
3. Coordination Evaluation
Evaluate cooperative effectiveness:
Communication Quality
Conflict Resolution
Resource Management
Multi-agent systems introduce added complexity:
Complexity Management: Evaluate agents individually, in pairs, and system-wide.
Emergent Behaviors: Monitor for collective intelligence and unexpected interactions.
Evaluation Granularity:
Agent-level: Individual performance
Interaction-level: Agent interactions
System-level: Overall performance
User-level: End-user experience
Performance Metrics: Latency, throughput, scalability, reliability, operational cost
Adapt single-agent evaluation methods like tool-calling evaluations and planning assessments.
See our guide on agent evals and use our pre-built evals that you can leverage in Phoenix.
Focus evaluations on coordination efficiency, overall system efficiency, and emergent behaviors.
See our docs for creating your own custom evals in Phoenix.
Structure evaluations to match architecture:
Bottom-Up: From individual agents upward.
Top-Down: From system goals downward.
Hybrid: Combination for comprehensive coverage.