Every agent in a multi-agent system can be correct on its own, and the system can still give a wrong answer. Information gets lost in handoffs, shared state drifts, and agents loop or deadlock. This guide, adapted from a guest article by lead data engineer Shuhua Xu, shows how to test the connections between agents. If you are new to the topic, start with what agentic AI is.
The goal is not to make LLMs deterministic. It is to make their behavior visible, measurable, bounded, and recoverable.
Why Agent Collaboration Needs Its Own Evals
LLMs are nondeterministic, and in a multi-agent system that uncertainty compounds. One agent's output becomes the next agent's context, so a false or misread statement spreads through the whole workflow.
The author's key idea is to treat agent-to-agent traffic like a retrieval-augmented generation (RAG) pipeline. RAG teams already evaluate retrieval and generation separately to find where a failure started. Multi-agent systems extend that: agent A, handoff, agent B, shared state, agent C, verification, final output. Checking only the final answer, or each agent alone, cannot show whether the collaboration worked.
Step 1: Capture the Full Execution Trace
You cannot evaluate what you cannot see. A useful trace records:
- Which agents ran, and in what order
- Inputs, outputs, and handoff payloads for each agent
- Routing decisions, tool calls, and shared-state reads and writes
- Retries, loops, termination decisions, plus latency, tokens, and cost per step
Step 2: Test for Deadlocks and Loops
A deadlock happens when agents wait on each other, such as an orchestrator waiting for a worker that is waiting for the orchestrator. A livelock is when agents keep exchanging messages but never progress, such as two agents bouncing the same clarification request back and forth.
| Category | Example Metrics |
|---|---|
| Progression | Workflow completion rate, execution time, average iterations |
| Coordination | Deadlock rate, infinite loop rate, average handoffs |
| Termination | Valid termination rate, max iterations reached, abandoned workflow rate |
Step 3: Evaluate Handoffs in Three Layers
1. Context passing
Did the downstream agent receive what it needs, without noise? Borrow RAG metrics: context precision, context recall, and context relevance.
2. Downstream interpretation
Did agent B understand and use it correctly? Check faithfulness (is the output supported by what it received), output relevancy, and response correctness where ground truth exists. An LLM-as-a-judge usually handles these semantic comparisons.
3. Interface integrity
Have agents exchange structured outputs, such as JSON schemas or Pydantic models, so part of the handoff can be checked by code. Track schema validity rate, required-field completeness, type validation rate, and handoff success rate.
Step 4: Evaluate Shared State
Shared state is the workflow's working memory: customer details, decisions, constraints, and history. It can go stale, contradict itself, or fill with noise. Treat each state change as a checkpoint and compare the state before and after every agent runs, using a state manager.
- Deterministic checks: schema validation, protected fields that must not change (such as a customer ID), business-rule consistency, freshness, and context-size limits.
- Semantic checks (LLM evaluator): Were key facts lost? Did a summary change the meaning? Was unsupported information added? Does the state still reflect the original request?
- KPIs: state validation rate, consistency rate, constraint preservation, summary fidelity, stale state rate, and context growth.
Step 5: Detect Model Monoculture
If a planner and a reviewer run on the same foundation model, they may share blind spots, so the reviewer reinforces the planner's mistake. Measure diversity in outputs, reasoning paths, tool use, retrieved evidence, decisions, and perspectives.
From Evaluation to Engineering Controls
- Handoffs: Validate payloads before the next agent consumes them. On failure, reject, retry the upstream agent, or reroute.
- Coordination: Use deterministic orchestration, such as stateful graphs in LangGraph, with maximum iterations, timeouts, and termination rules. Trace runs with LangSmith. Do not rely on an LLM to decide when to stop.
- State: Let a state manager monitor size, call a lightweight summarizer when a threshold is hit, and deterministically swap old messages for the summary.
- Models: Do not use one model everywhere. In a support workflow, use a fast model for routing, a grounded model for retrieval, a stronger reasoner for policy decisions, and a different model family for review.
Who Benefits and What You Need
This approach suits teams running two or more cooperating agents, such as support, finance, or coding workflows. You need tracing (the article names LangSmith), a structured handoff format, a test set with expected results, and an LLM judge for semantic checks. For a broader view of building automation pipelines, see our complete AI workflow automation guide.
Why This Matters
Adding more capable agents does not by itself make a system reliable. Reliability comes from engineering the boundaries between agents so they are observable, measurable, deterministic where necessary, and resilient to mistakes. This is a practitioner's framework, not a benchmarked study, and it reports no performance numbers. Treat it as a checklist to adapt. For where human sign-off still fits, read our piece on AI oversight and regulation.
Official source: Guest article by Shuhua Xu, lead data engineer, published through VentureBeat's guest posting program. Tools named: LangGraph and LangSmith.
Frequently Asked Questions (FAQ)
Why isn't testing each agent separately enough?
Failures often happen between agents, through lost context, misread handoffs, stale state, or loops. Individual tests cannot see those.
What is the difference between a deadlock and a livelock?
In a deadlock, agents wait on each other and nothing moves. In a livelock, agents keep interacting but never reach a decision or end state.
What is a state manager?
It is code that checks shared state after each agent runs, applies rules, and prunes or summarizes context when it grows too large.
What is model monoculture?
It is when several agents use the same foundation model and share blind spots, so they miss the same errors.
Should every agent use a different model?
Not necessarily. Choose each model by testing it on that agent's task, and keep diversity where independent verification matters, such as the reviewer.
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