AI agents can be unpredictable. Your control layer shouldn't be.

In multi-agent systems, agents can be individually correct and still produce a wrong result when they work together. One agent’s output becomes another agent’s context. Information can be lost or misinterpreted during a handoff, shared state can drift, and agents can get stuck in loops or deadlocks. When several agents rely on the same underlying model, they may even reinforce the same mistakes rather than catch them.

This makes multi-agent systems fundamentally different from single-agent applications. Looking at each agent independently, or simply checking the final answer, does not tell us whether the collaboration itself worked.

The solution is to evaluate what happens between agents. We need to see how information moves through the workflow, how agents hand off results, how shared state changes, and whether different agents actually provide independent reasoning.

This article presents a practical approach to evaluating these interactions and using the results to build stronger engineering controls. The goal is not to make LLMs deterministic, but to make their unpredictable behavior visible, measurable, bounded, and recoverable.

Why agent collaboration needs evals

Large language models (LLMs) are inherently nondeterministic. In a multi-agent system, this uncertainty does not disappear. One agent generates information, another agent consumes and interprets it, and its output may then become the context for the next agent. Any falsely generated or interpreted content propagates and amplifies across the entire workflow.

Multi-agents are essentially consuming context from one another. We can evaluate it like a retrieval-augmented generation (RAG) system. Agent-to-agent interactions can follow the same principles and methodology.

A typical RAG pipeline can be decomposed as:

User query → retriever → retrieved context (eval) → generation → generated answer (eval)

We evaluate content retrieval and generation separately to identify whether the problem originated in retrieval, context quality, or generation.

A multi-agent system extends this idea:

User query → agent A → handoff → agent B → shared state → agent C → verification → final output

Evaluating only the final response — or even evaluating every agent independently — does not tell us whether the collaboration itself worked correctly. We need visibility into what happens between agents.

Capture the multi-agent execution trace

Before evaluating collaboration, we need to observe the complete workflow. Just as LangSmith can capture prompts, retrieved documents, tool calls, and model responses for a RAG application, multi-agent tracing should capture:

• Which agents were invoked and in what order

• Inputs and outputs of each agent

• Agent-to-agent handoff payloads

• Routing and delegation decisions

• Shared-state reads and updates

• Tool calls and results

• Retries, loops, and termination decisions

• Latency, token usage, and cost at each step

The trace effectively becomes the execution history of the multi-agent workflow. Without it, we may know that the final result failed but have little visibility into where the collaboration broke down.

Evaluate workflow deadlocks and loops

Unlike a single-agent application, a multi-agent system is a distributed workflow in which agents depend on one another to decide what happens next. Each agent may wait for information, approval, or an action from another agent. Because these dependencies are often expressed through LLM-driven decisions rather than fixed program logic, the workflow can enter states where no agent can make progress, or where agents repeatedly trigger one another.

Deadlocks occur when agents have circular dependencies. For example, an orchestrator waits for a worker agent to return a result, while the worker agent waits for confirmation or additional instructions from the orchestrator. Neither can proceed, so the workflow stalls indefinitely.

Infinite loops (Livelocks) occur when agents continue interacting but never make progress. For example, a knowledge agent may send a request back to a transaction agent for clarification, while the transaction agent repeatedly sends it back because the same clarification is still missing. The agents are active, but the workflow never reaches a decision, action, or terminal state.

These failures are properties of the execution graph, not necessarily of any individual agent. We therefore evaluate the workflow trace using:

These metrics allow us to determine whether the agent network is actually making progress and reaching a valid terminal state, rather than simply producing individually reasonable responses.

Evaluate agent handoffs

In a multi-agent system, the output of one agent becomes the input or context of another. Even if each agent performs well independently, the workflow can still fail if information is lost, changed, or incorrectly interpreted when it crosses the agent boundary.

We can think of each agent-to-agent handoff as a small information pipeline:

Agent A output → context passed to agent B → agent B interpretation

Similar to RAG evaluation, we need to evaluate both the quality of the retrieval and the generation of each agent. In addition, multi-agent systems require interface-level validation to ensure that the communication contract between agents is maintained.

Evaluate context passing

The first question is whether the correct information produced by the upstream agent actually reaches the downstream agent.

Typical questions include:

• Was all relevant information from agent A passed to agent B?

• Was any important information lost during the handoff?

• Was unnecessary or irrelevant information included?

• Does the context provided to agent B contain what it needs to perform its task?

The same context-quality metrics commonly used in RAG evaluation can be applied to the agent boundary:

Evaluate downstream interpretation

Passing the correct context is only half of the handoff. We also need to determine whether the downstream agent correctly understands and uses the information it receives.

Typical questions include:

• Is agent B’s output supported by the information received from agent A?

• Did agent B preserve the meaning of the upstream result?

• Did it introduce information that was not supported by the handoff?

• Did it make the correct decision based on the provided information?

The same generation-quality principles used in RAG evaluation can be adapted here:

LLM-as-a-judge is usually used for semantic evaluations. It compares the upstream output, the context received by the downstream agent, and the downstream result.

Evaluate interface integrity

Agent collaboration also introduces another evaluation layer that is less prominent in traditional RAG pipelines: The interface contract between agents.

Whenever possible, even with long text, agents should exchange structured outputs rather than unrestricted natural-language responses. JSON schemas, Pydantic models, or other typed contracts allow part of the handoff to be evaluated deterministically before the downstream agent executes.

Useful metrics include:

This gives us three complementary layers of handoff evaluation:

Agent A output → context quality → interface integrity → agent B interpretation

Context metrics tell us whether the right information was transferred. Interface validation tells us whether it was transferred in the expected structure. Generation metrics tell us whether the downstream agent correctly understood and acted on that information.

Evaluate shared state

Shared state is the persistent working memory of the entire agent workflow. Multi-agent workflows rely on shared state to carry customer information, conversation history, intermediate results, decisions, and constraints between agents. As the workflow progresses, this state can become stale, contradictory, incomplete, or increasingly noisy. 

State evaluation therefore treats each state transition as a checkpoint: Compare the state before and after an agent executes and verify that important information has been preserved correctly.

For example, consider the shared state of a SaaS customer-support workflow:

{   "customer_id": "C1024",   "plan": "Enterprise",   "issue": "duplicate_charge",   "identity_verified": true,   "refund_eligible": true,

  "refund reason": xxx long text }

The solution is to create a function called a state manager that can evaluate the updated state using two types of checks.

Deterministic checks validate properties that can be expressed as rules:

• Schema validation: Are required fields present and correctly typed?

• Immutable-field validation: Did an agent unexpectedly change customer_id or another protected field?

• Consistency checks: Does refund_eligible = true conflict with another state field or business rule?

• Freshness checks: Is an agent using an expired or superseded value?

• Context-size checks: Has conversation history or shared context exceeded a defined threshold?

Semantic checks evaluate changes that cannot be reliably captured by fixed rules. An LLM evaluator can compare the previous state, recent agent outputs, and updated state to determine:

• Were important facts or constraints lost?

• Did summarization change the meaning of the conversation?

• Was unsupported information introduced into the state?

• Does the updated state still represent the customer’s original request?

• Is irrelevant information accumulating and thus distracting downstream agents?

These checks can be measured with state-level KPIs:

• State validation rate: Percentage of state transitions passing schema and business-rule checks

• State consistency rate: Percentage of transitions without conflicting state values

• Constraint preservation rate: Whether important requirements survive across state transitions

• Summary fidelity: Whether compressed state preserves the meaning and critical facts of the original context

• Stale state rate: Frequency with which downstream agents consume outdated information

• Context growth: Growth of active state or token usage as the workflow progresses  

The state manager therefore functions similarly to a checkpoint in a data pipeline:

Previous state → agent execution → updated state → state evaluation → next agent

This allows us to evaluate not only whether individual agents produce correct outputs, but whether the shared understanding of the workflow remains accurate as information moves across multiple agents.

Evaluate correlated reasoning (model monoculture)

A key advantage of multi-agent systems is that specialized agents can validate each other’s reasoning. However, when multiple agents rely on the same foundation model, they may share similar reasoning patterns and blind spots. If a planner agent reaches a plausible but incorrect conclusion, a reviewer agent built on the same model may reinforce the error instead of detecting it. As a result, every agent may perform well individually while the overall system remains systematically wrong.

This failure exists not within any single agent, but in the lack of independence between agents. Detecting model monoculture therefore requires measuring whether agents actually provide diverse reasoning, evidence, decisions, and failure patterns.

• Output diversity: Whether agents generate similar responses for the same task — embedding cosine similarity, BERTScore, LLM-as-a-judge

• Reasoning diversity: Whether agents follow different reasoning paths — reasoning trace similarity, graph edit distance

• Tool diversity: Whether agents use different tools or workflows — tool usage entropy, tool distribution

• Retrieval diversity: Whether agents rely on different evidence — Jaccard similarity, document overlap

• Decision diversity: Whether agents consistently reach the same decisions — decision entropy, decision agreement rate

• Failure correlation: Whether agents fail on the same tasks — error agreement rate, pairwise failure correlation

• Perspective diversity: Whether agents approach the problem from different perspectives — LLM-based reasoning classification  

Among these metrics, failure correlation is particularly important. Different wording, reasoning traces, or retrieved documents do not necessarily mean that agents have different blind spots. If multiple agents repeatedly fail on the same benchmark cases, their apparent diversity provides little additional protection. A healthy multi-agent system should instead demonstrate complementary failure patterns, where one agent can detect or compensate for errors made by another.

From evaluation to engineering control

The evaluation framework discussed in this article ultimately aims to make a nondeterministic system more predictable and controllable. This leads to an important engineering principle: Agent collaboration should not rely entirely on LLM decisions. Because LLM behavior is probabilistic, deterministic controls should be embedded into the workflow to enforce the interfaces, execution rules, and state management that agents must follow.

Engineering agent handoffs. Define explicit interfaces using structured outputs such as JSON Schema or Pydantic models. Validate required fields, data types, and business rules before the downstream agent consumes the result. If validation fails, the system can reject the payload, retry the upstream agent, or route the workflow to an alternative path. This allows semantic reasoning to remain flexible while the communication interface remains deterministic.

Engineering workflow coordination. The orchestration layer should explicitly track agent invocations, routing decisions, handoffs, iterations, execution time, and terminal states. This is where deterministic orchestration becomes important. Frameworks such as LangGraph allow workflows to be represented as stateful graphs with explicit nodes, edges, branches, and checkpoints. Deterministic controls such as maximum iterations, timeouts, and termination conditions can prevent deadlocks and infinite loops rather than relying entirely on an LLM to decide when to stop. The execution trace can then be captured with LangSmith, providing the visibility needed to evaluate workflow progression and diagnose abnormal execution.

Engineering state management. Shared state should be managed by a state manager rather than passed between agents indefinitely.

Agent execution → state manager → state update / pruning → next agent

The state manager uses deterministic rules to monitor context size and determine when pruning is required. When a threshold is reached, it invokes a lightweight summarization agent to compress the conversation. The state manager then deterministically replaces older messages with the summary while retaining recent context. This creates a hybrid model: The LLM performs semantic summarization, while deterministic code controls when and how the state changes. This keeps the shared state compact and prevents context from growing indefinitely.

Engineering model selection: The first and most important principle is not to use a single foundation model for every agent. Different agents have different responsibilities and therefore different requirements for reasoning capability, latency, cost, and reliability.

For example, a SaaS customer-support system might use:

• Router/triage agent: A fast, low-cost model for classification and routing.

• Knowledge/RAG agent: A model that performs well at context understanding and grounded generation.

• Transaction agent: A stronger reasoning model for interpreting policies and determining whether an action is allowed.

• Reviewer agent: A different model family to reduce shared reasoning blind spots.

Model selection should be empirical rather than based on model reputation. Create an evaluation dataset for each agent’s specific task, run candidate models against the same test cases, and compare task-specific metrics such as accuracy, faithfulness, tool-calling success, latency, and cost. The goal is not to find the best foundation model for the entire system, but to select the best model for each agent’s specific responsibility while maintaining meaningful diversity where independent verification is required.

These engineering practices turn the evaluation framework into a production architecture. Structured interfaces control information flow, orchestration controls workflow execution, state management controls shared context, and model selection controls the diversity and capability of the agents. Evaluation then provides the feedback needed to continuously verify that these controls are working as intended.

Ultimately, building a reliable multi-agent system is not simply about adding more capable agents. It is more about engineering the boundaries between them — making those boundaries observable, measurable, deterministic where necessary, and resilient when an agent makes a mistake.

Shuhua Xu is a lead data engineer.

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