Building and evaluating multi-agent applications

A multi-agent application should be built only when specialization, isolation, or parallel work provides measurable value over one agent with tools. Evaluation must test that claim. The right benchmark compares the multi-agent design with a simpler baseline on task success, coordination failures, latency, and cost.

Pick an architecture for a concrete reason

Common patterns include a router that selects a specialist, a supervisor that delegates dynamically, handoffs that change the active agent, and a custom graph that mixes deterministic steps with agents.

Use a router when categories are clear. Use a supervisor when task decomposition depends on evolving context. Use handoffs when conversation ownership changes. Use a deterministic workflow when the order is known. LangChain's official multi-agent documentation describes routing and supervisor tradeoffs; LangGraph provides the low-level stateful runtime.

Instrument ownership and handoffs

Create a root trace for the user task. Each agent invocation becomes a named span or nested trace with its input context, output, tool calls, model, and cost. Record every handoff with source agent, target agent, reason, and state fields transferred.

Stable names make aggregate analysis possible:

request (trace)
├── router.classify (generation)
├── agent.research (span)
│   ├── tool.search (span)
│   └── synthesize (generation)
└── agent.finalize (generation)

Do not pass the entire transcript by default. Explicitly define the context each agent needs and trace what was transferred. Context bloat is both a cost problem and a source of conflicting instructions.

Build a layered eval suite

Test components before the whole system:

1. Routing: Was the appropriate agent or path selected?
2. Handoff: Were constraints and relevant state preserved?
3. Specialist: Did the agent complete its bounded task?
4. Synthesis: Did the final response reconcile outputs correctly?
5. System: Was the user's goal completed within safety and resource limits?

Include ambiguous requests, tasks requiring multiple specialists, unavailable agents, tool timeouts, contradictory findings, and requests that should remain single-agent.

Measure coordination directly

Useful trajectory metrics include wrong-agent rate, unnecessary handoffs, duplicate tool calls, dropped constraints, circular delegation, context tokens, and steps per successful task. Pair these with outcome quality.

An automated judge can score semantic decomposition and synthesis, but use deterministic evaluators for selected agents, tool results, budgets, and graph termination. Inspect regressions through their traces.

Compare against a simpler baseline

Run the same dataset through one agent with the same tools. A multi-agent system earns its complexity only if it improves a relevant metric enough to justify additional latency, tokens, and operational failure modes.

Segment results. Parallel specialists may help research tasks while harming simple support questions. Route only the former into the complex path.

Test failure and recovery

Inject timeouts, malformed outputs, missing permissions, and partial state. Evaluate whether the supervisor retries sensibly, chooses an alternative, asks for help, or terminates with an honest partial result. Set maximum handoffs, steps, time, and cost.

Improve from production behavior

Online traces reveal routing categories and combinations absent from the initial suite. Cluster recurring handoffs, expensive paths, and user corrections. Review representative failures and add them to the offline dataset.

Currai's trace hierarchy captures the whole multi-agent tree, while evaluation scores can attach to the root or any component. Combined with agent harness engineering, this creates a disciplined path from architecture idea to production evidence.