What Is a Multi-Agent System?

What Is a Multi-Agent System?

A multi-agent system is an agent architecture where two or more LLM-driven agents collaborate, each with its own role, prompt, tools, or memory, to complete one shared task. The agents coordinate through explicit handoffs, shared state, or a manager/group-chat orchestrator that decides whose turn it is. In FutureAGI, it appears as a production trace with per-agent sub-traces, handoff spans, and one joint outcome. Frameworks such as CrewAI, AutoGen, Google ADK, LangGraph, and the OpenAI Agents SDK implement the pattern differently, but the reliability problem is the same: many agents, one outcome.

Why multi-agent systems matter in production LLM and agent systems

Single agents hit a ceiling. A monolithic agent with twenty tools and ten responsibilities ends up with a bloated system prompt, brittle tool selection, and context that overflows long before the trajectory ends. Splitting the work into specialists — a researcher, a writer, a reviewer — usually outperforms the monolith on accuracy and is dramatically easier to debug. The trade-off is coordination cost: now you have inter-agent handoffs, shared memory, and cross-agent traces to keep coherent.

The pain shows up in specific ways. A backend engineer sees a CrewAI run where the researcher returned facts but the writer ignored them — a handoff payload bug, not a model bug. An SRE watches an AutoGen group chat loop between two agents that keep deferring to each other. A product lead reviews logs where one trace ran ten agents for a task that needed two — fan-out without a budget cap. Without per-agent observability, all these read as “the AI is bad.”

In 2026, multi-agent is now the default for non-trivial agentic products. Customer-support agents have a triage agent and a resolver agent. Coding agents have a planner, a coder, and a reviewer. Voice agents have an STT agent and a routing agent. Every one of these needs cross-agent tracing, per-agent evaluation, and joint-outcome scoring — none of which a single-agent observability story provides.

How FutureAGI handles multi-agent systems

FutureAGI’s approach is per-agent traces stitched into one joint trajectory. The traceAI integrations cover the major multi-agent frameworks: traceAI-crewai instruments CrewAI agents and tasks, traceAI-autogen instruments AutoGen group chats, traceAI-google-adk instruments Google ADK sub-agents, and traceAI-langgraph covers LangGraph multi-agent graphs. Each agent’s work appears as a sub-trace; each handoff appears as a cross-agent span with the source agent name, target agent name, and payload. The agent.trajectory.step attribute identifies which agent the step belongs to, so dashboards can slice fail rate by agent role.

Evaluation runs at two levels. Per agent, you evaluate that agent’s sub-trajectory with ToolSelectionAccuracy, ReasoningQuality, or a role-specific custom evaluator — “did the researcher return five distinct sources?” Per team, you run TaskCompletion and TrajectoryScore against the joint trace to grade the outcome.

Concretely: a team running a CrewAI research crew — researcher, writer, reviewer — instruments with traceAI-crewai. They notice end-to-end TaskCompletion is 72% but TrajectoryScore shows the researcher node passes 95% of the time and the writer fails 23%. Compared with a plain LangSmith run tree or raw OpenTelemetry trace, the useful unit is not just a span; it is the agent-role slice that explains which specialist failed. They tune the writer prompt only, leave the researcher untouched, and TaskCompletion climbs to 88%. The fix took an hour because the trace localized the failure to one agent.

How to measure or detect a multi-agent system

Always measure the joint outcome and the per-agent contribution — one without the other hides bugs:

• TaskCompletion: returns 0–1 plus reason for the team’s joint goal.
• TrajectoryScore: aggregates per-step (and per-agent) scores into a trajectory rating.
• GoalProgress: useful when binary success at the team level is too coarse.
• per-agent eval-fail-rate (dashboard signal): the % of traces in which each named agent failed its role-specific evaluator.
• handoff-failure rate (dashboard signal): the % of handoffs where the receiving agent dropped state or rejected payload.
• p99 latency by agent role (dashboard signal): detects one slow specialist hiding inside a healthy-looking team trace.
• token-cost-per-team-run (dashboard signal): catches fan-out where extra agents add cost without improving TaskCompletion.
• agent.trajectory.step (OTel attribute): combined with the agent name, lets you filter trace views to one agent’s slice.

Minimal Python:

from fi.evals import TaskCompletion, TrajectoryScore

team_score = TaskCompletion().evaluate(
    input=user_goal,
    trajectory=joint_trace_spans,
)
print(team_score.score, team_score.reason)

Common mistakes

• Conflating multi-agent with handoff. Multi-agent is the architecture; handoff is one coordination protocol inside it. Not all multi-agent systems use explicit handoffs — some share state, some use a manager.
• Splitting one agent into many for show. If two agents have the same tools and prompt and just take turns, you’ve added overhead without specialisation. Roles must differ.
• Measuring only the joint outcome. A 70% team success rate hides which agent is the weak link; always evaluate per agent too.
• Letting agents share unbounded memory. Cross-agent context bleed creates subtle coupling that breaks when one agent’s prompt changes — keep handoff payloads explicit and typed.
• No max-turn cap on group chats. AutoGen group chats and similar will loop forever between two agents without a hard turn limit.