A single AI agent searches, reasons and acts within one context window. A multi-agent system splits the work between a lead agent that plans and several specialized subagents that run in parallel. Anthropic measured that this architecture beats a single agent by 90.2% on its own research eval, at the cost of roughly 15 times more tokens. For a Belgian company moving from demos to production, the real question is not whether several agents beat one, but when orchestration is worth the cost, and how to govern it.
An AI agent is a language model that uses tools in a loop to reach a goal: it searches, reads a result, decides the next step, repeats. As long as the task fits in one context window and follows a single thread, one agent is enough. Multi-agent starts when you hand the task to several agents that split the work and move in parallel.
Anthropic describes the most common pattern: a lead agent analyzes the request, sets a strategy, then spawns specialized subagents that each explore an angle in their own context window before returning a synthesis. That is the orchestrator-worker pattern. Its benefit is precise: each subagent compresses a lot of information into few useful tokens, which goes beyond the limits of a single context.
One thread, one context window. Simple to build, follow and debug. Suited to sequential tasks where each step depends on the previous one.
Several agents in parallel, coordinated by a lead agent. Suited to broad tasks, with a high volume of information, where the sub-questions are independent.
Coordinating agents is not a single recipe. A few patterns recur in frameworks and production systems, from the simplest to the hardest to control.
A lead agent breaks down the task, delegates to subagents and synthesizes their answers. This is the orchestrator-worker pattern, the most common in production.
most commonAgents pass the baton in a fixed order, each processing the previous one's output. Readable and predictable, like an assembly line.
chained stepsSupervisors of supervisors: a lead coordinates teams of agents. Useful when subtasks split further down.
teams of teamsAgents talk directly to each other, with no single lead. Powerful, but harder to control and trace.
peer to peerThe gains are not theoretical. On its internal research eval, Anthropic found that a system with Claude Opus 4 as the lead agent and Claude Sonnet 4 subagents beat a single-agent Claude Opus 4 by 90.2%. The advantage is clear on broad questions, where several independent leads must be followed at once.
Parallelization also changes response time. By spinning up 3 to 5 subagents in parallel rather than serially, Anthropic cut research time by up to 90% on complex queries. The dominant factor is no mystery: in its analysis, token usage alone explains 80% of the performance gap. The more a system can spend tokens wisely, the better it answers.
Tasks with heavy parallelization, where information exceeds a single context window and many tools must be queried. Research, monitoring, collecting and cross-checking sources are the typical examples. Anthropic cites a broad question where the single agent fails through sequential search, while the multi-agent system finds the answer by splitting the work.
This power has a direct price. Still according to Anthropic, an agent uses about 4 times more tokens than a plain chat, and a multi-agent system about 15 times more. Orchestration is therefore only justified for tasks whose value covers that extra cost.
Multi-agent is not always the right choice. When all agents must share the same context, or when subtasks depend heavily on one another, coordination costs more than it returns. Anthropic notes that most coding tasks, less parallelizable than research, are a poor fit today.
Gartner forecasts that over 40% of agentic AI projects will be canceled by the end of 2027, for lack of clear value, controlled costs or risk controls. A poorly scoped system multiplies tool calls, tokens and unexpected behavior: one of the first failure modes at Anthropic was spawning 50 subagents for a simple query. The rule stays the same: start small, measure the cost per task, cap it.
For agents to cooperate, two kinds of connections are needed: linking an agent to tools and data, and linking agents to each other. Two open standards took hold very fast in 2025.
The Model Context Protocol, published by Anthropic in late 2024, standardizes the connection between a model and tools or data sources. OpenAI adopted it in March 2025, followed by Google and Microsoft. It became the common port between agents and tools.
Agent2Agent, announced in April 2025 with more than 50 partners, lets agents from different frameworks and vendors communicate and delegate tasks. Handed to the Linux Foundation in June 2025, it aims for neutral governance.
The practical issue is interoperability. An agent from your CRM, an agent from your ERP and an in-house agent do not speak the same language by default. MCP gives them common access to tools, A2A lets them coordinate. Without that layer, every integration stays fragile, custom-built and costly to maintain.
On the tooling side, the offering has grown fast. No framework dominates, and the choice mostly depends on your tech stack and your production constraints.
Orchestration through a state graph, with fine control over transitions between agents. Popular for complex flows.
Agents organized into crews with roles and tasks. A simple, readable approach to get started.
Open-source SDK released in October 2025, bringing together Semantic Kernel and AutoGen. Production-oriented, on .NET and Python.
Google's Agent Development Kit (April 2025) and OpenAI's Agents SDK (March 2025, from Swarm) tool the building of multi-agent systems.
Broad, repetitive, high-value tasks: competitive monitoring, document analysis, cross-checking sources, preparing case files. There, orchestration pays back its token overhead.
Simple, sequential tasks, or ones that depend heavily on a shared context. A single well-equipped agent is faster, cheaper and easier to trace.
A multi-agent system commits tool calls, data and sometimes spending. Flows pass through several models, possibly remote. Controlling where the data goes and who sees what becomes central. A rollout that respects the GDPR and the EU AI Act, on infrastructure you control, stays simpler to govern than an opaque assembly of third-party services. Every agent that acts must be traceable, capped and supervised, by design.
Broad, parallelizable, high-value. If a single agent is enough, stop there.
Lead agent, specialized subagents, the scope of each. Who decides, who executes, who validates.
MCP for tools, A2A if several frameworks coexist. Log every call for the audit.
Track tokens per task, cap them, supervise sensitive cases, document for the regulator.
Molderez Consult helps Belgian companies decide when multi-agent orchestration is worth the cost, choose patterns, protocols and frameworks, and deploy the whole with traceability and governance, on infrastructure they control.
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