Why single agents hit a ceiling
A single LLM agent with tool access can accomplish remarkable things. But it has structural limitations that become painfully obvious at scale. Context window constraints mean a single agent can only hold so much state at once — for long-running workflows, it begins to lose track of earlier steps. Single agents are also serial: they complete one sub-task before starting the next, which makes complex pipelines slow. And a single agent can't specialise — it's a generalist at every part of the task. Multi-agent architectures address all three limitations. They distribute work across specialised agents, run tasks in parallel where dependencies allow, and keep each agent's context focused and manageable. The tradeoff is coordination overhead, which must be designed for explicitly.
Pattern 1: Orchestrator-Worker
This is the most commonly used pattern and the right starting point for most multi-agent systems. An orchestrator agent receives the high-level task, decomposes it into sub-tasks, dispatches sub-tasks to specialised worker agents, collects their outputs, and synthesises the final result. Workers have no awareness of each other — they receive instructions from the orchestrator and return results. Example: a competitive intelligence pipeline. The orchestrator receives 'produce a competitive analysis of Company X.' It spawns a web research agent (which searches and scrapes), a financial data agent (which queries a financial API), a news summarisation agent (which processes recent press), and a synthesis agent (which produces the final document). Each worker runs independently; the orchestrator manages sequencing and aggregation. This pattern works best when sub-tasks are clearly separable and the orchestrator's task decomposition logic is stable.
Pattern 2: Peer-to-Peer with a Shared State Store
In peer-to-peer architectures, agents communicate directly with each other rather than through a central orchestrator. A shared state store (typically a structured document or a key-value store that all agents can read and write) coordinates the work. Each agent monitors the state store, picks up tasks that match its capabilities, updates the state when it completes work, and triggers downstream agents by updating state fields they're watching. This pattern is more resilient than orchestrator-worker — there's no single point of failure. It also handles emergent workflows better: when the exact sequence of steps can't be determined upfront, agents can react to state changes dynamically. The tradeoff is that debugging is harder, state conflicts require explicit resolution logic, and it's easier to end up with agents working at cross purposes if the state schema isn't carefully designed.
Pattern 3: Hierarchical Multi-Agent Systems
For very large task surfaces — think an autonomous operations agent that can manage an entire department's workflow — hierarchical architectures add layers of management. A top-level strategic agent sets goals and constraints, mid-level coordinator agents manage domains (one for finance, one for HR, one for customer operations), and specialist worker agents handle individual tasks. Hierarchical systems can handle genuine organisational complexity, but they're expensive to build and maintain. Every layer adds latency and failure modes. We recommend reaching this pattern only when orchestrator-worker or peer-to-peer architectures have been proven insufficient — typically when you have more than 8–10 distinct agent roles or when coordination logic becomes too complex for a single orchestrator to manage reliably.
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