Multi-Agent Orchestration: Patterns That Scale

Building a single capable agent is hard. Building a system of ten agents that coordinate reliably is an order of magnitude harder. Too many promising agentic systems collapse under their own complexity — not because the individual agents are poorly designed, but because the orchestration layer is an afterthought.

This post covers the patterns that make multi-agent systems actually scale.

The Three Failure Modes

Before discussing patterns, it's worth naming what we're designing against:

1. Cascade failure — one agent's error propagates unchecked, corrupting downstream agents' state
2. Resource contention — agents competing for shared resources without coordination mechanisms
3. Context pollution — agents passing unstructured state between each other, leading to compounding hallucinations

Every pattern below addresses one or more of these failure modes.

Pattern 1: The Supervisor-Worker Hierarchy

The most common and battle-tested pattern. A supervisor agent holds the high-level task and decomposes it into sub-tasks assigned to specialized workers. Workers operate in isolation and return structured outputs — they have no knowledge of each other.

The supervisor handles failure: if a worker fails or returns an unexpected result, the supervisor can retry, reassign, or escalate. This isolation means cascade failure is contained at the supervisor boundary.

When to use it: Any workflow with clearly decomposable subtasks. Order processing, content generation pipelines, data enrichment workflows.

Pattern 2: The Blackboard Architecture

Named after the classic AI architecture from the 1970s. A shared, structured state store (the "blackboard") is the only way agents communicate. No direct agent-to-agent messaging. Each agent reads the current state, contributes its output, and writes back.

This solves context pollution because the blackboard schema is typed and validated. An agent can only write what the schema allows. It also makes the system trivially debuggable — the blackboard state at any point in time is the complete picture of what happened.

When to use it: Workflows where multiple agents need to converge on a shared artifact — document analysis, research workflows, multi-step data transformation.

Pattern 3: Event-Driven Choreography

Rather than a central orchestrator, agents react to events in a message bus. Each agent subscribes to events it cares about, performs its work, and emits new events. The workflow emerges from these event chains.

This pattern is highly scalable — you add new agents by adding new event subscriptions without touching existing agents. The downside is debuggability: tracing a failed workflow through an event chain requires robust distributed tracing infrastructure.

When to use it: High-throughput workflows where individual task execution time varies widely. E-commerce event processing, real-time data pipelines, async notification systems.

Memory Architecture

All agents in a multi-agent system need memory, but not all memory is the same:

• Working memory: The context window — what the agent is reasoning about right now
• Episodic memory: A record of past actions and outcomes, queryable by the agent
• Semantic memory: A vector store of domain knowledge that agents can retrieve

The most common mistake is over-relying on working memory. A well-designed agent should be stateless between invocations, reconstructing what it needs from episodic and semantic memory. This makes agents independently restartable and dramatically easier to debug.

The Non-Negotiables

Whatever pattern you choose, these are non-negotiable:

• Every agent action must be logged with a timestamp, inputs, outputs, and a unique trace ID
• All inter-agent communication must be schema-validated — never pass raw strings
• Every agent must have a maximum execution time and a defined behavior when it exceeds that limit
• Human escalation paths must be built in from day one, not added later

Multi-agent systems that ignore these principles work fine in demos and fail in production. Build the observability infrastructure before you build the agents.