Multi-Agent Architecture Patterns

Multi-Agent Architecture Patterns

Multi-agent architectures distribute work across multiple language model instances, each with its own context window. When designed well, this distribution enables capabilities beyond single-agent limits. When designed poorly, it introduces coordination overhead that negates benefits. The critical insight is that sub-agents exist primarily to isolate context, not to anthropomorphize role division.

When to Activate

Activate this skill when:

• Single-agent context limits constrain task complexity
• Tasks decompose naturally into parallel subtasks
• Different subtasks require different tool sets or system prompts
• Building systems that must handle multiple domains simultaneously
• Scaling agent capabilities beyond single-context limits
• Designing production agent systems with multiple specialized components

Do not activate this skill for adjacent work owned by other skills:

• Deciding task-model fit, pipeline shape, or project-level cost before topology is known: project-development.
• Designing hosted sandboxes, warm pools, remote sessions, or background runtime infrastructure: hosted-agents.
• Sharing orchestrator state through KV-cache compaction in controlled runtimes: latent-briefing.
• Designing the tools each agent exposes: tool-design.

Core Concepts

Use multi-agent patterns when a single agent's context window cannot hold all task-relevant information. Context isolation is the primary benefit — each agent operates in a clean context without accumulated noise from other subtasks, preventing the telephone game problem where information degrades through repeated summarization.

Choose among three dominant patterns based on coordination needs, not organizational metaphor:

• Supervisor/orchestrator — Use for centralized control when tasks have clear decomposition and human oversight matters. A single coordinator delegates to specialists and synthesizes results.
• Peer-to-peer/swarm — Use for flexible exploration when rigid planning is counterproductive. Any agent can transfer control to any other through explicit handoff mechanisms.
• Hierarchical — Use for large-scale projects with layered abstraction (strategy, planning, execution). Each layer operates at a different level of detail with its own context structure.

Design every multi-agent system around explicit coordination protocols, consensus mechanisms that resist sycophancy, and failure handling that prevents error propagation cascades.

Detailed Topics

Why Multi-Agent Architectures

The Context Bottleneck Reach for multi-agent architectures when a single agent's context fills with accumulated history, retrieved documents, and tool outputs to the point where performance degrades. Recognize three degradation signals: the lost-in-middle effect (attention weakens for mid-context content), attention scarcity (too many competing items), and context poisoning (irrelevant content displaces useful content).

Partition work across multiple context windows so each agent operates in a clean context focused on its subtask. Aggregate results at a coordination layer without any single context bearing the full burden.