mediar-ai Multi-Agent Architecture Patterns
Design multi-agent systems for distributing work across multiple context windows.
Prerequisites
- Understanding of context limitations
- Familiarity with agent frameworks
Instructions
Core Insight
Sub-agents exist primarily to isolate context, not to anthropomorphize role division. Each sub-agent gets a clean context focused on its subtask.
Token Economics
| Architecture | Token Multiplier |
|---|---|
| Single chat | 1× baseline |
| Single + tools | ~4× baseline |
| Multi-agent | ~15× baseline |
Pattern 1: Supervisor/Orchestrator
Central agent delegates to specialists and synthesizes results.
User -> Supervisor -> [Specialist, Specialist] -> Aggregation -> Output
When to use: Clear decomposition, coordination across domains, human oversight needed.
Pitfall: "Telephone game" where supervisor paraphrases incorrectly. Fix with forward_message tool for direct responses.
Pattern 2: Peer-to-Peer/Swarm
Agents communicate directly with explicit handoffs.
def transfer_to_agent_b():
return agent_b # Handoff via function return
When to use: Flexible exploration, emergent requirements, no rigid planning.
Pattern 3: Hierarchical
Layers of abstraction: Strategy → Planning → Execution.
When to use: Large-scale projects, enterprise workflows, clear hierarchy.
Context Isolation Mechanisms
- Full context delegation: Complex tasks needing full understanding
- Instruction passing: Simple, well-defined subtasks
- File system memory: Complex tasks with shared state
Consensus Mechanisms
- Weighted voting: Weight by confidence or expertise
- Debate protocols: Adversarial critique across rounds
- Trigger intervention: Detect stalls or sycophancy
Guidelines
- Design for context isolation as primary benefit
- Choose pattern based on coordination needs, not metaphor
- Implement explicit handoff protocols with state passing
- Use weighted voting or debate for consensus
- Monitor for supervisor bottlenecks
Notes
- Model upgrades often beat token increases for performance
- Swarm slightly outperforms supervisor with direct responses
- Validate outputs before passing between agents
Source: muratcankoylan/Agent-Skills-for-Context-Engineering