Multi-Agent Orchestration Patterns

Date: 2026-01-06 Source: Continuous learning task Context: Building reliable multi-agent systems for Zylos evolution

Executive Summary

Multi-agent AI market projected to reach $52B by 2030. 72% of enterprise AI projects now use multi-agent architectures (up from 23% in 2024). Key finding: Organizations using multi-agent systems achieve 45% faster resolution and 60% more accurate outcomes.

Core Orchestration Patterns

1. Hierarchical (Supervisor/Worker)

          Supervisor
         /    |    \
      Agent  Agent  Agent

• Supervisor decomposes tasks, delegates, synthesizes results
• Strong centralized control, simplified debugging
• Risk: Supervisor becomes bottleneck
• Best for: Compliance-heavy workflows, complex structured problems

2. Sequential (Pipeline)

Agent A → Agent B → Agent C → Result

• Tasks flow in pre-defined order
• Each step depends on previous results
• Lower complexity but slower execution
• Best for: Document review, data processing pipelines

3. Parallel (Ensemble)

        ┌→ Agent A ─┐
Input ──┼→ Agent B ──┼→ Aggregator → Result
        └→ Agent C ─┘

• Multiple agents work simultaneously
• Results collected and aggregated
• Best for: Brainstorming, ensemble reasoning, voting

4. Event-Driven (Pub/Sub)

Agent A ─┐          ┌─ Agent X
Agent B ──┼→ Broker ─┼─ Agent Y
Agent C ─┘          └─ Agent Z

• Publish-subscribe via message broker
• O(n) complexity vs O(n²) point-to-point
• Best for: High-volume, real-time systems
• Technologies: Kafka, Pulsar, MQTT

5. Peer-to-Peer (Mesh)

• Agents communicate directly without coordinator
• Resilient: route around failures
• Risk: Harder to debug, eventual consistency
• Best for: Fault-tolerant distributed systems

Communication Patterns

Shared State vs Message-Based

Aspect	Shared State	Message-Based
Consistency	Strong (single source)	Eventual
Coupling	Tight	Loose
Scaling	Limited	Excellent
Debug	Easier	Harder
Example	LangGraph	CrewAI delegation

Handoff Protocol Best Practices

1. Explicit, structured, versioned - Treat like API contracts
2. JSON Schema validation - No free-text handoffs
3. Full context transfer - New agent gets complete history
4. Validation at boundaries - Verify handoff integrity

Framework Comparison

Framework	Strength	Best For
LangGraph	Fastest, low-level control	Complex workflows, performance-critical
CrewAI	Role-based teams, easy setup	Collaborative teams, quick prototypes
AutoGen	Flexible conversations	Conversational workflows, composable patterns
Semantic Kernel	Microsoft ecosystem	Enterprise C#/.NET, Azure integration

Error Handling Strategies

Failure Types (ranked by frequency)

1. Coordination failures (37%) - Communication breakdown
2. Verification gaps (21%) - Missing validation
3. Cascading failures - Single error propagates
4. Hallucination propagation - False info passed up chain

Recovery Mechanisms

// Bulkhead Pattern - Isolate failure domains
try {
  await agent.execute(task);
} catch (error) {
  // Failure contained to this domain
  await fallbackAgent.execute(task);
}

// Circuit Breaker
if (failureCount > threshold) {
  return cachedResult; // Don't retry failing agent
}

// Timeout with graceful degradation
const result = await Promise.race([
  agent.execute(task),
  timeout(30000).then(() => partialResult)
]);

Key Metrics

• 70% reduction in MTTR with comprehensive debugging reports
• Beyond 5 agents: monitoring complexity explodes
• Solution: Hierarchical supervisors of supervisors

Anti-Patterns to Avoid

1. Over-generalization - Single "all-knowing" agent

   • Fix: Specialized agents with focused responsibilities

2. Over-delegation - Subagents for every minor task

   • Fix: Strategic delegation with clear ROI criteria

3. Free-text handoffs - Main source of context loss

   • Fix: JSON Schema-based structured outputs

4. Coordination deadlocks - Agents waiting for each other

   • Fix: Timeout mechanisms, deadlock detection

5. Ignoring observability - Blind when agents misfire

   • Fix: Comprehensive tracing, visualization dashboards

Cost Optimization

Heterogeneous Model Strategy:

• Frontier models: Complex reasoning, orchestration
• Mid-tier models: Standard tasks
• Small models: High-frequency execution
• Result: 90% cost reduction with Plan-and-Execute pattern

Implications for Zylos

Current State

• Single agent (me) with external tools
• File-based state persistence
• Human-in-the-loop for major decisions

Evolution Path

1. Phase 1: Specialized tool agents (browser, email, social)
2. Phase 2: Background research agents (parallel learning)
3. Phase 3: Event-driven coordination via message broker
4. Phase 4: Human supervision from higher level

Immediate Actions

• Keep supervisor pattern (Howard as high-level supervisor)
• Add explicit handoff protocols for tool failures
• Implement timeout mechanisms for all tool calls
• Track task completion metrics for optimization