LLM Orchestration Cheat Sheet

Updated 2026-04-28

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LLM orchestration manages the coordination, execution flow, and state management of AI agents and multi-step LLM workflows in production systems. It handles agent communication, task routing, and decision-making logic to ensure reliable, scalable applications that combine multiple models, tools, and data sources. The discipline spans workflow frameworks, coordination patterns, and execution strategies that balance autonomy with predictability — and in 2026, has expanded to include open interoperability protocols (MCP, A2A), agent security, and enterprise governance for regulated deployments.

What This Cheat Sheet Covers

This topic spans 16 focused tables and 135 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core Orchestration PatternsTable 2: Workflow Execution ModelsTable 3: Agent Coordination StrategiesTable 4: Workflow Control MechanismsTable 5: Major Orchestration FrameworksTable 6: Low-Code Orchestration PlatformsTable 7: Task Routing and DispatchTable 8: State Management ApproachesTable 9: Agent Memory TypesTable 10: Execution OptimizationTable 11: Observability and MonitoringTable 12: Gateway and InfrastructureTable 13: Agent Communication ProtocolsTable 14: Best Practices and PatternsTable 15: Agent Security PatternsTable 16: Advanced Orchestration Concepts

Table 1: Core Orchestration Patterns

The shapes that multi-agent systems take, from a simple linear pipeline to a supervisor handing tasks down to specialists or a swarm of peers passing work around autonomously. Whether agents run in sequence, in parallel, or under a manager is the first architectural decision you make, and it sets the ceiling on how much coordination and overhead the rest of the system has to carry.

Pattern	Example	Description
Sequential	`agent1 → agent2 → agent3`	• Linear pipeline where each agent processes output from the previous step • ensures ordered execution with clear dependencies.
Concurrent (Parallel)	`[agent1, agent2, agent3] → merge`	• All agents work simultaneously on the same input • reduces total runtime and provides comprehensive coverage before aggregation.
Supervisor (Hierarchical)	`supervisor.assign(task, worker)`	• Manager agent decomposes tasks and assigns work to specialized subordinates • provides centralized control and monitoring.
Router-based	`router(query) → best_agent`	• Semantic routing directs requests to the most appropriate agent based on capabilities • achieves 95%+ routing accuracy in production.
Handoff	`agentA.transfer_to(agentB)`	• Agents delegate tasks to specialized peers based on context • enables dynamic responsibility transfer without central control.

Table 1: Core Orchestration Patterns

Pattern	Example	Description
Sequential	`agent1 → agent2 → agent3`	• Linear pipeline where each agent processes output from the previous step • ensures ordered execution with clear dependencies.
Concurrent (Parallel)	`[agent1, agent2, agent3] → merge`	• All agents work simultaneously on the same input • reduces total runtime and provides comprehensive coverage before aggregation.
Supervisor (Hierarchical)	`supervisor.assign(task, worker)`	• Manager agent decomposes tasks and assigns work to specialized subordinates • provides centralized control and monitoring.
Router-based	`router(query) → best_agent`	• Semantic routing directs requests to the most appropriate agent based on capabilities • achieves 95%+ routing accuracy in production.
Handoff	`agentA.transfer_to(agentB)`	• Agents delegate tasks to specialized peers based on context • enables dynamic responsibility transfer without central control.