multi agent orchestration patterns

The core content directly discusses design patterns (actor model, graph-driven, message-driven) for orchestrating multiple agents—this is a foundational concept for multi-agent context engineering

Paper explicitly discusses multi-agent coordination and planning loops, which are core orchestration patterns in context engineering.

Identifies specific pattern (single-threaded writes) that makes multi-agent work, advancing beyond generic multi-agent discussion

The MTTR-focused mentality Hashimoto critiques is what happens when agents operate without proper coordination context—each agent optimizes locally without global coherence

Agent context autonomy depends on how tools are integrated into the agent's decision loop; MCP vs CLI is a concrete instantiation of this broader orchestration concern.

Tutorial explicitly covers 'architectural patterns' for multi-agent systems, which is a core multi-agent orchestration concern

Paper directly addresses coordination and control of multi-agent teams, a core CE pattern

Paper demonstrates how SCM unifies multi-agent collaboration as a context pattern, showing this as instance of general context structuring principle

LangGraph + CrewAI integration is a specific implementation of multi-agent orchestration, representing the pattern of combining task graph orchestration with agent autonomy frameworks.

Survey's core focus on coordination mechanisms across agent systems directly maps to orchestration patterns; hierarchical vs decentralized discussion is foundational to understanding agent topology.

DoorDash's orchestrator connecting independent onboarding steps mirrors the challenge of coordinating multiple AI agents where each agent has its own context and they must share state without pollutin

The emphasis on team practices and shared, repeatable context implies coordination across multiple agents or team members using the same context framework.

Custom orchestration layers (alternative to frameworks) are the practical application of multi-agent patterns. Article discusses event-driven vs synchronous chain execution—a core orchestration design

Tutorial explicitly covers multi-agent systems; managing context across agents is a core orchestration challenge

Core to the paper's theme. Orchestration requires context management across agent boundaries—which agents need what information, in what order, with what confidence levels.

Shows how tool binding becomes complex in multi-agent systems where different agents share access to the same tools—requires careful context management to avoid state conflicts.

Google ADK and CrewAI sections describe different orchestration models; CrewAI's role-based delegation is a specific orchestration pattern

MCP servers as reusable capability sources support orchestration patterns; the adapter layer concept is relevant to how agents discover and use tools.

Article claims agentic AI is trending and critical to understand, but provides zero insight into orchestration challenges, context preservation across agent steps, or coordination patterns. Suggests t

Runtime-agnostic framework abstraction likely enables easier orchestration of agents across different execution contexts (Node.js, serverless, CI/CD). The portability requirement implies agents can be

Article mentions multi-agent coordination as a feature (e.g., Orq.ai's 'step-by-step execution to event-driven task hand-offs') but doesn't explain patterns or trade-offs

Article mentions multi-agent systems but provides no orchestration patterns, communication protocols, or context flow diagrams that would constitute real pattern documentation