agent orchestration patterns

Article explicitly describes 8 different orchestration/coordination models for agents. Each represents a pattern for managing context flow between agent nodes.

Article explicitly discusses ReAct, Reflection, REWOO, Plan/Execute as different agent orchestration approaches, which requires different context management strategies.

The nodes/edges/routing logic model is a specific orchestration pattern for coordinating multiple agents.

The three orchestration models (centralized/decentralized/hybrid) are direct instantiations of how context authority and information flow can be structured in multi-agent systems

Multi-agent collaboration and governance are core orchestration challenges where context management becomes critical

The article directly addresses how to orchestrate multi-step agent workflows with branching and looping, showing practical orchestration patterns beyond basic tool calling.

CrewAI and LangGraph represent different orchestration philosophies: role-based (CrewAI) vs graph-based (LangGraph) control flow management

Article explicitly references supervisor and swarm multi-agent architectures, which are orchestration patterns that determine how context flows between agents.

The graph structure and agent-to-agent communication (A2A) pattern shows concrete orchestration approach where context flows through defined channels rather than random agent interactions.

The '100x engineer directing agents' is an orchestration pattern where judgment/specification becomes the bottleneck, not execution

Author directly contradicts the standard 'predetermined multi-agent orchestration' pattern, arguing it's fundamentally incompatible with agentic reasoning

The managed runtime enables multi-step workflows (like Sentry's analysis→fix→PR pattern) by controlling execution order and tool access.

MCP enables multi-agent coordination by providing standardized protocol for agents to discover and invoke shared tools, mentioned in context of A2A (Agent-to-Agent) protocol

The article discusses multiple orchestration approaches (centralized, event-driven, distributed) which are fundamental to how context flows through multi-agent systems.

The post directly describes orchestration as tool calling, memory, branching/loops, multi-step planning—all core orchestration patterns in multi-agent systems

The manager-agent supervising specialized agents pattern is a canonical orchestration structure. Article demonstrates hierarchical orchestration where control flows top-down through the manager layer.

Implies that effective multi-agent systems require hierarchical delegation and context isolation—sub-agents that don't require full context from parent, similar to organizational structure.

LangGraph is orchestration framework. Teaching 'components and how to combine them to build flow-based applications' directly supports agent orchestration as a context engineering concern.

MCP as connective tissue between reasoning models and external tools is foundational to agentic orchestration; context flows through MCP boundaries

The systematic analysis of how functional modules interact is foundational to understanding multi-agent orchestration patterns and context flow between agents.

The article describes a specific pattern of agent nodes (capture, summarize, decide, act) orchestrated in a loop—a concrete instantiation of multi-agent coordination.

Shows how agent skill evolution requires meta-level oversight and systematic refinement

Claude Managed Agents represents an opinionated approach to orchestrating agent execution with a specialized harness, which is a core orchestration pattern

Description of engineers shifting from code-writing to 'directing agents' is an example of orchestration pattern. The skill requirement (judgment vs execution) reveals what orchestration demands.

Paper's taxonomy across Requirements → Code Generation → Testing suggests sequential agent patterns; organizing context for task handoffs between agents is a context engineering concern

Implies that agent harness architecture (orchestration of tools, context, memory) is the actual differentiator in agent infrastructure.

Context engineering described as necessary discipline for agents 'juggling multiple data sources, tools' implies context is the infrastructure enabling agent orchestration.

Agent frameworks require clear definitions of harnesses, scaffolds, and environments to orchestrate effectively. The glossary effort is an attempt to standardize these architectural concepts.

Flue implements an agent orchestration framework with built-in harness. The abstraction of runtime context is an orchestration pattern.

Paper documents 30 deployed agents which are examples of orchestration patterns in practice; the design choices and ecosystem dependencies reveal how orchestration is being solved

Multi-layer optimization (harness, model, fine-tuning) implies different orchestration strategies for different improvement targets. How to structure the agent system to enable learning.

LangGraph is mentioned as agentic AI framework, but no patterns about how agents manage context are discussed

RunAgent appears to be an orchestration layer for agents across languages, but no evidence provided of the pattern used (RPC? Serialization? State management?)