agent orchestration

The seven patterns (Parallel, Sequential, Loop, Router, Aggregator, Network, Hierarchical) are specific implementations of agent orchestration strategies.

Programmatic tool calling and multi-agent coordination are explicitly discussed as core improvements for 2026.

Core claim that agent orchestrators manage context, memory, tools, and permissions is central to the article's thesis about orchestration layers.

The planner→executor→verifier pattern is a concrete instantiation of multi-agent orchestration with explicit context routing

The article frames agents as the central pattern for managing context flow and evolution in real systems. Agents are described as both architects and users of context, which is a concrete expression o

Multi-agent systems are a specific instance of agent orchestration patterns. The article's discussion of handoff mechanisms and context transfer is orchestration implementation.

Article explicitly discusses orchestration frameworks and coordination mechanisms as core to multi-agent systems

ReAct agent implementation and tool-calling patterns are core to understanding how context must flow through decision-making cycles.

'How to use agents without losing control' directly addresses agent orchestration boundaries

MCP is presented as the infrastructure enabling orchestration of agents across multiple tools/systems, which requires context management across tool boundaries.

Article describes orchestration layer that decides what to retrieve, how to refine it, and when to iterate—core orchestration function.

Multi-agent coordination (subagents) mentioned as harness design decision; context management foundational to orchestration

Describes agentic architectures as 'specialized agents that coordinate, call tools and maintain their own memory/context' - explicit context preservation across interactions

The entire article is about orchestrating multiple agents with defined roles and sequential task flow. This is the core pattern being taught.

Articulates that agent failure is orchestration (what to do next) not execution (doing it). Orchestration is context-dependent decision-making.

The Agent SDK's mcp_servers parameter shows how context infrastructure enables multi-agent orchestration by providing shared context sources.

Introduces hierarchical orchestration pattern (lead agent + sub-agents) as specific orchestration strategy that measurably reduces hallucinations (71% improvement).

Article directly implements orchestration pattern using LangChain; shows how agents coordinate via orchestration layer

The article explicitly discusses agent architectures and orchestration patterns, which is a core component of multi-agent context engineering.

Article demonstrates CrewAI orchestration of multiple agents with distinct roles in a coordinated workflow

Harness design is presented as critical infrastructure for agent work coordination and context provisioning

The researcher/writer pipeline demonstrates sequential agent orchestration with context flow between specialized roles

Shift from isolated CLI to remotely-orchestrated nodes is a multi-agent coordination pattern allowing external steering and observation

Multi-agent patterns (subagents) are mentioned as one harness design decision, suggesting orchestration flows through context management.

Compares three orchestration patterns (independent, decentralized, centralized) and quantifies their failure modes.

Standardized MCP communication is foundational for orchestrating multiple agents with consistent tool access patterns.

Service registration for agents IS a form of orchestration pattern—managing how agents discover and interact with external capabilities.

Self-modifying compaction adds a meta-layer to agent architecture—agents managing their own context parameters

Reveals a failure mode in naive agent orchestration: lack of persistent execution context management.

Multi-agent systems are inherently about orchestrating multiple agents; the article likely discusses coordination patterns

StateGraph + tool integration + LLM coordination shows agent orchestration pattern implementation

Multi-agent systems require tools to be composable and transferable between agents; MCP enables this architectural pattern

Article demonstrates multi-agent patterns and how to coordinate agent behavior through structured workflows (LangGraph nodes, CrewAI crews), which is a core agent orchestration pattern.

The entire article is about orchestration layer design—which agent runs first, output passing, termination. This is directly about routing context through a system.

ACE enables long-horizon agentic tasks by solving context preservation—critical for multi-step agent orchestration

LangGraph is explicitly designed for orchestrating multi-agent workflows with explicit control over interaction patterns. This is a foundational agent orchestration use case.

MCP is infrastructure for multi-agent systems—allows different agents to share access to tools and data through standardized context interface

Article demonstrates multi-agent orchestration using CrewAI and LangGraph, which are concrete implementations of agent coordination patterns.

Argues orchestration frameworks may be unnecessary overhead for procedural tasks, contradicting framework utility

The Context→Execution→Verification→Loop pattern describes how humans orchestrate and verify agent work—a multi-turn coordination pattern.

The AgentExecutor section covers multi-step agent patterns, which require context preservation across tool calls.

Course builds 'course advisor agent' with LangChain orchestration, demonstrating how retrieval and memory layers must be orchestrated together.

Article frames multi-agent coordination as orchestration challenge requiring message queuing and state management

MCP's role in 'tool discovery, tool calling, and context exchange between AI systems' suggests context engineering enables multi-agent coordination through standardized information flows.

Article explicitly discusses building multi-agent systems with agent roles, task delegation, and tool coordination—this is orchestration pattern at the architectural level.

The Pi agent is an orchestration layer coordinating Qwen + Llama.cpp, demonstrating how agent architecture determines context flow and capability

Article discusses multi-agent communication costs and latency tax, revealing context overhead in orchestration patterns.

Article describes agent architecture with memory, planning, and tool coordination—core agent orchestration pattern

Subagents and parallel conversations suggest multi-agent coordination patterns where each agent maintains separate context/memory

Discusses how general-purpose agents + MCP servers replace custom agent implementations. Implies orchestration pattern where agent capabilities are composed via MCP server discovery.