You’re running multiple AI agents? Then you need traffic control. Orchestration is what determines how your autonomous agents communicate, delegate, and coordinate with each other.
This guide is part of our comprehensive resource on understanding multi-agent AI orchestration and the microservices moment for artificial intelligence, where we explore the architectural patterns transforming AI system design.
Get it wrong and coordination overhead eats 40-50% of your execution time. Get it right and you have a system that scales predictably while staying reliable.
There are five distinct orchestration patterns out there: centralised, decentralised, hierarchical, event-driven, and federated. Each one trades off different capabilities. This article gives you the framework to pick the right one based on what you’re actually constrained by.
We’ll walk through the core patterns, the TAO cycle that enables autonomous behaviour, context management, and how to minimise coordination overhead. By the end you’ll have a comparison matrix for selecting the approach that fits your needs.
What Are the Core Orchestration Patterns and When Do You Use Each?
Five fundamental patterns shape how multi-agent systems coordinate work. These cluster into three broader architectural approaches.
Centralised orchestration puts a single agent in charge directing all workers in a hub-and-spoke topology. ChatDev demonstrates this with a CEO orchestrator assigning tasks to designers, developers, and testers. You get high control and observability, but you’re accepting a single point of failure.
Use this for customer service workflows, deterministic business processes, or anything requiring audit trails and strict execution order.
Decentralised orchestration flips the model. Agents coordinate autonomously through peer communication. Microsoft’s AutoGen implements this, enabling agents to message and negotiate without central mediation.
The trade-off? High resilience with no central failure point, but coordination overhead explodes. With N agents, you create O(N²) communication pathways.
Choose this when fault tolerance matters more than predictability. Research and exploration tasks benefit from emergent creativity. Conversational assistants and customer support can leverage the adaptive problem-solving.
Hierarchical orchestration splits the difference. You get multi-layered delegation with supervisor agents managing worker teams. Top-level supervisors define objectives, mid-level supervisors manage domains, workers execute tasks. It balances centralised control with decentralised scalability.
Each supervisor manages typically 5-10 agents. Enterprise workflows spanning multiple domains—software development with architecture, implementation, and testing teams—fit this model well.
Event-driven uses asynchronous message-based coordination. Federated handles cross-organisational collaboration.
Michael Fauscette emphasises most enterprises should adopt hybrid models—centralised governance for control, paired with decentralised execution within defined boundaries.
How Does the TAO Cycle Enable Autonomous Agent Behaviour?
These patterns all rely on the same mechanism: the TAO cycle. Thought-Action-Observation. It’s the iterative reasoning loop that breaks down complex tasks into manageable steps an LLM can handle.
In the Thought phase, the agent analyses current state and goals, then decides the next step.
During Action, the orchestrator executes that action—typically querying databases or calling APIs. Tool calling extends agent utility beyond pure language reasoning.
The Observation phase closes the loop. The orchestrator captures the action result and feeds it back to the LLM as input for the next cycle.
This continuous reasoning-execution-feedback mechanism creates autonomous behaviour.
Sharon Campbell-Crow puts it clearly: “The moment an LLM can decide which tool to call next, you’ve crossed a threshold. You’ve moved from building a chatbot to building an autonomous system.”
The problem? Even with 99% success per step, a 10-step process only has ~90.4% chance of succeeding.
The orchestrator manages this through stateful memory, error handling, and conditional branching. Centralised orchestrators plan and delegate, decentralised agents coordinate with peers—but the fundamental mechanism stays the same.
Each TAO iteration incurs LLM call latency. Multiple agents multiplying iterations—that’s where coordination overhead compounds.
What Is Centralised Orchestration and What Are Its Trade-Offs?
Centralised orchestration means a single entity maintains global awareness and directs individual agents. All communication flows through the orchestrator.
ChatDev demonstrates this: the orchestrator receives requirements, decomposes them into tasks, delegates to specialised agents, monitors completion, and synthesises deliverables.
The benefits: simplified decision-making, low communication overhead, easier conflict resolution, predictable execution flow.
Observability gets particularly easy. Everything flows through one point. If something breaks, you know where to look.
The trade-offs: single point of failure halts everything, scalability constraints, and all coordination overhead concentrates in one component.
Use this for workflows requiring transparency and traceability. Enterprise banking. Anywhere compliance or safety requirements demand predictable execution.
How Does Decentralised Orchestration Differ and When Is It Appropriate?
Decentralised coordination distributes responsibilities across agents with no single entity having complete control.
Microsoft’s AutoGen enables this through direct agent messaging and negotiation. Agents collaborate autonomously.
The benefits: greater robustness through redundancy, improved scalability, parallel execution, and emergent creativity.
The costs? With N agents creating O(N²) communication pathways, coordination overhead becomes significant. Global optimisation becomes difficult. Conflict detection gets more complex.
Michael Fauscette describes observability as “wildlife tracking rather than flowcharting.” Debugging resembles detective work.
Use this when system resilience outweighs predictability. Research and exploration tasks. Real-time applications prioritising responsiveness—anywhere graceful degradation matters more than perfect execution.
Michael Fauscette’s advice? “Begin with hybrid architectures” and “strategically identify where decentralisation provides measurable advantages.”
What Role Does Hierarchical Orchestration Play in Complex Workflows?
Hierarchical orchestration gives you both centralised control and decentralised scalability.
The structure follows layered delegation: top-level supervisors define objectives, mid-level supervisors manage domains, workers execute tasks.
Coordination overhead sits in the moderate zone. Less than decentralized’s O(N²) explosion, more than centralized since multiple supervisors communicate. The overhead distributes across levels instead of concentrating.
Each supervisor manages typically 5-10 agents. When you hit capacity, add another layer.
Supervisory oversight creates a reliability benefit. Supervisors monitor worker progress and detect when workers lose focus. When task derailment happens, supervisors provide redirect or context.
Michael Fauscette describes the appeal: “Centralised governance for control, paired with decentralised creativity.” You maintain audit trails while enabling autonomous execution that scales.
Use this for complex business processes spanning multiple domains. Content production pipelines with editorial oversight. Any workflow needing supervisory monitoring but wanting workers executing independently.
How Do Agents Manage Context and Why Does It Matter?
All these patterns share a challenge: managing context across agent interactions.
Four types of information need systematic handling:
Temporal context: conversation history and sequences. What’s been discussed, decided, executed. Enables continuity across TAO cycles.
Social context: agent relationships, roles, capabilities. Who has expertise in which domains. Enables effective delegation.
Task context: current goals, objectives, constraints, and progress. What needs accomplishing, what’s complete, what’s blocked.
Domain context: specialised knowledge. Product catalogues, coding standards, regulatory requirements.
Why does this matter? Because LLMs are stateless. Sam Schillace, Microsoft’s deputy CTO: “To be autonomous you have to carry context through a bunch of actions, but the models are disconnected and don’t have continuity.”
This is the “disconnected models problem.” Without shared understanding, you get reasoning-action mismatches, task derailment, redundant work.
The performance impact is measurable. Context retrieval dominates coordination overhead—40-50% of execution time spent fetching conversation history, checking task state, retrieving domain knowledge.
Context windows compound the problem. As each agent adds reasoning and outputs, context windows grow rapidly.
Context engineering means designing what agents share, when they share it, how context propagates. Scope persisted state to minimum necessary to reduce token overhead.
Model Context Protocol for context sharing addresses this. MCP provides standardised mechanisms for context storage, retrieval, and sharing across agent boundaries.
What Is Coordination Overhead and How Do You Minimise It?
Multi-agent systems consume significantly more resources: agents use about 4× more tokens than chat interactions, multi-agent systems use about 15× more.
That 15× multiplier comes from coordination overhead. Four sources compound it: context fetching, inter-agent communication, state synchronisation, and LLM call latency multiplication.
Data retrieval for context assembly dominates. Coordination overhead eats 40-50% of execution time.
Is overhead justified by capability gains? Anthropic’s multi-agent research system provides the answer. Using Claude Opus 4 + Sonnet 4 in multi-agent configuration outperformed single-agent Claude Opus 4 by 90.2%. Despite the 15× token consumption.
When capability gains exceed overhead cost, multi-agent makes sense. When they don’t, stick with single agents.
Semantic caching provides the primary mitigation. A 70% cache hit rate reduces overhead from 50% to 30%—avoiding repeated context retrieval.
Context pruning helps. Share only necessary information. This reduces data transfer and token costs.
Asynchronous communication changes the game. Agents work concurrently rather than blocking, reducing waiting time.
Infrastructure requirements for orchestration make retrieval faster. Redis and distributed caches reduce latency.
Azure suggests assigning each agent a model matching task complexity. Not every agent requires the most capable model. Monitor token consumption to identify expensive components.
Pattern selection impacts overhead differently. Centralised concentrates it at the orchestrator. Decentralised amplifies through O(N²) communication. Hierarchical balances load across layers.
The bottom line: coordination overhead is manageable. Semantic caching, context pruning, appropriate model selection, and infrastructure reduce the tax.
How Do Orchestration Patterns Affect System Reliability and Failure Modes?
Multi-agent orchestration inherits distributed systems problems: node failures, network partitions, message loss, cascading errors.
Understanding how patterns affect failure modes is critical for production deployments.
Centralised coordination offers easier conflict detection. Everything flows through one point. But that single point of failure creates a reliability liability.
Decentralised approaches provide greater robustness through redundancy. No single point of failure means the system degrades gracefully.
But emergent behaviour creates unpredictability. Multi-agent systems take varied valid routes. This requires LLM judges with rubrics rather than exact output matching.
Hierarchical patterns offer scoped failure impact. Supervisor failures affect sub-trees but not the entire system.
Event-driven patterns handle temporary failures through message persistence and retry. But eventual consistency requires reconciliation logic. Messages may arrive out-of-order.
Azure recommends: implement timeout and retry mechanisms, include graceful degradation, surface errors so downstream agents can respond.
Output validation prevents cascade failures. Malformed responses can cascade through a pipeline. Validate agent output before passing it on.
Agent isolation reduces shared failure modes. Ensure compute isolation. Evaluate how shared endpoints create rate limiting that could cascade.
The trade-off: easier observability—centralised patterns—typically means lower fault tolerance. Harder observability—decentralised patterns—typically means higher resilience.
Pattern Comparison Matrix and Selection Framework
Here’s how the patterns compare:
Sequential orchestration: linear pipeline with deterministic routing. Best for step-by-step refinement. Low complexity, high control, moderate scalability.
Concurrent orchestration: parallel coordination. Best for independent analysis and latency-sensitive scenarios. Moderate complexity, high scalability.
Group chat orchestration: conversational coordination where a chat manager controls turn order. Best for consensus-building. Moderate-high complexity, moderate control.
Handoff orchestration: dynamic delegation with one active agent. Agents decide when to transfer control. Best when the right specialist emerges during processing.
Magentic orchestration: plan-build-execute coordination where a manager assigns tasks dynamically. Best for open-ended problems. High complexity, high scalability.
The broader patterns map onto these mechanisms. Centralised uses sequential or concurrent with single orchestrator control. Decentralised implements group chat or peer handoffs. Hierarchical layers concurrent execution under supervisory oversight.
Kore.ai recommends starting simple: begin with configuration-based patterns, advancing to custom implementations only when necessary.
For help choosing patterns for use cases, apply these decision criteria: Need control? Choose centralised. Need resilience? Choose decentralised. Need balanced capabilities at scale? Choose hierarchical. Need async throughput? Choose event-driven. Need cross-organisational collaboration? Choose federated.
Common mistakes: choosing decentralised for simplicity when it’s high complexity, choosing centralised for scale when it creates bottlenecks, ignoring observability requirements.
After pattern selection, consider infrastructure. Context sharing benefits from MCP primitives. State management needs Redis state management and distributed caches. Frameworks—AutoGen for decentralised, Semantic Kernel for enterprise, LangChain for flexibility—provide the building blocks.
Azure’s guidance: use the lowest complexity that reliably meets your requirements. Adopt multi-agent when single agents demonstrably can’t handle your requirements and coordination overhead is justified by capability gains.
For a complete overview connecting these patterns to the broader multi-agent orchestration fundamentals, see our comprehensive guide.
What’s the difference between sequential and concurrent orchestration?
Sequential orchestration executes agents one after another in strict order—used when tasks have dependencies. Concurrent orchestration runs multiple agents in parallel—used when tasks are independent. Sequential is simpler but slower; concurrent is faster but requires coordination to aggregate results. Both are coordination strategies that work within any orchestration pattern.
When should I use multi-agent orchestration instead of a single agent with tools?
Use single-agent when tasks fit within one agent’s context window and capability range. Use multi-agent when tasks require specialised expertise across domains, exceed single agent context limits, benefit from parallel execution, or need resilience through redundancy. Multi-agent adds 40-50% coordination overhead and 15× token consumption; only justified when capability gains outweigh costs. For detailed decision criteria, see our practical framework guide.
How do I choose between centralised and decentralised orchestration?
Choose centralised when you need predictable execution, strict control, easy observability, compliance requirements, or deterministic workflows. Choose decentralised when you need fault tolerance, resilience, exploration tasks, or when agents must continue operating despite component failures. Centralised has single point of failure but high control; decentralised has no single point of failure but emergent behaviour.
What causes the 40-50% coordination overhead in multi-agent systems?
Coordination overhead comes from four sources: (1) Context retrieval—agents fetching conversation history, task state, domain knowledge before each action; (2) Inter-agent communication—message passing and handoffs; (3) State synchronisation—ensuring agents see consistent data; (4) LLM call latency multiplication—each agent’s TAO cycle adds latency. Semantic caching reduces overhead to 30% through 70% cache hit rates.
How does the TAO cycle work in multi-agent systems?
Each agent runs its own TAO (Thought-Action-Observation) cycle iteratively: (1) Thought—agent analyses current state and goals using LLM reasoning; (2) Action—agent executes chosen action, potentially calling tools/APIs; (3) Observation—agent captures action result and feeds it back to LLM for next cycle. In centralised patterns, the orchestrator’s TAO includes delegating to workers. In decentralised patterns, each agent’s TAO includes coordinating with peers.
What is context management and why does it matter?
Context management maintains four types of information: (1) Temporal context—conversation history; (2) Social context—agent roles and relationships; (3) Task context—goal state and progress; (4) Domain context—specialised knowledge. Without shared context, agents cannot collaborate effectively; they lose continuity, duplicate work, make inconsistent decisions. Context retrieval dominates coordination overhead (40-50% execution time). Model Context Protocol (MCP) provides standardised primitives for context sharing.
How do I reduce coordination overhead in my multi-agent system?
Four primary strategies: (1) Semantic caching—cache similar queries/responses to achieve 70% hit rates, reducing overhead from 50% to 30%; (2) Context pruning—share only necessary information; (3) Asynchronous communication—use event-driven patterns so agents work concurrently; (4) Better state management infrastructure—use Redis or distributed caches for faster context retrieval. Pattern selection also impacts overhead: centralised concentrates it, decentralised amplifies it through O(N²) communication, hierarchical distributes it.
What frameworks support multi-agent orchestration?
AutoGen (Microsoft) implements decentralised peer-to-peer patterns. ChatDev demonstrates centralised CEO orchestrator pattern. Semantic Kernel (Microsoft) supports concurrent orchestration with production state management. LangChain provides chain and agent abstractions for multiple patterns. Azure Architecture Centre documents Sequential, Concurrent, Group Chat, Handoff, and Magentic patterns with implementation guidance.
How do orchestration patterns affect system reliability?
Centralised patterns have low fault tolerance (single point of failure) but reduce reasoning-action mismatches through validation; best for control and predictability. Decentralised patterns have high fault tolerance (no single point of failure) and graceful degradation but emergent behaviour creates unpredictability; best for resilience. Hierarchical patterns provide moderate fault tolerance (supervisor failures affect sub-trees only) and prevent task derailment through oversight; balanced approach.
What is the Model Context Protocol and how does it help orchestration?
Model Context Protocol (MCP) is an emerging standard for context sharing across multi-agent systems. It addresses the “disconnected models problem”—LLMs are stateless, so multi-agent systems must explicitly engineer context sharing. MCP provides standardised primitives for propagating temporal, social, task, and domain context across agents. This reduces coordination overhead by eliminating redundant context retrieval and ensures consistent context across agents.
How does hierarchical orchestration prevent task derailment?
Hierarchical patterns use supervisor agents to monitor worker progress. Supervisors detect when workers lose focus, drift from goals, or pursue tangents. When derailment is detected, supervisors provide redirect, context, or constraints to workers, maintaining goal alignment. This supervisory oversight prevents workflows from degrading while still enabling worker autonomy. Particularly valuable for complex multi-step workflows where maintaining goal alignment is critical. Understanding these reliability implications helps prevent common failure modes.
What’s the difference between event-driven and decentralised orchestration?
Decentralised orchestration focuses on peer-to-peer agent communication patterns; agents directly message and negotiate; typically synchronous interactions. Event-driven orchestration focuses on asynchronous message-based coordination; agents publish events and subscribe to relevant events; loose coupling through message broker; agents don’t know about each other directly. Event-driven enables higher throughput through async processing but introduces eventual consistency challenges. Decentralised provides tighter coordination through direct peer communication but higher overhead through O(N²) pathways.
