Multi-Agent Orchestration Patterns (2026)

Multi-Agent Orchestration Patterns

Multi-agent systems are no longer a novelty. They are the backbone of production workflows that span research, coding, support, and operations. The hard part isn't getting a single agent to work - it's coordinating many agents with predictable behavior, bounded cost, and a clear recovery path when things go wrong.

This guide covers four orchestration topologies I use in OpenClaw deployments:

• Hub-and-Spoke
• Pipeline
• Peer-to-Peer
• Hierarchical

You'll learn when to use each, how message passing and state management differ, and where error handling and monitoring typically break down. Examples are based on building with OpenClaw and running 14+ agents across a connected ecosystem (personality marketplace, prompt battle arena, and bounty marketplace).

Baseline Concepts (Before Patterns)

Before we pick a topology, align on these primitives:

1. Message passing - how instructions and results move.
2. State management - where truth is stored and who owns it.
3. Error handling - how failure is detected and recovered.
4. Monitoring - how we observe latency, costs, and correctness.

These primitives behave differently depending on your topology. If you know which primitive is hardest for your project, you can choose an architecture that makes that problem cheaper.

Pattern 1: Hub-and-Spoke

Diagram

                +-----------------+
                |   Orchestrator  |
                +-----------------+
                 /   /   |   \   \
                /   /    |    \   \
     +---------+  +------+    +------+  +---------+
     | Agent A |  | Agent B |  | Agent C | | Agent D |
     +---------+  +------+    +------+  +---------+

When to Use

• You need central control and auditability.
• Tasks are independent or lightly coupled.
• You want a single place to manage rate limits, budget, and priority.

I use hub-and-spoke for:

• Tool routing (e.g., search, browse, code, ticket updates).
• Quality gates (one agent produces, another reviews, orchestrator approves).
• Multi-tenant workflows where per-tenant policies differ.

Message Passing

The hub is the message broker. Spokes send results back to the hub. A single schema simplifies tracing.

Key benefit: easy to inspect and replay. Risk: hub becomes a bottleneck.

State Management

The hub should own state. Agents operate statelessly or with scoped context pulled by ID. This improves safety (no long-lived secrets in agents) and reduces drift.

OpenClaw example: the orchestrator stores run state in Convex; agents only receive scoped payloads:

// Orchestrator calls worker with minimal context
await callAgent("summarizer", {
  runId,
  inputDocId,
  guardrails: { maxTokens: 1200, redaction: true }
});

Error Handling

Hub-and-spoke makes retries and fallbacks straightforward:

• Retry: hub detects failure and re-dispatches.
• Fallback: hub swaps to a cheaper model or simplified task.
• Circuit breaker: hub pauses a failing agent class.

Monitoring

Centralized logs and metrics:

• Agent latency and cost per run
• Run DAG and dependency timing
• Error rates per agent type

Tradeoff: if the hub fails, everything stops. Use redundant orchestrators or a recoverable task queue.

Pattern 2: Pipeline

Diagram

+----------+   +----------+   +----------+   +----------+
| Ingest   |-->| Extract  |-->| Enrich   |-->| Publish  |
+----------+   +----------+   +----------+   +----------+

When to Use

• Tasks are sequential and each stage depends on prior output.
• You want clear accountability per stage.
• You can tolerate backpressure.

Pipeline works for:

• Document processing (OCR → summarization → classification → publishing)
• Agent-based content generation (outline → draft → edit → QA)
• Code pipelines (spec → implementation → tests → review)

Message Passing

Stages pass transformed outputs downstream. Message size tends to grow - enforce budgets and summary checkpoints.

State Management

Pipeline state should be append-only with immutable stage outputs. That enables replay of a single stage without redoing everything.

OpenClaw example: I store each stage output in Convex with a version hash. If a step changes, we re-run only from that point.

Error Handling

• Local retries at each stage.
• Dead-letter queue for failed inputs.
• Manual inspection for high-value items.

Monitoring

Track:

• Throughput per stage
• Queue depth (backpressure signal)
• Drop rates or retries per stage

Tradeoff: pipelines can hide subtle errors that accumulate. Add sanity checks between stages (schema validation, confidence gating).

Pattern 3: Peer-to-Peer (P2P)

Diagram

+---------+   <----->   +---------+
| Agent A |   <----->   | Agent B |
+---------+   <----->   +---------+
      ^                    ^
      |                    |
      v                    v
+---------+   <----->   +---------+
| Agent C |   <----->   | Agent D |
+---------+   <----->   +---------+

When to Use

• Agents need direct negotiation or consensus.
• You want resilience (no single orchestrator).
• Problems are dynamic and context-rich (e.g., strategy games, collaborative design).

I use P2P for:

• Prompt battle arena where multiple agents debate scoring.
• Personality marketplace matching where agents negotiate constraints.

Message Passing

Distributed. Often event-driven or pub/sub. You need a protocol: message types, versioning, and TTLs.

State Management

State becomes eventual and shared. Adopt these patterns:

• Conflict-free replicated data types (CRDTs) for shared state.
• A shared ledger with optimistic concurrency checks.
• Periodic checkpointing to a central store.

Error Handling

• Agent dropouts are normal. Design for partial participation.
• Add timeouts and quorum rules (e.g., "need 2 of 3 votes").
• If consensus fails, fall back to a single reviewer agent.

Monitoring

• Conversation graph depth
• Number of messages per decision
• Consensus latency

Tradeoff: P2P systems are hardest to debug. Expect higher variance in output quality and runtime.

Pattern 4: Hierarchical

Diagram

                +-----------------+
                |  Director Agent |
                +-----------------+
                   /      |      \
          +---------+  +---------+  +---------+
          | Manager |  | Manager |  | Manager |
          +---------+  +---------+  +---------+
             /  \           |          /   \
        +-----+ +-----+  +-----+   +-----+ +-----+
        | W1  | | W2  |  | W3  |   | W4  | | W5  |
        +-----+ +-----+  +-----+   +-----+ +-----+

When to Use

• Work is decomposable into sub-domains.
• You need different levels of abstraction (strategy → tactics → execution).
• You're running many agents (10+).

I use hierarchical orchestration for:

• Bounty marketplace: director sets scope, managers handle categories, workers run actual tasks.
• Long-running research: director defines plan, manager handles sections, workers collect sources.

Message Passing

Top-down instructions, bottom-up results. Enforce contract boundaries between levels.

State Management

State is layered:

• Director owns the master run state.
• Managers own local state for their domain.
• Workers have no persistent state (or short-lived caches).

Error Handling

Failures are contained at the manager layer. If a worker fails, the manager retries or substitutes. If a manager fails, director reassigns the domain.

Monitoring

• Roll-up metrics per manager
• Latency per domain
• Error rates per layer

Tradeoff: hierarchy can become rigid. If a worker could solve a new problem but has no permission, it stalls. Add escalation paths.

Message Passing Patterns (Practical)

No matter the topology, your messages should be:

• Typed (schema validated)
• Versioned
• Limited (size and time to live)

Example schema (Zod):

import { z } from "zod";

export const AgentMessage = z.object({
  id: z.string(),
  runId: z.string(),
  from: z.string(),
  to: z.string(),
  type: z.enum(["task", "result", "error", "heartbeat"]),
  payload: z.record(z.any()),
  createdAt: z.number(),
  ttlMs: z.number().default(15 * 60 * 1000)
});

Tip: Add ttlMs to avoid zombie messages in long-running workflows.

State Management Choices

In OpenClaw, I default to central store for orchestration and use event logs for pipeline stages. P2P is the exception, not the norm.

Error Handling Patterns That Work

1) Retry with Backoff

async function retry<T>(fn: () => Promise<T>, retries = 3) {
  let lastErr: unknown;
  for (let i = 0; i < retries; i++) {
    try { return await fn(); } catch (err) { lastErr = err; }
    await new Promise(r => setTimeout(r, 2 ** i * 500));
  }
  throw lastErr;
}

2) Fallback Model / Tool

If a model fails or is too expensive, fallback to a cheaper path:

const response = await tryPrimary().catch(() => tryFallback());

3) Human Escalation

For high-stakes tasks, define a threshold that triggers human review.

Monitoring: What Matters

At minimum, collect:

• Latency per agent
• Cost per run
• Failure rate per agent type
• Queue depth / backlog

Also track quality through sampling or automated evaluation (see the testing and evaluation guide in this series).

OpenClaw practice: I instrument runs as a DAG and send events to Convex for realtime dashboards.

Putting It Together: Choosing the Right Pattern

Quick heuristic:

• You need control & auditability: Hub-and-spoke
• You need clear stages & replay: Pipeline
• You need collaboration & negotiation: P2P
• You need scale with abstraction layers: Hierarchical

Most real systems are hybrids. For example, my content engine uses a pipeline inside a hub-and-spoke — see the multi-agent content pipeline case study for the full architecture. The hub dispatches stages, and each stage is a pipeline step. That gives central policy control with stage-level observability.

Final Notes from the Field

OpenClaw made orchestration easier, but designing the topology is still the hard part. Don't chase novelty - choose the pattern that makes your hardest problem cheaper.

If you're running more than 10 agents, start with hierarchy. If you need strict compliance, use hub-and-spoke. If you're building a debate arena, P2P is your friend. And if you're shipping reliable content pipelines, keep it simple and stage-based.

The architecture isn't the product. But it decides whether your product can survive real usage. I cover multi-agent orchestration patterns in depth — including the failure modes that aren't obvious until production — in the AI Agent Masterclass.

For the foundations of building individual agents before you orchestrate many, see the complete builder's guide. If you're running Claude Code agents as part of a multi‑agent coding team, the agent teams explainer covers practical patterns. And for a real‑world example of what overnight multi‑agent builds look like, check out the overnight agent builds case study.