Workflows & Orchestration

Multi-agent systems, AI pipelines, and orchestration patterns. How to make AI agents work together.

Beyond single agents

A single AI agent can do impressive things. But the real power emerges when multiple agents coordinate — each with specialized skills, working together on complex tasks.

Orchestration is the discipline of designing, deploying, and managing these multi-agent systems. It’s where the field is heading, and it’s still early.

Patterns

Hub-and-spoke

A central coordinator agent delegates to specialized sub-agents. Each sub-agent handles a domain (coding, research, communication) and reports back.

Example: OpenClaw’s architecture — a gateway coordinates personal, coding, and knowledge management agents.

Pipeline

Agents process work sequentially, each adding value. Like a manufacturing line for information.

Example: Research agent → Analysis agent → Writing agent → Review agent

Swarm

Multiple agents work in parallel on related tasks, with lightweight coordination. Good for exploration and search problems.

Example: Multiple coding agents tackling different issues in a repository simultaneously.

Hierarchical

Agents organized in a tree structure with managers coordinating teams of worker agents.

Example: A project manager agent coordinating frontend, backend, and testing agent teams.

When orchestration is worth it

Do not add orchestration because it sounds more agentic. Add it when the workflow needs structure that a single prompt-response loop cannot provide.

Good reasons:

  • the task has explicit stages and state transitions
  • different steps need different tools or permissions
  • work can run safely in parallel
  • one agent should review or critique another agent’s output
  • failures need retries, fallbacks, or human escalation
  • the workflow needs durable traces for evaluation and audit

Weak reasons:

  • the demo looks more impressive with multiple agents
  • a framework makes it easy to add roles
  • the task is still vague
  • the single-agent version has not been tested yet

Design rules

Reliable orchestration is mostly about boundaries.

  • Give each agent or workflow step one job.
  • Separate read tools from write tools.
  • Keep shared state explicit and inspectable.
  • Log decisions, tool calls, and handoffs.
  • Add approval gates before irreversible actions.
  • Make failure states recoverable.
  • Test the workflow with replayable tasks.

If the system cannot explain why a handoff happened, it is not ready for autonomous operation.

Minimal orchestration stack

For a first production-minded agent workflow:

  1. Define the job and success condition.
  2. Build a single-agent version first.
  3. Add explicit state transitions for planning, tool use, review, and final output.
  4. Store traces and artifacts from every run.
  5. Add a small regression set from real tasks.
  6. Split into multiple agents only when one role is overloaded or a separate reviewer materially improves quality.

This path keeps the architecture inspectable while still leaving room for multi-agent systems when the product earns the complexity.

Key frameworks

LangGraph — Graph-based orchestration for complex agent workflows CrewAI — Role-based multi-agent framework AutoGen — Microsoft’s multi-agent conversation framework OpenClaw — Personal operations orchestration with specialized agents

What to watch

  • Standard protocols for agent-to-agent communication
  • Reliability and error handling in multi-agent systems
  • Cost optimization for multi-agent pipelines
  • Human-in-the-loop patterns for agent oversight

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