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.
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