Multi-agent orchestration is the coordination of several AI agents that each handle part of a larger task. An orchestration layer decides which agent works on what, passes inputs and intermediate results between them, manages shared state, and combines their outputs into a final result. Instead of one general agent with every tool and instruction in a single prompt, a multi-agent system splits work across specialists, such as a planner, a data retrieval agent, a SQL agent, and a reviewer.
How It Works
Orchestration designs differ mainly in who decides the next step:
- Supervisor (hierarchical). A coordinating agent receives the goal, delegates subtasks to worker agents, reviews their results, and decides when the task is done.
- Sequential pipeline. Agents run in a fixed order, each consuming the previous agent’s output, like stages in a data pipeline.
- Graph-based workflows. Agents are nodes in a defined graph with conditional edges, so the path can branch or loop based on results while staying inspectable.
- Peer collaboration. Agents exchange messages directly, debating or refining an answer until they agree or hit a limit.
Whatever the pattern, the orchestrator handles the same concerns: routing work to the right agent, keeping shared state consistent, setting step and cost limits, handling failures and retries, and recording what each agent did. Many agentic frameworks provide these primitives.
Interoperability standards matter when agents come from different vendors. The Agent2Agent (A2A) protocol, created by Google and contributed to the Linux Foundation in June 2025, defines how agents advertise their capabilities through “Agent Cards” and exchange tasks over JSON-RPC 2.0 on HTTP(S). It complements the Model Context Protocol (MCP), which connects a single agent to tools and data rather than to other agents.
| Single agent | Multi-agent orchestration | |
|---|---|---|
| Prompt and tool scope | One large prompt with all tools | Smaller, focused prompts per agent |
| Debugging | One trace | Traces per agent plus handoffs |
| Model choice | One model | Different models per role possible |
| Cost and latency | Fewer calls | More calls and coordination overhead |
| Best fit | Narrow, well-defined tasks | Tasks with distinct stages or expertise |
More agents is not automatically better. Each handoff adds latency and a chance to lose context, so many teams start with one agent and split only when prompts or tool lists grow unmanageable.
Multi-Agent Orchestration in the Lakehouse
Analytics work divides naturally into roles. A planner interprets the business question; a discovery agent searches the catalog for relevant Apache Iceberg tables and views; a SQL agent writes queries against the semantic layer and runs them on an engine such as Dremio or Trino; a reviewer checks results against known metric definitions before anything reaches a user.
The lakehouse supplies what keeps those agents consistent with each other. A shared catalog gives every agent the same view of what data exists. Governed views and metric definitions keep two agents from computing “revenue” two different ways. Catalog-enforced access controls apply to each agent’s credentials, so a retrieval agent cannot read data the requesting user is not allowed to see. Agent actions and results can also be logged to Iceberg tables for audit and evaluation.
See Turning an Analytics Question Into a Verified Agentic Graph, Metric Contracts in Code, and the AI agents glossary entry.
Summary
Multi-agent orchestration coordinates specialized AI agents through supervisors, pipelines, or graphs, with standards like A2A connecting agents across platforms. On a lakehouse, shared catalogs, semantic definitions, and access controls keep those agents working from the same governed facts.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.