What is multi-agent orchestration?
Multi-agent orchestration means splitting work across several AI agents and managing their order, communication and how their results come together. Steps like research, planning, coding and review go to different agents, which run one after another or in parallel.
Common patterns
Most multi-agent setups combine a few basic patterns.
- Pipeline: one agent’s output is the next one’s input, as in research, plan, build.
- Fan-out: independent tasks go to different agents at the same time.
- Orchestrator and workers: one agent splits the job, hands out subtasks and collects the results.
- Writer and reviewer: one agent writes, another checks independently and sends it back if needed.
Handing off work
The most reliable way to pass information between agents is something explicit and lasting: a plan file, acceptance criteria or a shared notice board. Forwarding a long reply as is fills up the next agent’s context; passing on only what’s needed lowers both cost and the chance of mistakes.
Risks
More agents means more coordination problems.
- File conflicts: two agents in the same folder can overwrite each other; separate worktrees prevent that.
- Endless loops: two agents can keep sending each other work, so set a turn limit.
- Cost: every agent carries its own context, and total tokens climb fast.
- Unclear ownership: record which agent decided what.
One agent or many?
For small, tightly connected work, a single agent is usually faster and more consistent. Several agents pay off when the work really can be split, when you need an independent check, or when the job is too big for one context.
Orchestration in AgentVera
AgentVera gives you several ways to run different CLI agents together in one window.
- Parallel team: splits a big request into parts; each part runs with its own agent in its own worktree, the parts merge on an integration branch and you open a single PR (Pro and Team).
- Flows: wire agents together on a canvas; when one finishes its turn, its last reply goes to the next through a template. There are condition, wait, transform and human approval nodes, plus turn limits (Pro and Team).
- Task line: Analysis, Plan, Build and Review stages; tasks move through in order and go back to Build if the reviewer rejects them (Pro and Team).
- Agent coordination: warnings when agents edit the same file, and a shared board per agent group that agents use through MCP tools.
FAQ
Is a multi-agent system better than a single agent?
Not always. It helps when the work can be split and needs an independent check; for small jobs, the extra coordination and token cost don’t pay off.
Can I mix different agents?
Yes. You might use Claude Code for research and planning and Codex for the build. What matters is handing off in an explicit form, such as a file.
What happens if two agents change the same file?
In the same folder, changes can overwrite each other. Separate git worktrees, or splitting the work by file, reduce the risk.
How is orchestration different from subagents?
A main agent starts a subagent internally and gets the result back. In orchestration, independent agent sessions are connected by a flow or pipeline, and you can see each one.