How to Build an AI Agent Team
About 3 hours for a working multi-agent team
An AI agent team is more than a list of agents — it's a coordinated system with shared memory, clear roles, capability-based routing, and human oversight. This guide walks through the steps to build one in production using LeafMesh ADK.
Steps
- 1
Map the workflow first, agents second
Don't start with agents. Start with the workflow: what work needs doing, what decisions are made, what handoffs exist. Then assign agents to roles. Many teams over-decompose and end up with too many agents that fight over the same task.
- 2
Define each agent's role, capability, and authority
Each agent needs: a role (what it owns), a capability (what it can do), and authority (what decisions it can make autonomously). Authority is the most-skipped part — without explicit authority, agents either escalate too much or act outside their bounds.
- 3
Set up shared memory for coordination
The team's coordination happens through shared memory. In LeafMesh, declare a shared context for the workflow; each agent reads and writes scoped fields. Shared memory enables 'every agent sees what the others see'.
- 4
Define handoff rules
When does work move from one agent to another? Encode it. Confidence threshold? Capability mismatch? Policy violation? Each handoff is a runtime decision LeafMesh routes based on your rules — not the agent's improvisation.
- 5
Add a governance agent or layer
Add a separate agent or LeafMesh policy layer that evaluates every team decision against the rules. Treat governance as its own role, not a side-effect of the working agents. This separates 'doing the work' from 'is the work allowed'.
- 6
Plan human checkpoints explicitly
Where do humans decide? Pre-decide. LeafMesh provides approval gates and escalation routing as primitives. The team's design should specify exactly when a human is required and what context they see.
- 7
Run the team in production with observability
Once defined, deploy the team under LeafMesh. Watch the dashboards: which agent is doing what, where handoffs happen, what the cost looks like, where humans are getting involved. Iterate on the team design as you observe its behaviour.
Common pitfalls
- !Too many agents → conflicts, latency, cost. Start with 3, grow only when needed.
- !Implicit authority → agents act outside their bounds. Make authority explicit.
- !No shared memory → agents work in isolation, repeat work, lose context.
- !No governance role → bias and policy violations slip through.
Want to put this into practice?
LeafMesh ADK is the agent operations fabric that runs the patterns in this guide.