How to Orchestrate AI Agents in Production
About 2 hours from zero to first orchestrated workflow
Orchestrating AI agents in production is more than chaining LLM calls. It requires shared memory, capability-based routing, runtime governance, and observability. This guide walks through the steps — using LeafMesh ADK as the agent operations fabric — so the agents you build behave reliably in production.
Steps
- 1
Define each agent's role and capability
List the agents you need and what each one does. Write each as a capability declaration — not as a prompt. A capability is what the agent can do; a prompt is how you ask. Capability-first definitions let LeafMesh route tasks correctly.
In YAML, declare agents with name, role, capabilities, and the LLM provider. Multiple agents with overlapping capabilities are fine — LeafMesh routes by suitability.
- 2
Define shared memory and context boundaries
Decide what state agents share and what stays local. Shared memory enables coordination; over-sharing leaks unnecessary context and increases cost. LeafMesh provides per-workflow shared memory by default — you scope it explicitly.
- 3
Add policy and human-in-the-loop checkpoints
For every high-stakes decision, add a policy gate: thresholds, approval rules, escalation paths. LeafMesh's policy engine evaluates each agent decision; under threshold it auto-approves, over threshold it routes to a human with full context.
- 4
Configure capability-based routing
Tell LeafMesh which LLM to use for which task. Use small fast models for routine routing, large models for complex reasoning, specialised models for domain tasks. Capability metadata on each agent + LeafMesh's routing engine handles the rest.
- 5
Add observability and audit
Enable LeafMesh's built-in dashboards and audit log. Every agent decision, every cost, every handoff is logged. Export to your existing observability stack via OpenTelemetry.
- 6
Test in pre-compose pipeline
Before deploying, run the workflow through LeafMesh's pre-compose pipeline. The 6-layer validation stack catches schema errors, type mismatches, policy violations, safety issues, cost overruns, and output anomalies.
- 7
Deploy and monitor
Deploy to production with one command. LeafMesh validates, wires the mesh, enables observability, and runs your agents under governance. Monitor through dashboards, iterate on policies as you learn.
Common pitfalls
- !Don't make every agent stateless — shared memory is what enables coordination
- !Don't put policy in agent prompts — encode it as runtime policy that's testable and auditable
- !Don't lock to one LLM vendor — vendor-agnostic routing is a force multiplier
- !Don't skip observability — agent debugging without traces is misery
Want to put this into practice?
LeafMesh ADK is the agent operations fabric that runs the patterns in this guide.