Multi-Agent Orchestration: How to Chain AI Agents into Workflows

Multi-agent orchestration turns brittle one-shot prompts into repeatable pipelines: research, outline, draft, review, publish. The hard part is not calling multiple models - it is making the chain observable and reversible when something goes wrong.

When to chain versus single-shot

Use a chain when steps have different risk profiles or owners. Keep single calls for low-risk summaries where a single reviewer is enough.

Contracts between agents

Pass structured outputs - JSON with fields, not prose blobs - between steps so downstream agents do not re-parse hallucinated structure. Validate with lightweight schemas before the next hop.

Human gates that do not bottleneck

Approval should be one click with context: diff, sources, and policy flags. If reviewers re-read everything from scratch, you have recreated the old editorial queue.

Failure modes and replay

Retries, partial outputs, and model timeouts should log cleanly. Being able to replay a failed step without re-running expensive upstream work saves hours during incidents.

Observability for EU AI Act readiness

Each step should emit who approved what and which model version ran. That narrative is what you will need when legal asks how a customer-facing answer was produced.