Raltic field guide
AI agent orchestration: a practical guide for teams
Agent orchestration is the coordination layer that turns separate models and tools into an accountable workflow with clear roles, handoffs, controls, and a human-owned outcome.
Move from the definition to the operating decision without losing the thread.
Direct answer
AI agent orchestration coordinates which agent performs each task, what context and tools it receives, how agents hand work to one another, how the team observes execution, and where a human reviews the outcome. Good orchestration reduces context loss and permission sprawl; it does not add agents for their own sake.
What an orchestration layer controls
Orchestration controls routing, context, tool access, state, handoffs, review, and recovery across the workflow.
A model call is only one component. Teams also need to determine which runtime receives a task, which evidence follows it, what tools are available, how progress is reported, and what happens when the work is incomplete.
OpenAI's agent tooling emphasizes handoffs, guardrails, and tracing, while Anthropic recommends composable patterns and the simplest architecture that meets the task. Both point toward explicit workflow design rather than uncontrolled agent proliferation.
- 01Role and task routing
- 02Context packaging
- 03Tool and connector grants
- 04Run and task state
- 05Handoffs and escalation
- 06Human review and recovery
Start with one agent before building a multi-agent system
Use multiple agents only when specialization or independent critique improves the workflow enough to justify coordination cost.
One capable agent with good tools and a clear brief is easier to observe and evaluate. A second agent is useful when it has a genuinely different role, such as source researcher versus critic, or when a separate runtime is required for local repository access.
Avoid splitting a straightforward task into many personas. Every handoff can lose context, increase latency, and make accountability less clear.
- 01Add a specialist for a distinct evidence domain
- 02Add a critic for independent challenge
- 03Add a local runtime for machine-bound tools
- 04Do not add agents merely to simulate an organization chart
Production orchestration checklist
Validate task fit, permissions, observability, evaluation, fallback, and human ownership before expanding autonomy.
Run the workflow manually before adding event triggers. Confirm the agent has only the tools it needs, the reviewer can inspect the evidence, and failures produce an actionable state rather than silence.
Treat orchestration quality as an operating metric: fewer lost handoffs, better evidence, faster review, and more reusable outcomes.
- 01Bounded objective and owner
- 02Least-privilege tool access
- 03Visible run and task state
- 04Evaluation criteria
- 05Timeout and escalation path
- 06Human decision for consequential action
Primary sources
Product and practice claims in this guide are grounded in current first-party or standards sources. Raltic product claims are limited to shipped behavior described on this site.
FAQ