Architecture Pattern
Agentic Orchestration
A loop where an AI agent reads input, decides an action, calls a tool, observes the result, and decides again — instead of a single-shot prompt and response.
What it is
Rather than one prompt producing one final answer, the system runs a loop: the model reasons about what to do next, calls a tool or API if needed, reads the result, and continues until the task is done or it needs human input. This is what lets an AI system take multi-step actions, not just answer questions.
When to use it
When a task genuinely requires multiple steps and decisions — triaging a ticket and taking action, researching then drafting, checking a database before responding — not a single question with a single answer.
Real tradeoffs
- —More capable than single-shot prompting, but harder to predict and debug — each step can go wrong in a different way
- —Needs an explicit fallback/escalation path, or errors compound silently across steps
- —Costs and latency scale with the number of steps the agent takes, not a single model call
Related
FAQs
How is this different from a normal automation workflow?
A traditional workflow follows a fixed script; agentic orchestration lets the model decide the next step based on what it observes, within boundaries you define. More flexible, and correspondingly more important to design a fallback path for.
Have a project in mind?
Tell us what you're trying to automate or build — we'll reply with next steps, not a sales pitch.