Agentic systems follow a repeatable loop: gather data, reason about it, set or refine goals, decide the next action, execute it, then learn from the outcome. Orchestration coordinates multiple agents. The diagram below shows this flow at a glance.

Data comes from the environment: user input, APIs, databases, sensors. The system needs up-to-date information before it can decide.

The AI interprets the data and sets or updates goals. It may use NLP, vision, or other models to understand context and choose objectives.

It evaluates possible actions (e.g. which tool to call) and picks one. Then it runs that action—calling an API, querying a DB, or responding to the user.

After acting, the system can use feedback to improve. In multi-agent setups, orchestration assigns tasks, passes results, and handles failures so the overall goal is met.