Agentic Loop
The cycle an AI agent runs continuously: observe the data, notice what changed, investigate why, act within its permissions, and verify the action worked.
An agentic loop is what separates an agent from a script. Automation executes a fixed sequence someone wrote in advance; a loop is goal-directed: the agent decides its next step from what the data just showed it, then checks its own work. In marketing, a loop might watch signup conversion, notice a drop after a release, trace it to one traffic source, draft the report and flag the campaign. The five stages (observe, notice, investigate, act, verify) each depend on a capability of the stack underneath: unsampled data to observe, history to notice against, person-level joins to investigate with, an API to act through, and measurement to verify by. A loop on bad data does not fail loudly; it narrates wrong numbers confidently, which is why the data layer decides whether the loop is an asset or a liability.
Related terms
An autonomous system that uses an LLM to plan, execute, and iterate on tasks with minimal human intervention.
Coordinating multiple AI agents so their work composes: who runs when, what each is allowed to touch, and how their outputs feed each other.
The mechanism by which an AI model invokes external functions, APIs or systems: the difference between an agent that can only talk and one that can act.
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