Agentic AI describes a class of artificial intelligence systems capable of autonomous, goal-directed behavior. Unlike traditional AI models that respond to a single prompt and produce a single output, agentic AI systems can break down complex objectives into steps, use tools and external data sources, make decisions, and iterate toward a goal — without requiring a human to guide each action.
The term "agentic" comes from the concept of agency — the capacity to act independently in pursuit of a goal. An agentic AI system doesn't just answer questions; it takes actions.
How agentic AI differs from generative AI
Generative AI (like a chatbot or image generator) responds to a single input and produces a single output. The interaction is stateless — each prompt is treated independently.
Agentic AI maintains state across multiple steps. It can search for information, analyze results, make decisions based on what it finds, trigger actions in other systems, and loop back to refine its approach. Think of the difference between asking someone a question versus giving them a project and letting them run with it.
Common agentic AI design patterns
- ReAct (Reason + Act) — the agent reasons about what to do, takes an action, observes the result, and repeats
- Tool use — agents are given access to external tools (search, databases, APIs, code execution) they can call as needed
- Multi-agent systems — multiple specialized agents collaborate, with one orchestrating the others
- Memory and context — agents maintain memory of prior steps to inform future decisions
Agentic AI in enterprise data workflows
In enterprise contexts, agentic AI is increasingly being applied to data workflows — automatically identifying anomalies in financial data, generating and distributing reports without manual intervention, initiating procurement workflows when inventory thresholds are hit, or answering complex business questions by querying multiple data sources in sequence.
For agentic AI to work reliably in production, it needs access to high-quality, real-time, well-governed data. Agents that query stale or fragmented data produce unreliable outputs — which is why the underlying data platform matters as much as the AI model itself.
Agentic AI and Incorta
Incorta's platform is designed to support agentic AI workflows by providing direct, real-time access to enterprise application data — from Oracle, SAP, Workday, Salesforce, and more — without the data quality issues and latency that plague traditional data warehouse architectures. Incorta's semantic layer gives AI agents the business context they need to query data accurately, and its governance controls ensure agents only access data they're authorized to see.
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