Agentic AI

Technical explanation

Agentic AI architectures organise one or more AI agents around an observe–reason–act loop. They may decompose goals into tasks, call APIs or software tools, maintain working state, evaluate intermediate results, and coordinate specialised agents. The defining characteristic is goal-directed action under bounded autonomy, not simply the use of a large language model.

Business relevance

Agentic AI can automate less deterministic processes such as research, case handling, system coordination, and exception resolution. Its value comes from reducing manual orchestration, while its risk comes from compounding model errors across multiple steps and actions.

Implementation example

An agentic revenue-operations system investigates a pipeline anomaly by querying CRM data, checking campaign activity, identifying a broken lifecycle rule, proposing a repair, and waiting for approval before changing production automation.

Limitations and common misconceptions

The term is used inconsistently and is sometimes applied to ordinary chatbots or scripted automation. Multi-step autonomy increases latency, cost, observability requirements, and the potential impact of erroneous or manipulated outputs.

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