Agentic AI

Traditional generative AI operates as a stateless text-in, text-out function: a user submits a prompt and receives a completion. Agentic AI wraps one or more foundation models inside a runtime control loop that perceives state, formulates a plan, invokes external tools, observes the resulting environment changes, and iterates until a termination criterion is met.

Under the hood, an agentic system relies on four architectural pillars: a reasoning core capable of task decomposition and self-correction; a tool layer connected via schemas or the Model Context Protocol; short-term working state paired with long-term episodic or vector memory; and deterministic guardrails that bound permissible actions. When a tool call returns an API error or an unexpected database row, the system reads the error trace and reformulates its next step rather than failing outright.

For example, a UK energy supplier uses agentic AI to handle disputed commercial meter billings. Instead of drafting a generic holding email, the system pulls half-hourly smart-meter telemetry, cross-checks tariff schedules in SAP, identifies a faulty multiplier after a meter exchange, calculates the credit adjustment, and queues the refund for a billing supervisor's one-click sign-off.

Agentic AI shifts enterprise return on investment from individual productivity nudges to end-to-end process execution. While a chat assistant might save an analyst ten minutes drafting a summary, an agentic system can resolve complete back-office tickets, reconcile financial exceptions, or triaging security alerts overnight, compressing multi-day operational queues into minutes.

The commercial risk lies in compounding error rates, unpredictable token bills, and autonomous side effects. If a single reasoning step is 95 per cent reliable, a ten-step autonomous chain succeeds only 60 per cent of the time unless intermediate verifiers are built in. Organisations should avoid fully autonomous agentic AI for high-precision, deterministic flows where a conventional state machine or a structured prompt pipeline delivers lower latency, lower cost, and total auditability.