AI Agent

Technical explanation

An AI agent combines a model or decision engine with instructions, memory or state, tools, and an execution loop. It receives observations, plans or selects a next action, invokes permitted tools, evaluates results, and continues until it reaches a stopping condition. Agents may be interactive or operate as background processes. Their autonomy, permissions, memory, and ability to affect external systems should be explicitly constrained and monitored.

Business relevance

AI agents can automate multi-step knowledge and operational work that conventional rule-based workflows cannot handle reliably. They can reduce hand-offs and cycle time, but they also introduce risks involving access control, prompt injection, data leakage, unreliable decisions, and excessive agency.

Implementation example

A governed service agent classifies an incoming request, retrieves approved account information, drafts a response, and creates a CRM task. Refunds or contract changes require human approval before any external action is executed.

Limitations and common misconceptions

An AI agent is not inherently autonomous, accurate, or trustworthy. Performance depends on model capability, tool design, context quality, evaluation, and controls. Giving an agent broad permissions or vague goals increases operational and security risk.

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