What is an AI Agent?
An AI agent is a software system that can perceive its environment, process information, make decisions, and take actions to achieve specific goals — often with varying degrees of autonomy. Unlike traditional software that follows fixed rules, AI agents can adapt their behaviour based on context, learn from interactions, and operate with minimal human supervision. In enterprise settings, AI agents range from simple chatbots to complex autonomous systems that manage workflows, make decisions, and interact with multiple tools and data sources.
Autonomy Levels
AI agents operate at different autonomy levels, each requiring different governance approaches. Tasker: executes specific, well-defined tasks with human direction (e.g., summarising a document, drafting an email). Automator: handles repetitive workflows autonomously within defined boundaries (e.g., processing invoices, routing support tickets). Collaborator: works alongside humans, suggesting actions and making decisions with human oversight (e.g., Microsoft Copilot, AI coding assistants). Orchestrator: coordinates multiple systems and makes complex decisions with minimal human intervention (e.g., supply chain optimisation agents, autonomous trading systems).
Enterprise AI Agents in Practice
Common enterprise AI agents include: Microsoft 365 Copilot (embedded across Office apps for content generation, analysis, and automation), custom LLM agents (built with frameworks like LangChain, CrewAI, or AutoGen for domain-specific tasks), customer service bots (handling inquiries, routing tickets, and resolving common issues), data analysis agents (processing datasets, generating insights, and creating reports), and code generation agents (writing, reviewing, and testing code). Each requires governance to manage risk, ensure compliance, and maintain quality.
Why Agent Governance Matters
As organisations deploy more AI agents, governance becomes critical. Without governance, there is no visibility into what AI systems are running, what data they access, what decisions they make, or whether they comply with regulations like the EU AI Act. Agent governance provides: a registry of all AI agents with their purpose, data access, and risk classification; approval workflows that ensure human review before deployment; continuous monitoring with audit trails; incident management when agents produce unexpected outputs; and compliance documentation for regulators.
Related Feature: Agent Governance
Fronterio provides built-in tooling for this.