AI Agent — IT definition

An AI agent is a software system that combines an LLM with the ability to plan steps, call tools, and execute actions to reach a user-defined goal. Unlike a chatbot that just answers, an agent acts: it reads a calendar, updates a ticket, queries a database, triggers a workflow, negotiates with another agent.

AI agents are the natural evolution of LLMs in 2025-2026. Per the Gartner Hype Cycle 2024, more than 33 % of enterprise applications will embed AI agents by 2028, up from less than 1 % in 2024. Salesforce reports that 70 % of IT decision-makers plan to embed AI agents in business processes in 2026.

Anatomy of an AI agent

An AI agent rests on five building blocks:

• •The model (LLM): GPT-4, Claude, Gemini, Mistral, Llama. It provides reasoning.
• •The tools: APIs, functions, databases, browsers, code execution. They let the agent act on the world.
• •Memory: short-term (the conversation context) and long-term (vector store, files, RAG).
• •The orchestrator: the loop that alternates between reasoning, tool calls, observation, and replanning (ReAct, Plan-and-Execute, Reflexion patterns).
• •Context: the IT estate, application, and organizational data the agent needs to understand to act well — typically via MCP.

AI agents vs chatbots vs RPA

• •LLM chatbot: generates text in response to a prompt. No external action.
• •AI agent: reasons about a goal, picks tools, executes, observes, replans.
• •Classic RPA: automates predefined steps, with no contextual understanding or adaptation.

Agents combine LLM flexibility (language, reasoning) with RPA's ability to execute.

Common agent patterns

• •ReAct agent: alternates Reason / Act — the agent reasons, acts, observes the result, reasons again.
• •Plan-and-Execute: a full plan is drafted first, then executed step by step.
• •Multi-agent: several specialized agents collaborate (planner, executor, critic). Frameworks: AutoGen, CrewAI, LangGraph.
• •Autonomous agent: long-running loop, can run for hours (AutoGPT, BabyAGI).

Enterprise use cases

• •Customer support: conversational agents that resolve simple tickets and escalate complex ones.
• •Sales: agents that qualify leads, write personalized emails, update the CRM.
• •IT / CIO: agents that diagnose incidents, propose fixes, open tickets, run procedures.
• •Finance: agents that reconcile invoices, send reminders, detect anomalies.
• •Engineering: copilots that code, test, deploy (Devin, Cursor, Cline).

Why application context is critical

An AI agent is only useful if it understands the company's IT estate: "which applications are used by which teams?", "who owns this service?", "how critical is this tool?". Without that context, the agent generalizes from internet knowledge — which produces hallucinations and wrong decisions.

That is precisely the role of a platform like Kabeen: expose the live context of the IT estate (applications, usage, cost, owners, risks) to both IT teams and AI agents through a unified interface — for instance an MCP server.

Security and governance

AI agents introduce new risks:

• •Over-permissioning: an agent with too many rights can cause large-scale damage.
• •Prompt injection: an attacker can hijack the agent through an instruction hidden in a document.
• •Runaway cost: an agent in a loop can burn thousands of tokens in minutes.
• •Audit and traceability: every agent action must be logged to reconstruct decisions.
• •[Shadow AI](/en/glossary/shadow-ai): employees deploy their own agents without IT approval.

Best practices: least privilege (IAM), human-in-the-loop on sensitive actions, sandboxing, monitoring, audit logs, and AI governance aligned with ISO 42001.