What is an AI Agent? Definition and How It Works

An AI Agent is a system that not only discusses an assignment but independently handles it within a mandate. It plans work steps, uses approved tools, considers results, and continues working until the goal is reached, a follow-up question is needed, or a defined boundary applies. This creates technologically generated work capacity—not just another interface for text input.

How an Agent Works

Agentic work does not follow a rigid single step. The agent first understands the desired result and available context. It then selects a sensible next step, uses an approved tool, evaluates the result, and adjusts its approach.

This cycle is only commercially viable when the framework is established. An agent must not determine for itself which data, risks, or external effects are acceptable. This authority remains with the company.

Agent, Chatbot, and Classical Automation

• A chatbot conducts a dialogue and primarily provides answers for further handling by a person.
• Classical automation follows predefined workflows and conditions. It is powerful when the process is completely defined.
• An agent pursues a result with room for action. It can plan intermediate steps and respond to new information, but remains bound by mandate and policies.

These forms are not mutually exclusive. An agent can use a fixed workflow as a tool or ask follow-up questions through a chat. What matters is who carries the actual work and what control boundaries apply. The direct comparison is explored in depth at AI Agent vs. Chatbot.

What an Agent Needs in the Enterprise

Reliable deployment rests on several interconnected foundations:

• Mandate: a clear result, responsibility, and priority;
• Context: the business data and professional specifications relevant to it;
• Tools: only the capabilities the task area actually needs;
• Memory: reliable context beyond a single work step;
• Policies: rules for permitted and forbidden behavior;
• Escalation: a clear path for uncertainty, risk, and conflicting goals.

If any of these foundations is missing, independence quickly becomes ambiguity. The shared operating system is therefore more important than any single model: it connects context, tools, work state, and control.

Control Means Steerability

Permanent approval of every action turns an agent back into a tool waiting for each click. Good control works risk-based. Within a clear, reversible, and permitted scope, the agent can act independently. Risky, unclear, or explicitly protected steps are presented for decision.

webRichtung agent holds assignments, automations, policies, heartbeats, memory, integrations, and approvals together. You set the direction and boundaries; the agent carries the work within that framework.

Agents Are Themselves Customers of the Operating System

An agent does not need to use business capabilities through an interface built for humans. The gateway at connect.webrichtung.de provides MCP and API for this purpose. It makes 278 capabilities visible and 212 contracts callable. Through search_capabilities, describe, dry_run, and invoke, an agent can find capabilities, understand their contract, prepare a call, and execute it within its authorization.

Access is in beta and is activated by the operator. This is not an openly available backdoor, but a separate, controlled entrance for agents as first-class customers.

Delegate Step by Step

Begin with a task area whose good outcome and boundaries you can clearly define. Give the agent only the necessary context and capabilities. Review results and escalations, refine policies and memory, and only then expand the mandate.

This way, work capacity grows along reliable experience. You do not have to take every step—but you stay at the helm.