The Short Answer
An AI agent is a system that pursues a goal on its own: it uses a language model to reason, then plans steps, calls tools, and evaluates results before continuing — where a chatbot answers once and stops. On ibl.ai you own all the code and the data, so the agent runtime is model-agnostic across any LLM and carries no per-seat pricing.
The rest of this page covers the loop every agent runs, the tools it calls, and where agents break.
What an AI agent is
An AI agent is a system that pursues a goal on its own. It uses a language model to reason, but adds the ability to plan steps, call tools, and evaluate results before continuing.
A plain chatbot answers and stops. An agent keeps going until the task is done or it needs a human.
The loop at the center
Every agent runs some version of the same cycle:
- Perceive — take in the goal and the current state from connected systems.
- Plan — break the goal into steps and decide the next action.
- Act — call a tool, API, or database to do something.
- Evaluate — check whether the action worked and what changed.
- Repeat — loop until the goal is met, then report.
This loop is what separates an agent from a single model call. The model is the brain; the loop is what lets it finish work.
Tools are what make it useful
On its own, a model can only produce text. Tools let an agent actually do things — query a database, file a ticket, send an email, update a record.
Connecting an agent to your real systems is where the value comes from, and also where control and security start to matter.
Types of AI agents
A few common patterns:
- Reactive agents respond to a trigger and act once.
- Goal-based agents plan multiple steps toward an objective.
- Multi-agent systems split work across specialized agents that coordinate.
- Human-in-the-loop agents act, then pause for approval on high-stakes steps.
Most production deployments mix these — routine work runs autonomously, and sensitive actions wait for a person.
Where agents run matters
Because agents take actions inside your systems and handle real data, where they run is part of the design, not an afterthought.
We build agentic AI you own and run on your own infrastructure: the agents act across your tools, but the data and the audit trail stay in your environment. You can browse concrete examples in the agent catalog.
The limits worth knowing
Agents are powerful but not magic. They can take wrong actions confidently, so the loop needs guardrails: scoped permissions, approval gates, and a full audit trail.
The teams getting value start narrow — one workflow, clear boundaries, real oversight — and expand as trust is earned.
Related: AI Agent Governance: Managing Autonomous AI Systems Responsibly
Related: AI Agent Security: How to Protect Autonomous AI Systems