What is an AI agent, and when does a business need one?

How an AI agent works

Every agent runs in a loop: it reads the goal and the context, decides the next action, calls a tool, observes the result and repeats until the task is done or a limit is reached. The model decides; what surrounds it, called the harness, defines what it can see, what it can do and when it must stop.

• Model: The LLM that reads the goal and picks the next action, such as Claude, GPT or Gemini.
• Tools: Functions the agent can call (tool calling), such as looking up an order in the ERP or opening a ticket; MCP (Model Context Protocol) is an open standard for exposing those tools.
• Context and memory: What enters the context window at each step, the short-term memory of the task and the long-term memory kept outside the model.
• Instructions and permissions: The agent's rules, what each tool may do and which actions require human approval.
• Checks: Tests, output validation and limits on steps and cost, which stop the agent from going further down a wrong path.

An example from our own work: Zorbit, Master Timm's own product, orchestrates AI coding agents through an MCP plugin with about 74 tools and audits the cost and the tokens of every run.

AI agent, chatbot or automated workflow: what is the difference

All three use language models; what changes is who decides the path. The right choice is usually the simplest one that solves the problem: a fixed workflow is more predictable and cheaper to test than an agent.

When a business needs an AI agent

An agent pays off when a task meets four conditions. If one is missing, a fixed workflow or a chatbot with RAG usually solves it for less.

• Several steps: The task requires looking things up, deciding and acting in sequence, not just answering a question.
• Action on systems: The work depends on reading or changing data in the ERP, the CRM or other APIs.
• A variable path: The next step depends on what the previous one found, and a fixed script would have too many exceptions.
• A measurable outcome: There is a definition of a correct result and someone on the business side to validate the answers.

Signs that it is not the time yet: the process changes every week, nobody can say what a correct answer is, or the data the agent would need cannot be reached through an API.

The risks of an AI agent and how to control them

An agent that acts on systems fails differently from a chatbot: instead of a bad answer, it can take a wrong action. The most common risks are predictable and have known controls.

• Prompt injection: Instructions hidden in an e-mail, a document or a web page try to divert the agent; the defense combines isolated system instructions, input and output validation and least-privilege permissions per tool.
• Wrong action: The agent changes the wrong record or acts when it should not; sensitive actions require human approval, and each tool can do only what the task needs.
• Unmeasured quality: The agent impresses in the demo and fails on real data; an evaluation dataset of real and edge cases runs on every prompt, model or tool change.
• Runaway cost: Long loops and repeated calls make each task expensive; step limits, caching and traces show the cost of every run.

On Mirador, The Hackett Group's generative-AI engine, we implemented prompt-injection defenses and an audit trail of the system prompt.

How to start: from use case to production

The safest path is to prove the value on one flow before giving the agent autonomy. The timelines below are typical ranges; discovery sets the ones for each project.

1. Pick a use case: A frequent task with a business owner and a clear definition of a correct result.
2. Discovery (1–2 weeks): Map the data, systems, integrations and risks, and define the architecture and the estimate.
3. Evaluation before code: Build the dataset of real questions, expected answers and adversarial cases.
4. Proof of concept (2–4 weeks): One end-to-end flow on real data, measured by the evals.
5. Production (6–12 weeks): Integrations, guardrails, evals in CI and traces of every run, with human approval for sensitive actions.