BYD TechThe short answer
An AI agent is software that uses a language model to work toward a goal: it reads a request, decides the next step, uses tools such as a database or an email system, checks the result, and repeats until the job is done or it hands off to a person. What makes it an agent, and not a chatbot, is that it can act, not only answer.
Key points
- An agent is a model plus instructions, tools and a loop.
- It decides its own next step, within limits you set.
- Tools are what make it useful: looking things up and making changes.
- It fails differently from normal software: plausible, confident and wrong.
- Good agents are narrow, tested on real cases, and able to hand off.
A simple definition
Think of a capable new assistant who has been given a clear job, access to a few systems, and rules about what they may do without asking. An AI agent is the software version of that. You give it a goal, it works out the steps, and it uses the tools it has been given to carry them out.
Anthropic’s engineering guide describes agents as systems where a language model directs its own process and tool use, in contrast to workflows, where the steps are fixed in advance by code. That distinction, who decides the next step, is the heart of it.
How an AI agent works
- Read: it takes in the request and whatever context it has been given.
- Decide: the model chooses the next step, such as looking something up.
- Act: it calls a tool, for example a search of your orders or a draft email.
- Check: it reads the result and decides whether the job is done.
- Repeat or hand off: it continues, or passes the case to a person with a summary.
Everything else is detail around that loop: the instructions that set its job, the memory of what has happened so far, and the limits on what it may do.
The parts of an agent
| Part | What it is | Why it matters |
|---|---|---|
| Model | The language model doing the reasoning | Sets how well it understands and decides |
| Instructions | Its job, tone and rules, in writing | A vague job gets vague work |
| Tools | The systems it can read or change | Without tools it can only talk |
| Knowledge | Your documents and data it can consult | Keeps answers tied to your facts |
| Limits | Rules enforced in code | Stops a wrong decision from becoming a costly one |
| Handoff | A way to pass work to a person | Covers what it should not handle |
How agents reach tools is becoming standardized through the Model Context Protocol, covered in What is an MCP server?. How they use your documents is covered in RAG vs fine-tuning.
What agents are used for
- Customer support: triage, drafted replies, order lookups, handoff with a summary.
- Sales operations: updating the CRM from emails and calls, drafting follow-ups.
- Documents: reading invoices, orders and contracts and filling in the fields.
- Software work: writing and changing code under an engineer’s review.
- Research: gathering and summarizing information from many sources.
Where agents go wrong
Ordinary software fails loudly, with an error. An agent can fail quietly, by producing something that reads well and is wrong. That changes how you have to build and check one.
- It can state something false with confidence.
- It can be talked into ignoring its instructions by text hidden in a document or a message.
- A small mistake early in a long task can carry through every later step.
- Costs rise with every step it takes, so an agent with no cap can become expensive.
None of this makes agents unusable. It is why the reliable ones are narrow, tested against real past cases, limited in code, and able to hand off. The practical steps are in How to build an AI agent, and that is how we approach AI agent development.
Sources
Checked when this guide was written.
