AI Agents for Business: Choosing Tools Step by Step

Machine Learning Mastery has published a guide to deploying AI agents in business: how to pick the right tools for developing and running LLM systems.

Why tools shape an agent's behavior

The authors start with a concrete scenario: you have built an agent with five tools. That sounds like few. But even with that set, the model has to decide several things every time: which tool to use, in what order, when to stop, and whether to use a tool at all in the given situation.

Language models have no built-in "common sense" when choosing between similar functions. They rely on tool descriptions, examples in context, and their general understanding of the task. A poorly written description, or a redundant set of similar tools, sharply reduces an agent's accuracy and reliability, even if the underlying model is strong on its own.

The authors make a key practical observation: an agent with three well-described tools often solves tasks more accurately than an agent with ten loosely defined ones. It is one of the central ideas of the whole guide.

What matters when selecting tools

• One tool, one task: blurry boundaries between functions force the model to guess
• Write the description for the LLM, not the developer: be specific, with examples of inputs and expected output
• No duplication: two similar tools create ambiguity and reduce selection accuracy
• A minimal set: with each new tool, the difficulty of choosing grows nonlinearly
• Test before adding: check the agent against real queries before expanding the set

The number of tools does not by itself make an agent more powerful. Controllability and predictable behavior depend on the quality of the descriptions and the clarity of the boundaries between functions.

Why does a bad tool description break an agent?

A typical mistake is describing a tool the way you would write API documentation for a developer. That does not work for a model. It sees all available tools at once and must instantly understand what each one does and when it should be used.

"A tool description is a prompt inside a prompt.

It must be unambiguous and predictable in any context," the guide's authors say.

Practical recommendations:

• Include a concrete input format and the type of data returned
• State when the tool should not be used; this helps separate similar functions
• Avoid technical jargon: the description should make sense in the context of the user's request
• Test each description with several different queries before considering the tool ready

The guide also warns about "tool drift": when an agent in the middle of a complex task switches to an unsuitable tool, the whole chain of actions breaks. Recovering without reworking the descriptions or shrinking the set is extremely difficult.

What this means

Tool selection is an architectural decision that directly determines how an agent behaves in production. A minimal, clearly described set improves reliability and reduces errors on real queries. The authors recommend starting with the minimum and expanding the set only when a specific scenario requires it.

Why is tool selection critical for AI agents?

Tools determine an agent's behavior. When choosing, the model decides which tool to use, in what order, when to stop, and whether to use one at all. Language models have no built-in common sense when choosing between similar functions.

How many tools does an AI agent need?

Even five tools pose several tasks to the model: pick the right tool, decide the order of use, and understand when to stop.

Why is the right choice of tools critical for an AI agent?

Tools determine an agent's behavior. Language models have no built-in common sense when choosing between similar functions, so the design of the toolset is a key architectural decision.