Most of the attention in agentic AI goes to the agent's reasoning capabilities. The model can figure out the right plan, break a goal into steps, and decide what to do next. Far less attention is given to the last step of that process, actually executing the action the model decided on. This “last mile” is taken for granted despite being the step that frequently causes agentic systems to quietly break in production. Tool calls give your agent hands.
It's the difference between a model that can only describe what it would do, and one that actually goes and does it, whether querying a database, filing a ticket, running a calculation, calling another service, and coming back with a real result instead of a guess. For an agent, tool calling isn't one feature among many, it's the entire mechanism through which reasoning becomes action. That also makes it prone to failure. Each step's output feeds the next step's input. So an agent that plans 5 steps and can only reliably execute 3 of them is as useful as one who doesn’t have access to any tools at all.
Every model calls tools in a slightly different way. If the tool call isn't formatted exactly right, the entire response can fall apart. 1: XML-style tags wrapping JSON, plain JSON, and function style tool call. Some models use specific tags or tokens to mark the call, others require a particular JSON shape and field names. A parser has to know, ahead of time, which shape a given model is going to produce.
This is made more difficult by models that mix chain-of-thought text in with the actual tool call, so the part of the response needed for the call is sitting right next to reasoning text that isn't meant to be parsed as instructions. In a single chat exchange, a badly parsed tool call is one bad answer. In an agent working through a multistep task, it's the first domino. The next step's plan depends on what the previous step actually did. It doesn’t matter what the model intended to do, only how the tool call was actually parsed and executed.