A Practical Introduction to AI Agents and Tool Use
What actually makes an AI system "agentic," how tool calling works underneath it, and where these systems still reliably break.
By The Internet Compass Editorial Team — Data Infrastructure Desk
The mechanism underneath the term
An AI agent, stripped to its mechanism, is a language model wrapped in a loop: given a goal, it decides on an action, an application executes that action (usually by calling a specific function or API), the result is fed back to the model, and it decides the next action — repeating until the goal is met or it gives up.
This is a meaningful step beyond a standard chat exchange, which produces exactly one response to one prompt with no ability to act on the world or revise its plan based on what happens next.
How the loop actually gets built
The plan-act-observe loop breaks down into distinct, buildable pieces.
- 1
Define the available tools explicitly
Every function the agent can call is described to the model in advance: its name, what it does, and what parameters it expects. The model can only take actions that map to a tool it's been given.
- 2
The model outputs a structured intent, not code
Given a goal, the model outputs a structured request — which tool to call, with which arguments — rather than free-form text. This is what lets the surrounding application parse its intent reliably.
- 3
The application executes the action, not the model
The model never runs anything itself. The calling application validates the request, executes the actual function or API call, and handles any errors — a deliberate safety boundary between deciding and doing.
- 4
The result is fed back as new context
Whatever the tool call returns — data, an error, a confirmation — is added back into the conversation, and the model decides its next step with that new information available.
- 5
The loop ends on a defined condition
A well-built agent has an explicit stopping condition: the goal is verifiably met, a maximum number of steps is reached, or the model determines it cannot proceed — rather than looping indefinitely.
Where these systems still reliably break
Per-step accuracy compounds across a multi-step task in a way that's easy to underestimate: a model that's correct 95% of the time on any single step is right on all ten steps of a ten-step task only about 60% of the time, if errors are independent. This is the core reliability challenge in agentic systems, not raw model capability.
The practical mitigations are consistent across production systems: constrain the tools available to only what a task genuinely needs, add explicit verification steps between consequential actions, and keep a human in the loop for anything with real-world irreversibility — sending an email, making a payment, deleting data.
Frequently asked questions
- Is an AI agent the same thing as an AI model?
- No — the model is the reasoning component. An agent is the model plus a defined set of tools, an execution loop, and (usually) constraints on what actions it's permitted to take. The same underlying model can power very different agents depending on how it's wired up.
- What is MCP and how does it relate to agents?
- The Model Context Protocol is a standard way for an application to expose tools and data sources to a model in a consistent format, so agent-building tooling doesn't need a custom integration for every different data source or tool it wants to use.