Building your first AI agent: a practical starting point
Agents are more than a chat loop. Here's a grounded way to think about tools, memory and guardrails before you write a line of code.
By Sofia Mendez
“Agent” has become one of the most overloaded words in AI. Before you build one, it helps to strip the term down to what it actually means: a model that can take actions in a loop, using tools, toward a goal.
Start with the tools, not the model
The single most important design decision is which tools you expose. A capable model with the wrong tools is useless; a modest model with the right tools can be remarkably effective.
For a first agent, keep the tool set small and well-described:
- One or two read tools (search, fetch).
- One write/action tool, scoped narrowly.
- Clear, typed inputs and outputs for each.
Guardrails are part of the design
An agent that can act is an agent that can act wrongly. Build the guardrails in from the start:
- Confirmation before any irreversible action.
- Bounded loops so the agent can’t run forever.
- Evaluation on a fixed set of cases so you catch regressions.
The question is never “can the agent do this?” — it’s “can we trust it to do this repeatedly, unsupervised?”
From demo to dependable
Most agent demos look magical and most agent products are mundane — and that’s the goal. The work that turns a flashy demo into something dependable is exactly what we cover in Building AI Agents: tool design, evaluation harnesses, and the guardrails that let you sleep at night.