Agentic AI Starter — Build Guide

A minimal, production-shaped agent has four parts: a planner/loop, tools, memory, and guardrails. This guide walks through each so you can build the scaffold yourself in an afternoon — before you ever need a framework.

The runnable GitHub template that mirrors this guide is being published right now — leave your email below and you'll get the link the day it's public. Everything you need to build it yourself is here in the meantime.

1 · The agent loop

The core loop is simpler than it looks:

observe → decide (call a tool or answer) → act → observe result → repeat

Implement this as an explicit loop you can read and step through — not a black box. Cap the number of iterations (e.g. 6–10) so a confused agent can't loop forever, and log every step (what it decided, why, what happened).

2 · Tools

• Define each tool with a strict schema (name, description, typed arguments).
• Validate arguments before execution — never trust model-generated input to a tool that has side effects.
• Keep tools narrow and composable (get_order, issue_refund) rather than one giant do_anything tool.
• Return structured results, not free text, so the model can reason over them reliably.

3 · Memory

• Short-term: the conversation/task history, windowed to fit the context budget — summarize or drop the oldest turns rather than truncating blindly.
• Long-term (optional): a vector store or key-value store for facts that should persist across sessions (user preferences, prior decisions).
• Be explicit about what the agent is allowed to remember and for how long — memory is a privacy surface, not just a feature.

4 · Guardrails

• Input validation: reject or sanitize obviously malicious input before it reaches the model.
• Action confirmation: anything irreversible or money-moving gets a confirmation step (human-in-the-loop) or a hard cap, not a bare tool call.
• Output constraints: validate the model's output against the tool schema before executing it — malformed or out-of-scope calls get rejected, not run.
• Prompt-injection defense: treat tool outputs and retrieved documents as untrusted data, not instructions. The model should never follow a command embedded in a tool result.

5 · Suggested repo layout

agent/
├─ loop.py            # the observe → decide → act loop
├─ tools/
│  ├─ registry.py      # tool schemas + validation
│  └─ ...              # one file per tool
├─ memory/
│  ├─ short_term.py
│  └─ long_term.py      # optional
├─ guardrails/
│  ├─ input_checks.py
│  └─ output_checks.py
├─ tracing.py          # log every decision, tool call, and result
└─ eval/
   └─ scenarios.py     # scripted tasks the agent must complete correctly

6 · What to build first

1. One tool, no memory, hard iteration cap. Get the loop working end to end.
2. Add tracing before you add a second tool — you'll need it to debug.
3. Add guardrails on the first tool with side effects.
4. Add short-term memory once multi-turn tasks show up in your eval scenarios.

Want this built out further — a full agent framework comparison, deployment patterns, and a graded project? That's in the AI Engineer Interview & Portfolio Kit.