AI Agent Development: What It Takes to Build Agents That Do Real Work

Video transcript

Agent development turns a language model into a system that pursues goals, calls real tools and finishes tasks inside your software.

The model is only the engine; value sits in tools, rules and tests. Four jobs remain: describe abilities, give reliable tools, constrain changes and prove messy input still works.

A chat agent answers and hands off; a task agent completes one defined job. An autonomous agent keeps working toward a goal with little supervision. Most businesses need the task agent first.

Every agent starts with a manual run-through of one narrow task. Gather real requests, because they arrive with typos and missing context. Code each tool with typed input, validation and error handling.

Use a managed platform for standard tasks and supported data. Go custom when the agent must call systems no builder exposes. Prototype on a platform, then rebuild parts needing depth.

Agents fail more on messy input and permissions than on reasoning. Build an evaluation set from real requests and run it after every change. Give each tool the smallest credential and route risky actions through approval.

The details are in the article.