How to Design an AI Agent

Agents are built through a ten-step iterative evolution, not a single deployment event.

A durable agent is not built in a single deployment event. It is the product of a ten-step evolution that starts with doing the task yourself. You document the manual runbook. You use a language model as an assistant. You add tools through the Model Context Protocol so the agent can act on external systems. You teach it procedures through reusable skills, give it memory, and orchestrate its work through standard operating procedures. You package it as a named agent, schedule it, integrate it with production triggers, and only then scale to multiple specialized agents coordinated by an orchestrator.

The reasoning behind the sequence is operational. Non-determinism is a feature for adaptive tasks: when systems break in unpredictable ways, an agent can reason through logs and adjust where a hardcoded script cannot. But that flexibility only pays off if the underlying process is defined, which is what the manual stage guarantees. At each later stage the human moves right along the workflow, doing less direct work while holding more accountability, and still approving irreversible actions.

After this module you will know the five building blocks: the brain, the hands, the skills, the memory, and the manager, with a cron job as the alarm clock and the filesystem as the workspace. You will know that agent skills are reusable procedures written in markdown and small scripts, loaded only when relevant, which saves context space and reduces hallucinations. You will know that standard operating procedures use RFC 2119 keywords such as MUST and SHOULD, and that multi-agent systems follow sequential, routing, or swarm patterns, with the filesystem as a simple inter-agent channel.

The failure modes mirror skipped steps. Teams skip the manual stage and discover the process was never defined. They deploy agents that run in the background with no observability. They grant write access to production before the read-only version has been observed for a full cycle.

In one paragraph

A durable agent is the product of a disciplined evolution from manual work to autonomous operation. You start by doing the task yourself and documenting the runbook. You then use a language model as an assistant. You add tools through the Model Context Protocol so the agent can act on external systems. You teach the agent procedures through reusable skills. You give it memory. You orchestrate its work through standard operating procedures. You package it as a named agent. You schedule it. You integrate it with production triggers. Finally, you scale to multiple specialized agents coordinated by an orchestrator. At each stage, the human moves right along the workflow, doing less direct work but holding more accountability.

This module is part of the Agents and Tooling track in the free AI Learning Hub. Source material: How to Design an AI Agent.