The Architecture of Agent Behavior

Mon, 01 Jan 0001 00:00:00 +0000

Chapters 1 and 2 introduced the working style of agent systems: conversation, variation, feedback, revision, and human direction. Now we need to look underneath that experience.

An agent does not become capable because a class is named Agent or because a prompt says “you are a researcher.” The behavior comes from the arrangement of parts around the model:

goal
  ↓
role + instructions
  ↓
context + state
  ↓
model proposes
  ↓
runtime validates
  ↓
tool / memory / message
  ↓
environment observation
  ↓
state update
  ↓
continue, revise, coordinate, or stop

That is the architecture of agent behavior. The model supplies interpretation and generation. The prompt and context shape what the model sees. The runtime owns state, tool execution, permissions, memory retrieval, orchestration, and stopping rules. The human operator supplies purpose, constraints, judgment, and responsibility.

Designing Your First Agent (On Your Phone)

Mon, 01 Jan 0001 00:00:00 +0000

The Goal

In this chapter, you will build your first small agentic system without writing code.

You will do it in an ordinary chat interface, even on your phone. The point is not that a chat thread is the final form of an agent platform. The point is that the core architecture from Chapter 3 can be practiced in plain language:

role
  ↓
task
  ↓
step
  ↓
tool-like action
  ↓
memory log
  ↓
reflection
  ↓
revision

This exercise builds a research assistant with a Manager role, a Researcher role, a visible Knowledge Log, and a review step.

Agent Roles on Programmer.ie: Modern AI programming

The Architecture of Agent Behavior

Designing Your First Agent (On Your Phone)

The Goal