From Stochastic Parrots to Autonomous Loops

The Anatomy of an Agent

From Stochastic Parrots to Autonomous Loops

Day 1 · Phase 1

Where We Just Were

You can use an LLM. You've typed prompts and gotten answers.

But a chat model is a brain in a jar — brilliant, and completely unable to do anything.

Today we give it a body.

The Question This Hour Answers

What turns a model that talks into a system that acts?

The answer is a loop with four parts. Once you see them, you'll see them in every agent ever built.

First: Three Stages of Getting Help from AI

A quick map of how we got here.

Chat → You ask, it answers. You do everything in between.

RAG → It reads your documents first, then answers. Better informed, still just talking.

Agent → It answers and acts — and then looks at what happened and goes again.

Chat and RAG end after one reply. An agent doesn't end. It loops.

The Parrot vs. The Worker

A stochastic parrot predicts the next word convincingly. Ask it to paint your house and it describes painting beautifully — and your house stays the same color.

A worker picks up the brush, paints a stroke, looks at the wall, and adjusts.

The difference isn't intelligence. It's the loop between acting and looking.

The Four Components

Every agent, no matter how complex, is this loop:

Perception → Reasoning → Action → Memory → (repeat)

Think of a person doing any task:

• See what's in front of you
• Decide what to do
• Do it
• Remember what happened
• Look again

That's it. That's an agent.

Component 1 — Perception

Analogy: opening your eyes at the start of a task.

WHY — The agent can only reason about what it can see; blind action is just guessing.

WHAT — A snapshot of the current state of the world: files present, the last command's result, the error that appeared, what the user asked.

HOW — The relevant state is gathered and placed into the model's context as the input for this turn.

Bad perception → the agent acts blind.

Component 2 — Reasoning

Analogy: the moment of "okay, so I should…"

WHY — Something has to choose the next move; perception alone doesn't decide anything.

WHAT — A decision, not an answer: which action to take, with which inputs, given the goal and what was just perceived.

HOW — The LLM weighs the goal against the current state and outputs an intended action.

Reasoning is the brain — but a brain with no hands changes nothing.

Component 3 — Action

Analogy: the hands — picking up the brush and painting.

WHY — Without acting, the agent only talks; action is the line between a chatbot and an agent.

WHAT — Something that changes the world: a tool call, a command, a written file, an API hit.

HOW — The decided action is executed against a real system, and the world is now different than it was a second ago.

No action, no agency.

Component 4 — Memory

Analogy: remembering you already checked that drawer, so you don't check it again.

WHY — Without it the agent is an amnesiac, repeating the same mistake forever.

WHAT — A record of what happened: past results, decisions, and context carried forward.

HOW — Each loop's outcome is stored and fed back into perception, so the next loop builds on the last.

Memory is what makes a sequence of actions feel like progress.

The Loop, In One Breath

Perceive the world → Reason about the goal → Act on it → Remember the result → and look again.

Stop the loop when the goal is met. That stopping condition is part of the design — not an accident.

Why "Loop" Is the Whole Point

A chatbot runs once: in, out, done.

An agent runs until the job is done: act → check the result → correct → act again.

Self-correction isn't a feature bolted on. It's just the loop running more than once.

How Far Does the Loop Scale?

The same loop, given more, climbs a ladder (Google's agent taxonomy):

• L0 — model alone, no tools (a plain chatbot)
• L1 — model + tools (what you build today — StackLog)
• L2 — strategic, multi-step planning over many tools
• L3 — a team of agents, each other's tools (Day-2 "personas as subagents")
• L4 — agents that build their own tools and agents

Everything in this workshop is L1 → L2. Same anatomy; the loop just runs richer.

The Other Half of the Skill: Restraint

Knowing how to build an agent is half the job. Knowing when not to is the other half.

The most senior engineering instinct: "Does this actually need an LLM at all?"

When NOT to Use an LLM

Reach for plain code — not an agent — when:

• The task is deterministic (same input, same output, every time)
• A simple rule or formula already solves it
• You need guaranteed correctness (math, validation, money)
• Speed and cost matter and the logic is fixed

If an if statement can do it, an if statement should do it.

When an LLM Earns Its Place

Use a model when the task involves:

• Ambiguity — fuzzy input, no single right parse
• Language — understanding or generating natural text
• Judgment — weighing messy options with no clean rule
• Open-endedness — the steps aren't known in advance

LLMs are for the problems rules can't pin down. Everything else is just engineering.

The Architectural Filter

Before adding an agent to anything, ask:

1. Is the task ambiguous or open-ended? (If no → use code)
2. Does it need to act in a loop, or just answer once?
3. What is the smallest set of actions it needs?
4. How will I know when it's done?

An agent is a powerful, expensive tool. Use it where it's the right tool — not everywhere.

What This Means for Today

For the rest of Day 1, we build the part that makes the loop real:

• The model gives us Reasoning
• We must build Perception and Action safely
• That bridge — model to real world — is the harness (MCP)

Next: how do we give the agent hands without letting it break everything?

Anchor This

A chatbot answers. An agent perceives, reasons, acts, remembers — and repeats.

The loop is the agent. Everything else this workshop is about is making that loop safe and directed.