TurtleanD
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Module 5: Agent Architecture

Agentic AI systems learning path

A concise learning map for agents: loop, tools, memory, planning, approval, reliability, routing, and coordination.

Back to program

Core idea

Agentic AI is a bounded work system, not a magic worker.

An agent observes, decides, acts, checks the result, then continues, stops, or asks a human.

Learn the layers in order so autonomy stays visible, testable, and accountable.

Learning ladder

  1. Loop: goal, state, action, result.
  2. Tools: search, read, write, and call APIs with clear permissions.
  3. Memory and state: separate current context, durable memory, task state, and noise.
  4. Planning: split work into steps without treating the plan as proof.
  5. Approval: require review for public, financial, account, data, reputation, or irreversible actions.
  6. Reliability: test behavior, inspect failures, and catch fluent wrongness.
  7. Routing: choose models by difficulty, risk, latency, and cost.
  8. Handoffs: use multiple agents only when coordination pays for itself.
  9. Artifacts: turn private learning into notes, diagrams, repos, or failure reports.

Simple example

A research assistant building a public reading list starts with a goal and a loop: find sources, read them, summarize them, check usefulness, and ask for review before publishing.

Tools let it search and save notes. Memory stores the learner level and source criteria. Planning separates discovery, evaluation, draft, and review.

Every added capability adds a verification duty.

Guardrails

This path is not a case for running agents by default. It is not a shortcut around review, permission, or responsibility. It is not an internal operations manual.

Keep examples generic, reversible, and safe to discuss in public.

Teach-back checkpoint

Answer in your own words:

  1. Why does the loop come before tools?
  2. What changes when an agent gains memory or state?
  3. Which actions need human approval first?
  4. When do multiple agents create more coordination cost than value?

Turtleand take

Teach agents as bounded work systems, not autonomous personalities. Add autonomy only when the control surface is clear.

Future lessons should link back here when they cover loops, tools, memory, planning, evaluation, routing, and coordination.