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Welcome to the implementation phase — where we move from theory into building working AI agents. In this lesson you’ll translate architectural concepts into runnable agents, incrementally adding the capabilities that make agents useful in real-world scenarios.
A presentation slide titled "IMPLEMENTATION" (Section 03) with the subtitle "Where we start building" and two buttons labeled "FUNDAMENTALS" and "ARCHITECTURE." A presenter in a white T-shirt stands on the right side of the image.
This lesson assumes familiarity with the basic concepts and architecture of AI agents. If you need a refresher, review the fundamentals before proceeding: agent types, agent-environment interaction, and common architectural patterns.

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What is an agent?

An agent is a software component that perceives its environment, makes decisions, and acts to pursue goals. Practical agents combine several capabilities — perception, memory, planning, and execution — in a continuous decision-making loop.

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The core agent loop

Every agent follows a simple repeated sequence:
  1. Perceive — collect inputs from users, sensors, or external systems.
  2. Interpret — convert raw inputs into structured observations.
  3. Decide (plan/reason) — determine the next action or subtask.
  4. Act — execute an action (API call, message, or other effect).
  5. Learn/Remember — update memory, logs, or models based on outcomes.
Understanding this loop makes it straightforward to add features like persistent memory, multi-step planning, and error handling.

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What you’ll build in this lesson

You will implement a first working agent, then progressively enhance it with the components below:
  • Memory: let agents retain and query past interactions.
  • Planning & reasoning: enable decomposition of complex tasks into actionable subtasks.
  • Multi-agent coordination: orchestrate several agents to collaborate on shared goals.
We’ll introduce four instructive host characters — Zippy, Savvy, Meshy, and Cody — each designed to teach specific agent patterns. You’ll implement and wire up each agent, then observe them solving realistic problems.

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Agent components covered

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Practical topics and production considerations

We also cover engineering topics you’ll need when deploying agents:
  • Error handling and retries for robust operation.
  • Agentic design patterns (delegation, supervisor agents, orchestration).
  • Common frameworks and deployment patterns (examples below).
Useful references:
When building agents that act autonomously, design for safety: validate external actions, restrict privileged operations, and add human-in-the-loop controls where appropriate.
By the end of this lesson you will have built a set of functional agents that demonstrate memory, planning, and multi-agent coordination — ready to be extended or deployed to solve real problems.

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