5 Days to Clarity: Demystifying AI Agents

Source: Dev.to

Overview

Before enrolling in the 5‑day AI agents intensive, I only knew the textbook definition of an agent. I expected to learn the basics, but the course quickly moved from theory to hands‑on labs where code came to life. By the end of the fifth day I was studying best practices for deploying an agent.

The whitepaper used a simple analogy: the model is the agent’s brain, tools are its hands, the orchestration layer is the nervous system, and deployment is the body and legs. This helped me visualize the Think → Act → Observe loop that runs behind a prompt to ChatGPT. It also introduced the idea of self‑evolving, agentic systems that can create new tools or agents at runtime.

Day 1 – Foundations

• Analogy: Model = brain, tools = hands, orchestration = nervous system, deployment = body/legs.
• Key concept: The “Think, Act, Observe” loop that powers an agent’s behavior.
• Insight: Agents can become self‑evolving systems, expanding their resources by generating new tools or agents on the fly.

Day 2 – Documentation & Integration

• Realization: Building AI agents isn’t just about technical know‑how; documentation and best‑practice protocols matter.
• Problem highlighted: The “N × M” integration challenge, where many agents and tools interact, can quickly become chaotic.
• Solution introduced: The MCP (Multi‑Component Protocol) to manage complex integrations.

Day 3 – Memory & Context Engineering

• Questions explored:
  1. If I tell the agent my favorite color is blue, will it remember that later?
  2. How does the agent update its knowledge when preferences change?
  3. Does it retain greetings like “Good morning”?
• Answer: An agent without memory is like an assistant with amnesia. Sessions and memory are essential building blocks for context engineering, regardless of the agent’s specialization.

Day 4 – Debugging & Evaluation

• Comparison: A calculator has a single correct answer (2 + 3 = 5), whereas a writer agent’s output is open‑ended.
• Challenge: Verifying correctness and tracing the agent’s reasoning process—did it call the right tools? Did those tools provide accurate information?
• Approach:
  • Implement LLM‑as‑a‑judge to automate evaluation.
  • Introduce a human‑in‑the‑loop for added reliability.
• Takeaway: Debugging an agent can be more complex and ongoing than building it.

Day 5 – Deployment & Multi‑Agent Communication

• Focus: Deploying agents and the A2A protocol, which enables different agents to “talk” to each other.
• Goal: Build agents that real‑world businesses can depend on, with continuous evaluation to maintain trustworthiness.
• Reality check: Fully trustworthy agents are still out of reach; human oversight remains essential.

Post‑Course Project

After the intensive, I spent the next 15 days building a project that applied all the learnings—from single‑agent design to multi‑agent systems, evaluation, and deployment.

Practical Example: SketchSensei

For anyone who has tried to draw a realistic human head and struggled with orientation and proportions, SketchSensei offers a solution. It overlays Loomis guidelines on an input image and generates step‑by‑step drawing instructions, letting you pick up a pencil and draw the head the Loomis way.

Acknowledgements

Thank you to Google × Kaggle for providing this course and equipping it with all the material needed to bring these concepts to beginners.