Learning Reflections: Kaggle’s 5-Day AI Agents Intensive with Google

Source: Dev.to

Overview

This submission reflects on the Google AI Agents Writing Challenge and summarizes my experience in Kaggle’s 5‑day AI Agents Intensive. The intensive transformed my view of large language models (LLMs) from single‑turn prompting to building systems that act, reason, and collaborate over time.

Key Insights

1. Agents are workflows, not prompts

The real power lies in orchestration: managing state, memory, tools, feedback loops, and evaluation. Prompting is merely the interface; the way components are wired together defines the agent’s capabilities.

2. Tool use unlocks real‑world impact

Choosing the right tools, designing schemas, and handling errors become first‑class concerns rather than afterthoughts.

3. Planning, reflection, and iteration matter

Effective agents plan tasks, reflect on intermediate results, and iterate to improve outcomes.

4. Multi‑agent systems amplify capability (and complexity)

Coordinating multiple specialized agents can achieve richer results, but also introduces additional coordination challenges.

5. Evaluation is hard—but essential

Robust evaluation strategies are required to surface failures, measure performance, and guide improvements.

Mindset Shift: From Prompt Engineering to Systems Engineering

• From single‑turn answers → multi‑step reasoning
• From static responses → adaptive behavior
• From monolithic models → modular, composable agents

This reframing makes agent design feel akin to building distributed systems, with language serving as the control plane.

Project: Multi‑Agent Research Assistant

• Planner Agent – Breaks down the overall task into manageable steps.
• Research Agent – Gathers and summarizes relevant sources.
• Critic Agent – Checks assumptions, identifies gaps, and validates findings.
• Synthesizer Agent – Combines the inputs into a coherent final answer.

Lessons Learned

• Clear role boundaries dramatically improve output quality.
• Naïve agent loops can explode in cost without proper stop conditions.
• Simple reflection steps catch hallucinations early in the pipeline.
• Simplicity wins – the most effective gains came from thoughtful structure rather than adding more agents.

Conclusion

The intensive sharpened both my technical skills and intuition. Agentic AI isn’t magic; it’s careful design, iteration, and evaluation. When done right, it unlocks a powerful new way to build intelligent systems that think in steps, use tools, and collaborate. I’m leaving the course excited to keep experimenting—moving from simple agents toward robust, production‑ready multi‑agent systems.