This is a submission for the Google AI Agents Writing Challenge: Learning Reflections.
Over the last five days, I took the Google x Kaggle AI Agents Intensive Course — and what started as “learning how to prompt better” quickly expanded into a complete understanding of how real AI agents think, act, store memory, collaborate, and evaluate themselves.
What surprised me most is how each day built naturally on the previous one, almost like watching a simple idea grow into a full intelligent ecosystem.
Below is my journey — day by day — with real-life analogies that helped me internalize the concepts.
🌱 Day 1 — From Prompt to Action (1A) & Agent Architecture (1B)
“A prompt is not the end. It is the ignition.”
On Day 1, I realized something fundamental:
A prompt is not a request — it is an instruction chain starter.
The first lesson showed how:
- prompts → goals
- goals → decisions
- decisions → actions
In real life, it felt like giving instructions to a personal assistant:
“Can you plan a birthday party for me?”
You don’t want a single answer —
you want:
- venue suggestions
- budget organisation
- guest list management
- timeline planning
This is what agents do.
They interpret the prompt as a multi-step workflow, not a single response.
🧠 1B — Agent Architecture (Expanded & Highlighted)
“If prompts are the spark, the architecture is the engine that makes an agent move.”
Agent Architecture was the first moment where I understood that an AI agent is not a chatbot.
It is a system composed of multiple interacting components — like a small intelligent organization.
🔹 The 4 Core Components of Modern Agent Architecture
1️⃣ Planner (the “brain”)
Interprets the request and converts it into structured steps.
A planner transforms vague human language → actionable plan.
2️⃣ Tools (the “hands and legs”)
Tools enable the agent to do things:
- search
- run code
- query APIs
- manipulate files
- analyze data
Intelligence becomes action only when tools exist.
3️⃣ Memory (the “long-term knowledge”)
Stores:
- user preferences
- prior steps
- facts
- context
This is what separates an agent from a chatbot.
4️⃣ Evaluator (the “quality inspector”)
Checks for:
- accuracy
- safety
- hallucinations
- correctness of tool usage
An evaluator makes the agent self-aware and self-correcting.
🔸 The 3 Major Types of Agent Architectures
One thing I appreciated was understanding that there isn’t just one architecture.
Different designs fit different needs.
1️⃣ Reactive Agents (simple responders)
- No planning
- No long-term memory
- Respond instantly Good for quick, rule-based answers.
2️⃣ Deliberative Agents (think → plan → act)
- Multi-step reasoning
- Tool usage
- Self-correction These feel closest to intelligent assistants.
3️⃣ Hybrid Agents (the best of both worlds)
They can:
- react quickly
- plan deeply
- remember patterns
- use tools This is what most advanced production systems use today.
🧩 The Agent Loop
The architecture works through a continuous cycle:
Input → Plan → Use Tools → Observe → Update Memory → Evaluate → Repeat
This loop makes agents feel alive — adjusting strategies dynamically until the task is complete.
By the end of Day 1, I found myself thinking less about “better prompts” and more about
how to architect intelligent systems with components that think, act, remember, and evaluate.
🧰 Day 2 — Agent Tools (2A) & Best Practices (2B)
“An agent without tools is a smart person with no hands.”
Tools turn agents into doers.
Examples:
- search APIs
- code execution
- file operations
- data extraction
Real-life analogy:
If Day 1 built the “brain,”
Day 2 gave the assistant a laptop, a phone, and the internet.
Best Practices
Key insights I took away:
- Give tools only when needed
- Define strict input/output formats
- Test tools independently
- Sandbox anything that could cause errors
Tools aren’t features—they are responsibilities.
🧭 Day 3 — Sessions (3A) & Memory (3B)
“Memory is the difference between a chatbot and a companion.”
Sessions
Sessions allow agents to:
- stay aware of the conversation
- continue tasks
- maintain context
Like telling a human:
“Let’s pick up where we left off.”
Memory
Memory was a breakthrough concept.
Agents can store:
- your preferences
- your style
- your earlier decisions
- the history of the workflow
Real-life analogy:
A personal trainer remembering your injuries, goals, and routines.
Memory transforms agents into something that can grow with you.
🔍 Day 4 — Observability (4A) & Evaluation (4B)
“If you cannot observe it, you cannot improve it.”
Observability
Agents need to expose:
- logs
- metrics
- errors
- internal reasoning
- tool usage
Just like monitoring production software, observability helps answer:
- Why did the agent behave this way?
- Where did a mistake happen?
- What step caused a failure?
Evaluation
Agents evaluate:
- correctness
- safety
- reliability
- latency
- cost
This is where agents become measurable, tunable, and improvable.
Like reviewing your work and improving your workflow.
🤝 Day 5 — Agent-to-Agent Communication (5A)
“One agent is powerful. Two agents are a team.”
On the final day, everything came together.
Agents can:
- delegate
- cross-check each other
- collaborate
- negotiate
- co-plan tasks
Real-life example:
Imagine multiple assistants:
- one finds hotels
- one checks reviews
- one books transport
- one optimizes budget Together → a flawless travel plan.
This showed me the future isn’t one super-agent.
It's ecosystems of specialized agents working together.
🌟 My Biggest Takeaways
✔ Prompts are not messages — they are architectures in disguise
✔ Tools turn agents into action-takers
✔ Memory creates personalization and continuity
✔ Observability brings reliability
✔ Evaluation ensures improvement
✔ Multi-agent systems unlock scalability
The course trained me to think like an AI systems architect, not just an AI user.
💡 Final Reflection
I entered the course thinking:
“I want to learn how AI agents work.”
I finished the course thinking:
“I want to build AI agent ecosystems that mirror real-world teamwork.”
The progression from
prompt → architecture → tools → memory → evaluation → agent-to-agent orchestration
changed how I view AI completely.
Agents aren’t just chat interfaces.
They are self-improving collaborators that can scale workflows, automate complexity, and amplify human capability.
This course didn’t only teach me concepts —
it reshaped how I view the future of intelligent systems.
Thanks to Google, Kaggle, and the Dev community for this opportunity to grow, learn, and build.
Top comments (0)