Learned about Agentic AI from Google & Kaggle

💡 Foundational Learning: The Architecture of AI Agents (Whitepaper) by #Google and #Kaggle If you're exploring how to move past simple LLM prototypes to building actual, robust, autonomous AI systems, this foundational whitepaper is essential reading. This resource helped me deeply understand: 1. The Agent's Anatomy: Defined as a Model (Brain), Tools (Hands), and Orchestration Layer (Nervous System). 2. The Autonomous Loop: How agents function using the continuous "Think, Act, Observe" cycle. 3. Agent Ops: Why a dedicated operational discipline is necessary for reliable and secure deployment of agents in production. 4. Taxonomy: A helpful breakdown of agents from a simple Level 1 Connected Problem-Solver to a Level 3 Collaborative Multi-Agent System. I'm committed to growing my AI expertise, and I believe in the power of shared knowledge. Grab this paper, and let's fuel each other's learning in this exponential space! 🤝🧠 Do watch this Youtube video for quick understanding: https://lnkd.in/gDxSNqcp ➡️ The Whitepaper is attached to this post below! #AI #AIAgents #MachineLearning #TechLearning #Kaggle #Google #AICommunity

Day 3 of the Kaggle × Google AI Agents Learning Journey Today’s focus was on Context Engineering: Sessions and Memory, exploring how agents retain context, learn from it, and build more natural conversations. I learned how to make agents stateful using sessions that capture conversation history, allowing them to understand follow-ups without losing context. Then I extended this with long-term memory, enabling agents to recall information across sessions and personalize their responses. It was fascinating to see how these ideas move agents beyond one-off interactions toward real contextual intelligence. Key learnings from Day 3: - Context engineering enables coherent, multi-turn conversations. - Memory turns agents from reactive tools into adaptive systems. - Combining short-term and long-term context creates more human-like AI experiences. A big thank you to Kanchana Patlolla, Anant Nawalgaria, Steven Johnson, Kimberly Milam, Julia Wiesinger, and Jay Alammar for the insightful and engaging Q&A session. Your perspectives made complex ideas easy to connect with. Thank you Maddula Sampath Kumar for the codelab and Kristopher Overholt for walking through the code. Each day in this journey reinforces how context and memory are key to building the next generation of intelligent, reliable, and collaborative AI systems. #AI #Agents #Gemini #Kaggle #GoogleAI #MachineLearning #LearningJourney #Innovation #ContextEngineering #GoogleADK

Catching up on posting this, but Day 4 of the Google × Kaggle AI Agents Intensive Course (on 13th) turned out to be one of the most insightful days for me. I spent the session understanding how observability and evaluation really shape reliable, real-world AI agents. Here’s what stood out to me: • How logs, traces and metrics actually show what an agent is doing internally • Why evaluation is very different from standard testing — especially for dynamic, non-deterministic systems • How real-world performance metrics help ensure accuracy and responsiveness • How building + debugging an AI-based home automation agent made these concepts feel practical instead of just theoretical Each day of this course is giving me a clearer picture of what it really takes to build production-ready, trustworthy AI systems. 🚀 #AIAgents #Google #Kaggle #MachineLearning #AI #AgentEvaluation #Observability #GenAI #LearningJourney #ArtificialIntelligence #TechLearning

🌟 Day 3 Complete: #Kaggle x #Google 5-Day AI Agentic Course Today’s focus was on something that makes artificial intelligence feel a little more human: memory. I learned how to help an AI agent remember information, continue conversations naturally, and use past context to make smarter decisions. 💡 What I Learned Today: ✅ Context Engineering I explored how to organize information for the model so that it always knows what is most important in a conversation. Instead of repeating everything each time, the system learns to keep only what truly matters. ✅ Sessions and Events I learned how to maintain a continuous conversation with the agent. This means it can remember what was said earlier and respond in a more natural and connected way instead of starting fresh every time. ✅ Persistent Storage This part was really interesting. I saw how conversations can be saved and restored even after the system is restarted. It feels like giving the AI a memory that stays with it between uses. ✅ Session State Here I learned to keep track of small but important details, such as user choices or information mentioned earlier. This helps the agent make consistent decisions later on. ✅ Building Memory I gave the agent a real memory system using a memory service. It can now save information from previous sessions, search through what it has seen before, and bring back relevant details whenever needed. There are two ways it can do this: • In a reactive way, where it decides when to recall something. • In a proactive way, where memory is automatically loaded at the start of the conversation. ✅ Memory Consolidation This is where the agent summarizes what it has learned from many conversations and keeps the most useful knowledge. We used tools that act like an organized memory bank, helping the system grow more capable over time without forgetting older information. By the end of the day, my agent could hold meaningful, continuous conversations, remember earlier discussions, and respond more intelligently based on past experience. This step made me realize that true progress in AI is not only about reasoning or coding. It is about creating systems that can learn, remember, and understand context just like we do. #GoogleAI #Kaggle #AIAgents #AgenticAI #Gemini #VertexAI #AIInnovation #MachineLearning

✨ 5-Day Gen AI Intensive Course with Google – Day 3 Completed! 🤖 Continuing my learning journey with the 5-Day Generative AI Intensive Course by Google, and today’s session was all about Generative AI Agents — one of the most exciting areas in modern AI development. ✅ Day 3 Highlights Today’s focus was on building sophisticated AI agents by understanding their core components and the iterative development process. We explored: • The architecture and lifecycle of AI agents • Techniques for goal-oriented task execution • Insights into the latest Agentspace developments and real-world applications Hands-on codelabs provided practical experience in developing and refining intelligent agents that can plan, reason, and act autonomously. 📍 Kaggle Codelab Links: https://lnkd.in/gJKy2v2d https://lnkd.in/gR_j5jXu Excited to continue with Day 4 — diving deeper into advanced GenAI concepts! #GoogleAI #GenAI #AI #Agents #AIagents #Gemini #ArtificialIntelligence #LearningJourney #Upskilling #AIInnovation

💡 Day 3 of the 5-Day AI Intensive Program! Today’s session took us deeper into the world of Agentic AI — exploring how to integrate APIs, set up environment keys securely, and build intelligent agents that can interact with data autonomously. 🔐 Learned how to: Configure and manage API keys safely using Kaggle Secrets Integrate Google’s Gemini API in a notebook environment Understand the role of agents and LLMs in automating workflows Every day, the concepts are becoming more practical and exciting — bringing AI from theory to real-world application 🚀 #AI #MachineLearning #AgenticAI #GeminiAPI #Kaggle #LearningJourney #Day3 #ArtificialIntelligence

🚀 Day 1 – Google × Kaggle AI Agents Intensive Course A great start to exploring how AI agents think, plan, and act using real-world tools. Here’s what I did today. 👇 🧠 Theory: Started with Unit 1 – Introduction to Agents. Read the foundational whitepaper and listened to the summary podcast to understand how agents make smart decisions, reason through environments, and use data to take meaningful actions. 🛠️ Practice:  Jumped into the Kaggle codelabs to:  • Build my first AI agent using Gemini + ADK  • Create a simple chatbot agent capable of answering queries  • Explore how multiple agents collaborate in a multi-agent system  • Learn how agents use tools like Google Search to solve real-world problems Huge thanks to Google and Kaggle for creating this amazing learning experience. #Google #Kaggle #AI #AgenticAI #Learning #ArtificialIntelligence

🚀 Google just dropped a new 50-page doc on building agents that actually work in the real world. It's a fast introduction to the theory of what you must know about agents. It covers: → core agent architecture → LLM (the brain behind agents) → tools (the hands of the agents) → multi-agent orchestration → how to deploy agents → evaluation and metrics → self-evolving learning agents → how agents evolve and learn → covering AlphaEvolve example You can download and read it from Kaggle: 🔗 https://lnkd.in/eCiVfAEk #GoogleAI #AIAgents #LLM #AI #MachineLearning #AIResearch #DeepLearning #AIInnovation #MultiAgentSystems #FutureOfAI #Kaggle

I’ve been astounded by how good some of Google’s videos on Machine Learning are. This one uses the example of plotting travel time through a city to explain Linear Regression, Loss, and Gradient Descent — showing how AI optimizes for the best answer. Highly recommend it for anyone else interested in what happens under the hood. 😎 https://lnkd.in/gDQ_SR_V #MachineLearning #AITransformation #GoogleAI #AIEducation #DigitalInnovation #DataScience #LinearRegression #GradientDescent #ContinuousLearning #AIinBusiness #TechLeadership #LearningInPublic

✨ Day 1 of Google's 5 Day Gen Ai Intensive Course Started working with Google’s Agent Development Kit and built my first AI agent with tool-calling abilities. The shift from simple prompts to agents that can reason and take action is a meaningful step toward real applied AI. Looking forward to exploring more complex workflows over the next few days. If anyone wanna know how : https://lnkd.in/eFRjUwyS #GoogleADK #AiAgent #Day1 #GenAI #Kaggle #Connections