AI product leader at Google, Author, and community builder making complex AI concepts simple for technical and business audiences. Published author of two books on GPT-3 and Neural Search. Lead and manage an active community of 200K+ AI developers across my social media channels and Unwind AI platform. Creator of Awesome LLM Apps, a #1 top-trending GitHub repository with 80k+ stars featuring practical AI Agents and RAG tutorials.

Building a community of 1M+ AI Developers at Unwind AI, an open AI education platform where 200k+ AI developers today learn to build with AI in just 3 minutes a day:
→ Daily AI news that actually matters
→ Code-first tutorials on AI Agents, RAG and LLMs
→ Open-source AI Agent and RAG apps you can use today with full code implementation

Author of books on: 
→ "GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API"
→ "Neural Search - From Prototype to Production with Jina"

Previously:
→ Built Tenstorrent's developer community from scratch & launched first generation of AI hardware products from AI Accelerator cards to AI Desktop Workstations.
→ Built AI systems for Wall Street hedge funds @Arcesium (DE Shaw)
→ Led data initiatives at 256 Network
→ Tech Leaders Fellow @Plaksha

I share daily tips and tutorials on:
→ Building production-ready LLM apps
→ Implementing RAG systems
→ AI Agents and opensource agentic frameworks

🤝 Let's connect if you're:
→ Building with AI and need practical guidance 
→ Looking for ready-to-use LLM implementations
→ Want to stay ahead of AI developments and grow your career in AI

Follow me for daily AI insights and tutorials on AI Agents, RAG and LLMs.

We just ran the biggest AI Agent course ever 🤯

Here's the list of all the resources we released from Day-1 to Day-5.

250+ pages of production AI Agent insights. 100% free.

📌 Day 1: Introduction to AI Agents (50 pages)
Most agent demos fail in production. This covers the 5 levels of AI agents, from basic reasoning to self-evolving systems, plus the security and architecture patterns that actually work at scale.

Link: https://lnkd.in/dQSzVjR7

📌 Day 2: Agent Tools and MCP (50 pages)
MCP servers can add new tools without your approval. Your poetry bot suddenly gains purchasing power overnight. This breaks down tool design, the Model Context Protocol, and the security risks hiding in plain sight.

Link: https://lnkd.in/dUztxWDA

📌 Day 3: Context Engineering and Memory (70 pages)
Agents with zero long-term memory treat every session like meeting you for the first time. Learn how to build memory systems that actively curate context, not just save conversations.

Link: https://lnkd.in/dMqjwrqF

📌 Day 4: Agent Quality (50 pages)
Traditional QA doesn't work for agents. You can't unit test judgment. This covers the 4 pillars of agent quality (effectiveness, efficiency, robustness, safety) and how to actually evaluate the reasoning process.

Link: https://lnkd.in/dQR3_HkG

📌 Day 5: Prompt to Production (40 pages)
Building an agent takes 2 weeks. Trusting it in production takes 6 months. Learn evaluation gates, circuit breakers, and evolution loops that close the gap between demo and deployment.

Link: https://lnkd.in/dVHSeWmE

With insights from experts including Antonio Gulli, Alan Blount, Michael Zimmerman, Vladimir Vuskovic, Michael S., Kanchana Patlolla, Santiago (Sal) Diaz, Madhurranjan Mohaan, Kimberly Milam, Meltem Subasioglu, Turan Bulmus, Wafae Bakkali, PhD, Dr. Sokratis Kartakis, Gabriela Hernández Larios, Ran Li, Elia Secchi, and Huang Xia 🔥

Kudos to Kanchana Patlolla, Anant Nawalgaria and Polong Lin for the amazing organization 🙌

The best part?
250+ pages. Zero fluff. 100% free.

Repost this so your network doesn't miss it ♻️

Shubham Saboo

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 80k+ stars) | 3x AI Author | Views are my Own

No alternative text description for this image

I just open-sourced 100+ AI Agent & RAG tutorials that others charge $10,000+ for...🤯

100% FREE. 100% Opensource. Zero paywalls.

Here's what 200K+ AI developers are building with these tutorials:

Starter AI Agents (Pick Your First Project):
• AI Blog to Podcast Agent
• AI Travel Agent (Local & Cloud)
• AI Data Analysis Agent
• AI Music Generator Agent
• AI Meme Generator Agent (Browser)
• Mixture of Agents

Advanced AI Agents:
• AI Deep Research Agent
• AI Investment Agent
• AI Movie Production Agent
• AI System Architect Agent
• AI Product Launch Intelligence Agent
• AI Home Renovation Agent with Nano Banana

Multi-Agent Teams:
• AI Legal Agent Team 
• AI Game Design Agent Team
• AI Teaching Agent Team
• AI Recruitment Agent Team
• AI Real Estate Agent Team
• Multimodal Design Agent Team
• AI Competitor Intelligence Agent Team
• Multimodal UI/UX Feedback Agent Team with Nano Banana

Autonomous Game Playing Agents:
• AI Chess Agent vs Agent
• AI 3D Pygame Agent
• AI Tic-Tac-Toe Agent

Voice AI Agents:
• Customer Support Voice Agent
• AI Audio Tour Agent
• Voice RAG Agent 

RAG Systems (Every Type Covered):
• Agentic RAG with Reasoning
• Agentic RAG with Embedding Gemma
• Corrective RAG (CRAG)
• Autonomous RAG
• Contextual AI RAG Agent
• Vision RAG
• RAG with Database Routing
• Gemini Agentic RAG

Complete Framework Crash Courses:
• Google ADK Crash Course
• OpenAI Agents SDK Crash Course

Every tutorial includes:
→ Step-by-step instructions
→ Real-world application
→ Opensource code on GitHub

Why am I giving this away for free?
Because I believe the best AI knowledge shouldn't be locked behind paywalls.

Get started in 2 simple steps:
1. Subscribe to Unwind AI (for free): theunwindai.com
2. Star the repo: https://lnkd.in/dW6b_dEn

New AI Agents and RAG tutorials added every week.

P.S. I create these AI tutorials and opensource them for free. Your 👍 like and ♻️ repost keeps me going.

Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.

Google just launched Code Wiki to turn any GitHub URL into instant docs  🔥

Code Wiki automatically generates, updates, and maintains documentation for your entire codebase using Gemini agent.

No more outdated docs. No more hunting through files for hours.

Here's what it does:
• Scans your repo after every commit and regenerates docs automatically
• Gemini chat that knows your entire codebase end-to-end
• Click any concept and jump straight to the exact code file
• Auto-generates architecture and sequence diagrams that stay synced
• Works with any public GitHub repository

Reading and understanding existing code is one of the biggest bottlenecks in software development.

Code Wiki bridges the gap between learning about the code and actually exploring it.

New contributors can make their first commit on Day 1. 
Senior devs can understand new libraries in minutes instead of days.

The best part? 
100% free in public preview.

Private repo support coming soon via Gemini CLI extension.

Link to try it out in the comments.

🥑 AI Product Manager at Tenstorrrent focused on bridging the gap between state-of-the-art software and the specialized hardware built for AI. 

📕 Co-author of "GPT-3: The Ultimate Guide To Building NLP Products With OpenAI API" and "Neural Search - From Prototype to Production with Jina"

🗞️ Run a daily AI newsletter "Unwind AI" that delivers the latest AI developments and best AI tools directly to your inbox.

🤝 Senior AI Evangelist at Jina AI responsible for building the next generation of AI ecosystem for developers to build and deploy multi-modal/cross-modal applications in the cloud.

🤖 Co-founded Kairos Data Labs to minimize the gap between state-of-the-art AI innovations and applied AI applications within organizations. Our mission is to accelerate innovation with the widespread adoption of AI.

👨‍💻 Founding Data Scientist responsible for creating organization-wide data strategies and infrastructure to build & scale the data science and machine learning vertical within Arcesium (The DE Shaw Group). Built state-of-the-art AI solutions in the area of post-technology services for the biggest hedge funds of the world.

💵 Lead the data initiative at 256 Network, to build a data-driven ecosystem of global decision-makers investing in the startup ecosystem.

📈 Part of the founding cohort at Plaksha's Tech Leaders Fellowship (https://plaksha.org/techleaders)

🤝 Let's Connect: Follow me to stay on top of what's happening in the AI world. Your next insight or inspiration is just a connection away.

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 79k+ stars) | 3x AI Author | Views are my Own

Google's repository of production-grade AI agents just went public 🔥

Agent Garden is now available to all developers.

Agent Garden is a curated repository of ready-to-deploy AI agent samples built with the Agent Development Kit (ADK).

It solves one of the biggest pain points in agent development: the upfront design and integration work that slows teams down.

Here's what you get:
→ Pre-built agent samples with full source code on GitHub
→ One-click deployment to Agent Engine with playground UI
→ Integration with BigQuery, Vertex AI Search, and other cloud services
→ Detailed architecture docs and use case guides
→ Firebase Studio integration for quick customization

The workflow is simple:
Explore → Deploy → Test → Customize

Every sample comes with detailed architecture docs, use cases, and fully open-source code on GitHub.

Link to Agent Garden in the comments!

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 77k+ stars) | 3x AI Author | Views are my Own

Build RAG with Gemini API without managing infrastructure 🤯

Google just launched File Search Tool, a fully managed RAG system built directly into the Gemini API.

And the Storage and query-time embeddings are 100% FREE. 

No more juggling vector databases, chunking strategies, or retrieval pipelines. File Search handles everything so you can focus on building your application.

Key Features:
• Automatic chunking and embedding generation with state-of-the-art Gemini Embedding model
• Built-in citations that show exactly which parts of your documents were used
• Vector search that understands context and meaning, not just exact keyword matches
• Supports PDF, DOCX, TXT, JSON, and most programming language files
• Simple integration with existing generateContent API

The best part?
It's live and ready to use in the Gemini API.

Link to the docs in the comments.

_____
♻️ Repost to share this with your network. Follow me (Shubham Saboo) for insights and tutorials on AI Agents and RAG.

Google

Ramanand R

I just deployed a production-grade AI agent in just 60 seconds 🤯

One command. 60 seconds. That's it

Using Google's Agent Starter Pack created by Elia Secchi. 

Building AI agents is the easy part.

But what follows is a nightmare:
• Setting up infrastructure  
• Configuring CI/CD pipelines  
• Adding monitoring & observability  
• Making it Secure and production-ready

Weeks of work. Dozens of tools. Endless configuration files.

But with Agent starter pack you instantly get: 
• Fully functional agent (ReAct, RAG, or multi-agent)  
• Backend + Frontend ready to go  
• Complete deployment infrastructure  
• CI/CD pipeline configured  
• Monitoring & observability built-in  

Even better:
Already have an agent? Run `enhance` to add production-ready deployment to your existing project.

We're moving from "Can we build AI agents?" to "How fast can we ship them?"

This isn't about prototypes anymore.

This is about getting AI agents into production while your competitors are still wrestling with Terraform configs.

Try it out: The whole thing is open source. 

Zero setup required in Firebase Studio or Cloud Shell.

🌟 GitHub repo: https://lnkd.in/d8jwyAxq

♻️ Repost to share it with your network. And follow me Shubham Saboo for more such tutorials.

We just released a 50-page guide on Introduction to AI Agents.

This came out of Google's 5-Day AI Agents Intensive on Kaggle, where we wanted to move the conversation beyond agent demos to real, deployable agentic systems.

Most AI agent demos work great until you try to put them in production.

Then reality hits: hallucinations, security risks, unpredictable behavior, and no clear path to scale.

So we documented everything we've learned about building agents that actually work.

The Core Insight 🧠
An agent isn't just an LLM with function calling.
It's a context curator running in a continuous loop:

Think → Act → Observe → Repeat

What you put into that loop determines success or failure.

The 5-Levels of AI Agents 🪜
→ Level 0: Pure reasoning, no tools
→ Level 1: Connected to external tools
→ Level 2: Multi-step strategic planning
→ Level 3: Multi-agent collaboration
→ Level 4: Self-evolving systems

Three core components of AI Agents ☄️
→ Model (The Brain): When to use Pro vs Flash, model routing
→ Tools (The Hands): RAG, code execution, APIs, human-in-the-loop
→ Orchestration (The Nervous System): Memory, planning, reasoning

The Hard Parts We Go Deep On 🤿
→ Security: Agents as a new class of principals
→ Production: From prototype to enterprise-grade systems
→ Self-evolution: How agents learn and improve over time

You're not a "bricklayer" writing explicit code anymore.

You're a "director" who sets the scene, chooses the cast, provides context, and guides performance.

The AI Agent does the rest.

The best part? 
50+ pages. Zero fluff. 100% free.

With expert insights from Antonio Gulli, Alan Blount, Michael Zimmerman and Vladimir Vuskovic 🔥

Link to the free guide in the comments!

Google Sr Dir, Distinguished Eng, CTO Office (AI, Cloud, Search) HAM: HB9IAZ IU5SKA. Angel Investor

PM <- Tech Lead - Web, FP, OS, ML, DA, & moarrr letters

Office of the CTO, Google Cloud

Product Management Director in Cloud AI, co-founder of Google Assistant and, more recently, co-founder of Gemini Enterprise || PhD

Rohit Kamra

Introducing Gemini 3 Pro, the new standard for multimodal understanding.

It's not just incrementally better, it's a different category of model. It beats Claude Sonnet 4.5, GPT-5.1 and Gemini 2.5 on almost every benchmark.

I've been testing it for 3 weeks. My first reaction was "wait, that shouldn't be possible."

From analyzing video streams in real-time to solving graduate-level physics, Gemini 3 Pro represents a generational leap in reasoning and capability.

Here is what makes Gemini 3 Pro a huge deal:

🚀 Best model in the world for multimodal understanding: It doesn't just read text, it natively understands video, audio, and code with unparalleled clarity.

🧠 SOTA at complex reasoning: It dives deeper into ambiguous problems, untangling complex logic that stumps other models.

🛠️ Insanely good at agentic tool use & vibe-coding: A force multiplier for developers. Whether it's one-shot prototyping or navigating complex codebases, its "vibe-coding" capabilities allow you to build faster than ever.

⚡ Great at zero-shot generation: Exceptional instruction following that nails the output on the first try, without the need for endless prompt engineering.

🏆 Tops the charts: #1 on the LMArena Leaderboard (1501 Elo), Humanity’s Last Exam, and GPQA.

💬 More direct and factual: It cuts through the noise - delivering concise, factual answers with significantly reduced hallucinations and sycophancy.

Link to the blog in the comment!

I just released this AI Agent Course for free what others charge $2,000+ for 🤯

A complete crash course on Google's Agent Development Kit with Gemini 3.

No paywall.
No email gate.
No get link in DM

Just drop everything and learn:
•  Agent creation & structured outputs
•  Tool integration (MCP, LangChain, custom functions)
•  Memory systems (in-memory + SQLite persistence)
•  Multi-agent patterns nobody teaches
•  Sequential, parallel, and iterative agentic workflows

9 tutorials. Real code. Real working examples.

Why free?
Because the best things I ever learned came from strangers who asked for nothing in return.

This is me paying that forward.

The repo already has 80,000+ stars.
80,000 developers who said "finally, someone just GAVE us the thing."

Go build something incredible.

🌟 Open Source GitHub Repository: https://lnkd.in/dW6b_dEn

♻️Repost to share it with your network. And follow me Shubham Saboo for more such tutorials.

Ifra Saifi

Entire multimodal AI stack in a single Python library 🤯

Pixeltable is an open-source Python library that replaces your entire multimodal AI infrastructure with a single declarative table interface.

No more juggling databases, vector stores, ETL pipelines, and orchestration tools.

Key Features:
• Multimodal data storage: Images, videos, audio, and documents in one table
• Automatic computation: Define transformations once, they run on all new data
• Built-in vector search: Semantic search without a separate vector DB
• Native AI integrations: OpenAI, Anthropic, Hugging Face, CLIP out of the box
• Time travel queries: Access and query any previous version of your data

Real-world use cases:
• RAG pipelines with document chunking and embeddings
• Object detection across image datasets
• Audio transcription with automatic model management
• Multi-agent workflows with tool calling

Everything persists automatically. All results are versioned. The entire workflow is declarative.

The best part? 
It's 100% Open Source.

Link to the repo in the comments!

_____
♻️ Repost to share this with your network. Follow me (Shubham Saboo) for insights and tutorials on AI Agents and RAG.

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 75k+ stars) | 3x AI Author | Views are my Own

Google just dropped 70 pages on Context Engineering and agent memory 🧠

Day 3 of Kaggle AI Agent Intensive is here.

Most AI agents have zero long-term memory.
They forget everything between sessions.
They treat every conversation like meeting you for the first time.

This isn't just annoying. It's architecturally broken.

What This 70-Page Guide Covers:

1️⃣ Context Engineering (The Foundation):
↳ The difference between prompt engineering vs. context engineering
↳ How to dynamically assemble context
↳ Managing context window limits without losing critical information

2️⃣ Sessions (The Workbench):
↳ What sessions actually are: conversation history + working memory
↳ How different frameworks handle sessions (ADK vs others)
↳ Production considerations: latency, security, data retention
↳ Context compaction strategies: truncation, summarization, pruning

3️⃣ Memory (The Filing Cabinet):
↳ Memory vs. RAG: complementary but fundamentally different
↳ The LLM-driven ETL pipeline: extraction → consolidation → retrieval
↳ Multimodal memory: handling images, audio, and non-textual data

The Critical Insight Most Teams Miss:
Memory isn't just "save the conversation."
It's an active curation system.
All happening asynchronously in the background.

The best part? 
70+ pages. Zero marketing fluff. 100% free.

With expert insights from Antonio Gulli and Kimberly Milam🔥

Link to the free guide in the comments!

Senior Software Engineer at Google

Your AI agent is doing too much work.

And that's why it's slow, expensive, and breaks every time you touch it.

I see this everywhere: developers cramming flight booking, customer support, billing logic, and data analysis into ONE agent with a 5000-token god-prompt.

Then they wonder why it hallucinates, costs a fortune, and takes 3 days to debug.

Here's what actually works:
Stop building one agent that does everything. Build 10 agents that each do one thing.

This is the Coordinator/Dispatcher pattern, and it's how smart engineers build AI systems that scale.

The setup is dead simple:
- ONE coordinator that routes requests
- TEN specialists that execute tasks

Think of it like a hospital. The triage nurse doesn't perform surgery—they send you to the right specialist.

Here's what it looks like:

User: "Book a flight to Paris"

❌ Most teams: Bloated agent with flight logic + hotel logic + weather logic + currency conversion → 5000 tokens → $0.015/call → slow as hell

✅ Smart teams: Coordinator routes to FlightAgent → focused specialist → 1000 tokens → $0.003/call → instant

The math:
Two targeted 1000-token calls cost 60% less than one bloated 5000-token call.
Scale that to 100K requests and you just saved $900/month.
But cost isn't even the point.

The real wins:
• Your BillingAgent breaks? Doesn't touch SupportAgent
• Need to add a feature? Add a specialist, don't rewrite the god-prompt
• Debugging? Check one agent, not 47 if/else branches
• Each agent has ONE job and actually does it well

The tradeoff:
You make more API calls. That's intentional.

Just like microservices vs monoliths - the coordination overhead is worth the operational sanity.

If you've ever written this in your system prompt:

"If the user asks about billing, then... else if they ask about support, then... else if..."

You need this pattern.

The bottom line: Specialists beat generalists.

Your agents should work like a team, not like one exhausted developer wearing 10 hats.

Stop building god-agents. Start building teams.

_____
♻️ Repost to share this with your network. Follow me (Shubham Saboo) for insights and tutorials on AI Agents and RAG.

Google just dropped a 50-page deep dive on MCP and AI Agent tools 🔥

Level 0 → Level 4 agents all need one thing: tools.
Without tools, your agent is just a pattern predictor.

But there's a critical gap most teams ignore:
MCP servers can dynamically change tools without your approval.

Your poetry agent connects to a Books MCP server for quotes. Low risk.
Then overnight, that server adds book purchasing.
Now your poetry agent can initiate financial transactions.
Nobody approved this. Nobody was notified.

This is just one of dozens of security issues hiding in plain sight.

Here's What This Guide Covers: 
1️⃣ You'll understand how tools actually work:
• What makes a tool different from a regular API.
• Why your tool descriptions matter more than your code.
• The types of tools that exist (and which ones you actually need).
• How to design tools that LLMs can actually use reliably.

2️⃣ You'll learn the Model Context Protocol (MCP):
• What MCP is and why it exists (hint: the N×M integration problem).
• The architecture: hosts, clients, servers, and how they communicate.
• The six capabilities MCP defines (but only one that most people support).
• Why MCP might solve interoperability...or create new problems.

3️⃣ You'll see the security risks nobody's talking about:
• Dynamic capability injection (your agent suddenly gains dangerous powers).
• Tool shadowing (malicious tools that impersonate legitimate ones).
• The "confused deputy" problem (when your privileged server gets tricked).
• Why most MCP implementations leak sensitive data without you noticing.
• The mitigations that actually work in production.

The future isn't just "pure" MCP.
It's MCP + governance layers:
• Explicit allowlists  
• API gateway controls
• Scoped credentials
• Policy enforcement
• Real observability

The best part? 
50+ pages. Zero marketing fluff. 

And 100% free.

Link to the free guide in the comments!

AI Product Manager @ Google | Open Source Awesome LLM Apps Repo (#1 GitHub with 78k+ stars) | 3x AI Author | Views are my Own

Watch an AI agent write code, launch browser, and click through your app to test features - completely autonomously 🤯

Google just launched Antigravity, an agentic IDE where AI agents don't just write code - they launch browser, click through your UI, run tests, and verify features actually work. All without you even touching the keyboard.

Key capabilities:
• Multi-surface autonomy: Agents work across editor, terminal, and browser simultaneously. They write code, run tests, and verify their own work.
• Asynchronous development: Spawn multiple agents to work on different tasks in parallel while you focus on what matters. 
• Task-level abstraction: No more watching every tool call. See progress at the task level with implementation plans, walkthroughs, and screenshots.
• Built-in verification: Agents don't just write code - they launch your app and test that features actually work in the browser.
• Model flexibility: Choose between Gemini 3, Claude Sonnet 4.5, or GPT models depending on your task.
• Learning system: Agents remember successful patterns and incorporate your feedback to improve over time.

The best part?
It's free during public preview.

Available for Mac, Linux, and Windows.

Download link in the comments!

Google's Advent of Agents 2025 is now live 🔥

Last month, 1.5 MILLION of you joined the biggest AI Agent course ever on Kaggle. 

Now we are back with another interesting way to learn AI Agents. 

25 days. Zero to Production-Ready AI Agents. 100% free.

Here's what happens EVERY DAY until Christmas:

⚡ One new feature
⏱️ Less than 5 minutes to try
💻 Copy-paste code that actually works
📖 Direct links to docs

No theory. No fluff. Just ship.

What you'll master:
• Gemini 3 (Context engineering, Computer Use, Live API)
• Google ADK (Agent Development Kit)
• Vertex AI Agent Engine
• E2E production templates with Agent starter pack

From quickstart to multi-agent orchestration.
From localhost to production deployment.

The Hub is your permanent archive.
Missed Day 3? Go back.
Need that code from Day 12? It's there.
Building your agent on Christmas Eve? We got you.

👉 https://lnkd.in/dsCF8_Fb

To get started, check out the "Introduction to Agents" white paper, released as part of the 5 Days of Agents with Kaggle.

Read it here: https://lnkd.in/dvuZEDBB

Video intro by Abraham Gomez. A huge thanks to the team building this course for the community Elia Secchi, Alan Blount and many more googlers. 

Day 1 starts now. Let's build AI Agents this December. 

♻️ Repost to share this free and interactive course with your network. And follow this space to stay updated for what's to come in the 25 days.

🗓️ SAVE THE DATE: The November ADK Community Call is next week. 

We are back with our monthly deep dive into the Google ADK. This isn't just a roadmap update; it’s where we break down the technical roadmap, answer your implementation questions, and show you exactly what’s coming next.

This month is packed with major updates to the developer experience (less boilerplate, more building!) and a look at the tools required to take your agents into production.

Here is what you can expect:

🔹 Focus on the Agent, not the Runner: We are stripping away the complexity. We’ll show you how we’ve reduced the boilerplate code needed to test your agents down to a single .run_debug() command.

🔹 Prototype to Production: Best practices for Agent-to-Agent (A2A) protocols and deployment patterns.

🔹 Observability & Evaluation: Building the agent is only half the battle. We are doing a technical deep dive into how to trace execution, run user simulations, and validate your agent's behavior using our latest tooling.

🔹 Context & Plugins: We will also share critical updates on managing state and extending your agent's capabilities with our new plugin architecture.

🔹 Live Demos: See the new .run_debug() method and Time Travel features in action.

🔹 New Agent Resources: A look at the 10 new tutorials from the Kaggle 5-Day Agents Intensive

If you're actively building with or contributing to ADK, get the event calendar to join the technical discussion! 

🗓️ Date: Nov 19th, 2025 (Next Wednesday!)
⏰ Time: 9:30 AM - 10:30 AM PST
🔗 Virtual, links in the comments 

Join us to meet the core team behind ADK and more... Julia Wiesinger, Polong Lin, Alan Blount, Bo Yang, Hangfei Lin, Katie O'Leary, Kristopher Overholt, Ivan 🥁 Nardini 

#OpenSource #AIAgents #ADK #Python

Google just released a 50-page guide on Agent Quality.

This came out of Google's 5-Day AI Agents Intensive on Kaggle, where we wanted to tackle the hardest problem nobody's solving: 

How do you actually know if your agent is working?

Most teams ship agents that pass demos but fail in production.

The agent hallucinates. Makes unsafe decisions. Burns tokens on inefficient loops. And you have no idea why.

So we documented the entire framework for building agents you can trust.

😣 Here's the core problem: 
Traditional QA doesn't work for agents. You can't write a unit test for judgment.

An agent that passes 100 tests can still fail because its failure isn't a bug in code, it's a flaw in reasoning.

You need a new discipline: Process Evaluation.

The Four Pillars of Agent Quality 🏛️
→ Effectiveness: Did it solve the user's actual problem?
→ Efficiency: Did it solve it well, or waste 20 steps?
→ Robustness: Does it handle errors and edge cases gracefully?
→ Safety: Does it operate within ethical boundaries?

You can't measure any of these by only looking at the final answer.

The Three Pillars of Observability 👁️
→ Logs: The agent's diary every decision, timestamped
→ Traces: The narrative thread connecting decisions into a story
→ Metrics: The scorecard for system health vs. decision quality

Observability isn't optional. It's the foundation. You cannot judge a process you cannot see.

The Evaluation Engine 🔍
→ Automated Metrics: Fast, scalable, surface-level
→ LLM-as-a-Judge: Scalable qualitative scoring
→ Agent-as-a-Judge: Evaluating the full reasoning trace
→ Human-in-the-Loop: The gold standard for ground truth

A hybrid approach. Not one or the other, all of them together.

The Agent Quality Flywheel ♻️
Define Quality → Instrument for Visibility → Evaluate the Process → Architect the Feedback Loop

Every production failure becomes a regression test.
Every test makes the system smarter.
The wheel spins faster with each iteration.

You're not building an agent that works once.
You're building an agentic system that learns to work better.

The best part?
50+ pages. Zero fluff. 100% free.

With expert insights from Meltem Subasioglu, Turan Bulmus, and Wafae Bakkali, PhD 🔥 

Link to the free guide in the comments!

NEW RELEASE: Build, Scale and Govern AI agents without the headache 🚢

We just released some major updates to Google Vertex AI Agent Builder that bridges the gap between prototype and production.

Here is what's new:

Build faster
• Control agent context with configurable layers (Static, Turn, User, Cache)
• Self-healing tool plugin that auto-retries failed calls
• Single command deployment with `adk deploy`
• Go language support alongside Python and Java

Scale smarter
• Built-in observability dashboard tracking tokens, latency, and error rates
• Production traces to visualize agent actions step-by-step
• Evaluation layer with User Simulator for testing non-deterministic systems
• Free tier for up to 90 days (no Google Cloud account needed)

Govern with confidence
• Native agent identities with IAM-level access control
• Model Armor for prompt injection protection and response screening
• Security Command Center integration for threat detection

The Agent Development Kit (ADK) has been downloaded 7M+ times and now powers agents across Google products and Gemini Enterprise.

Real impact: Color Health uses Agent Builder to connect women with breast cancer screenings at scale. Early detection has a 99% survival rate.

It's built on Vertex AI Agent Builder. 

Link to the blog in the comments.

Which desk is your AI agent working at right now?

Look at the image below. It explains why your agents fall apart deep in a conversation.

❌ The Left Side (The Trap): A typical agent at Turn 50. It's drowning in 160k tokens of noise, conflicting instructions, and irrelevant history. It's signal drowning.

✅ The Right Side (The Fix): The same agent with Context Engineering. Ruthless relevance. A single source of truth. Summarized history.

Think of your LLM as a brilliant engineer you’ve hired. By turn 50, you haven't just given them a task. You’ve buried their desk under:

→ 50 tabs of stale data. 
→ Three contradictory rule sets. 
→ Mountains of old API logs.

The engineer isn't incompetent. The workspace is impossible.

I see 160k token windows where 70% is pure noise. You're spending a fortune to make a genius wade through garbage.

The difference between failure and success usually isn't a newer model. It’s a cleaner and smarter context.

Stop asking for a miracle in a digital landfill. Focus on signal over noise.

Full blog link in the comment.

Your AI agent just gave away $10K in free products 🤯

It's 3 AM. No one's watching. And you have no idea why.

This is the "last mile" problem killing 8 out of 10 AI agent projects.

Google just dropped a 40-page guide just to talk about that.

Here's the brutal truth:
Building an agent takes 2 weeks.
Trusting it in production takes 6 months.

That gap? It's costing companies millions.

The 3 things you're probably missing:
→ Evaluation gates (no agent ships without passing behavioral tests)
→ Circuit breakers (instant kill switch when things go wrong)
→ Evolution loops (every failure becomes tomorrow's test case)

Real talk:
• A customer service agent tricked into giving products away? Guardrail failure.
• A $50K weekend bill with no explanation? Missing monitoring.
• An agent that worked yesterday but broke today? No continuous evaluation.

These aren't hypothetical. They're happening right now as you read this post.

The solution isn't more AI.
It's better Ops.

Observe → Act → Evolve

Observe the system's behavior in real-time. Act to maintain performance and safety. Evolve the agent based on production learnings. Repeat.

This loop is what separates demos from production systems.

The best part?
Google's Agent Starter Pack gives you production-ready templates with built-in CI/CD, evaluation harness, and observability.

40+ pages. Zero fluff. 100% free.

With expert insights from Dr. Sokratis Kartakis, Gabriela Hernández Larios, Ran Li, Elia Secchi, and Huang Xia 🔥

Link to the full guide in the comments.

♻️ Repost this if someone on your team is shipping agents to production.

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