Learn Multi-Agent Systems with Google Cloud's 3-Part Guide

We've all seen magical AI demos, but the real challenge is bridging the gap between a demo and a production service that's fast, reliable, and cost-effective. I built Bodhi AI to solve exactly that. I've attached a short demo of the app in action, but here's the engineering story behind it: 🧠 Sub-Second AI Responses. Cold starts kill real-time AI. I engineered a zero cold-start architecture using Docker on Google Cloud Run with provisioned instances. This deliberate trade-off ensures the system is always warm, hitting our p99 latency goal of <500ms from the very first user interaction. ⚡ Efficient, Scalable Inference. I designed a stateful "single agent" model that acts as a pre-warmed inference engine. By dynamically injecting context into this agent per session, the system can scale to thousands of unique topics while keeping resource usage flat, avoiding the costly latency of loading new models. 📉 Cost Control via Semantic Caching. To control API costs and boost performance, I architected a Redis cache for AI-generated personas. This strategy achieved a >70% cache hit ratio for popular topics, drastically reducing redundant API calls and lowering the overall cost-to-serve. This is what I love about engineering: building resilient, observable systems that transform a powerful AI model into a product people can actually rely on. You can try it out for yourself at https://bodhi-ai.com/. What are the biggest unforeseen challenges you've faced when taking an AI-powered feature into production? #AI #SRE #SystemDesign #GoogleCloud #GCP #Docker #GenAI #SoftwareEngineer #Google

Blueprint #59 for any ITDM and developer looking to use AI while managing strict data residency requirements Blueprint: An instance of Google Distributed Cloud (GDC) is deployed within the local country's data center. ➝ A user makes a call that requires real-time translation. ➝ The audio stream is processed entirely within the GDC environment. ➝ Speech-to-Text and Text-to-Speech services, along with Vertex AI translation models running on GDC, handle the translation. ➝ The translated audio is sent back to the user with super-low latency, delivering the AI service while guaranteeing all data remains in-country to comply with sovereignty regulations. CC Brian Kracik Rohan Grover Angelo Libertucci Muninder Singh Sambi

Day 2 of KodeKloud Free Week Connect AI to your documentation, policies, and runbooks. Build a searchable knowledge system that knows YOUR infrastructure. Lab Preview: Create an AI that can answer questions about your specific AWS compliance requirements and internal procedures. 📦 What Gets Installed:ChromaDB: Vector database for storing embeddings Sentence Transformers: For creating text embeddings OpenAI: LLM integration Tiktoken: Token counting for smart chunking ✅ Rank-BM25: Keyword search for hybrid retrieval Run the RAG system test to see how well it answers AWS compliance queries. The target accuracy is 90%. #rag #labs

🚀 Thrilled to share that I had the opportunity to attend the Google Cloud Technical Series – AI Agents Edition on 7th August 2025! This event went beyond a simple introduction — it was a deep dive into how AI Agents are reshaping the way businesses and engineers build intelligent, cloud-first solutions. Some of my biggest takeaways included: 🔹 Understanding the architecture of AI Agents and how they integrate with Google Cloud services to automate workflows, decision-making, and customer interactions. 🔹 Exploring real-world use cases where enterprises are deploying AI Agents to improve efficiency, reduce costs, and drive innovation at scale. 🔹 Learning about tools and frameworks within Google Cloud that accelerate AI development, from data ingestion and orchestration to secure deployment. 🔹 Discussions around responsible AI practices and governance, ensuring that AI systems remain transparent, reliable, and trustworthy. I’m grateful to Google Cloud for creating a platform where professionals can learn, ask questions, and connect over the next generation of cloud-powered AI. For me, this is not just about gaining a badge or certificate, but about continuously expanding my skills in data engineering, AI/ML, and cloud technologies — areas that are driving the future of tech. Looking forward to putting these learnings into practice and contributing to building smarter, scalable systems in my work. #GoogleCloud #AI #AIagents #CloudComputing #MachineLearning #ContinuousLearning

🚀 AWS Launches AgentCore to Lead the Agentic AI Era At the AWS Summit in New York, Amazon unveiled Amazon Bedrock AgentCore—a powerful platform for building secure, enterprise-scale autonomous AI agents. Features include: 🧠 Secure web access 🧠 Memory management 🧠 Contextual reasoning 🧠 Model Context Protocol (MCP) 🧠 Agent-to-Agent (A2A) interactions Backed by a US $100M investment and a new AI agents marketplace, AgentCore positions AWS as a central hub for trusted agentic AI development. At Transform LogiQ, we’re helping organisations explore how agentic AI can drive real-world efficiencies. #TransformLogiq #AWSAgentCore #AgenticAI #AILeadership #EnterpriseAI #DigitalTransformation #aws #aidevelopment #digitaltransformation

Semantic Kernel, Agent Core, Vertex AI, Crew AI, Langraph... the Agentic AI landscape in 2025 feels like the wild west. 🤠 If you're trying to figure out how to build with agents or wondering what else is out there, my latest article is for you: The 2025 Agentic AI Maze: A Developer’s Guide to Choosing the Right Framework This isn't just a list of features. I cut through the hype to compare the big cloud platforms against the open-source heroes, so you can solve real production headaches such as: The Big Clouds: How Microsoft, AWS, and Google are solving enterprise security and scaling (and the vendor lock-in you accept). Open-Source Powerhouses: The flexibility of LangGraph, CrewAI, and AutoGen for rapid prototyping (and the production chaos you inherit). The Gold Standard: Why a hybrid approach is emerging as the winning strategy for enterprise-grade agents. Plus, there's a quick-reference cheat sheet at the end to solidify your choice. Stop spinning your wheels and start building smarter. Read the full developer's guide here 🔗 in the comments. What's the biggest production headache you've faced with AI agents so far? #AgenticAI #Frameworks #LLM #GenerativeAI #AIdevelopment #LangChain #SemanticKernel #Databricks #MLOps

🤖 Next-Level FinOps Chat is Here CloudBalance.ai included AI features from the start—summarizing and prioritizing optimization opportunities, tracking usage and rate savings, and even generating rightsizing service tickets. Now, we’re taking it to the next level with agentic AI chat. 🔹 Brings together CloudBalance rightsizing, savings, and commitment data with comprehensive cost date from the AWS Cost Explorer MCP server 🔹 Uses agent-based retrieval to generate insights from both sources 🔹 Answers your AWS cost optimization questions directly in chat This isn’t just a chatbot—it’s an interactive FinOps assistant that helps you cut costs, plan commitments, and understand savings scenarios in plain language. 🚀 We’re currently testing with select customers and will roll this out more broadly in the coming weeks. 👉 What would you ask your CloudBalance AI cost optimization assistant? #AWS #FinOps #CloudOptimization #AgenticAI #CloudBalance

I’ve been excited about AI in FinOps from day one with CloudBalance.ai—using it to summarize savings opportunities, prioritize actions, and even auto-generate rightsizing tickets. Now we’re taking a big step forward: agentic AI chat for cloud cost optimization. Instead of just showing dashboards, you can ask questions in plain language—and our AI agent will retrieve data from CloudBalance and the AWS Cost Explorer MCP server to give you answers you can act on. It’s like having a FinOps co-pilot: ⚡ Rightsizing + Idle Resource Cleanup ⚡ Savings Plans & RI coverage and performance ⚡ Real-time cost and savings insights across accounts We’re testing with select customers now. Full release is just a few weeks away. 👉 If you could ask an AI FinOps assistant anything about your AWS costs, what would it be? #FinOps #AWS #CloudOptimization #AgenticAI #CloudBalance

🚀 Google just dropped EmbeddingGemma - a game-changer for on-device AI! This 308M parameter multilingual embedding model runs entirely offline on phones and laptops while supporting 100+ languages. It's the highest-ranking open text embedding model under 500M parameters on MTEB benchmarks. The model uses just 200MB of memory when quantized, enabling powerful RAG pipelines and semantic search without sending data to servers. This breakthrough makes private, intelligent AI accessible to everyone - no cloud dependency, no privacy concerns, just pure on-device intelligence. What on-device AI applications are you most excited about? 🤔 #AI #EmbeddingModels #PrivateAI