Multi-Agent Architecture for AI Development in ADK

This week, I had a great time building agents on Google Agent Development Kit (ADK) at Google Cloud’s Agentic AI Live + Lab. I have been using ADK since its launch earlier this year. Some of my observations on the current version of Google's ADK for agent builders:- Google ADK is No Longer Just a Kit! It's a Blueprint for Production-Ready AI Teams. As an Agent Builder, the last six months of Google ADK evolution have fundamentally shifted what's possible. It's time to stop building isolated ‘super-agents’ and start orchestrating intelligent systems. Here are the 5 game-changers for builders:- 1. The Rise of Multi-Agent Systems (MAS):- ADK now makes building hierarchical ‘Agent Teams’, with specialized agents delegating tasks via Workflow Agents (Sequential/Parallel/Loop) the standard. This means better reliability and modularity than ever before. 2. Native Production Readiness:- With the stable Python ADK v1.0.0 and robust built-in features for IAM, Audit Logging, and Data Governance, the path from a local prototype to an enterprise-grade, secure deployment is drastically shortened. 3. Debugging Via X-Ray Vision:- The new Trace View in the Developer UI provides an X-ray of the agent's full execution path and reasoning. Finally, we can debug multi-step workflows with visual clarity, not just print statements! 4. Agent-as-a-Tool:- Modularity just got an upgrade. You can now wrap an entire specialized agent and use it as a callable utility (AgentTool) inside another agent. This is the key to building truly scalable and reusable agent components. 5. Standardized Interoperability:- Updates to the Agent2Agent (A2A) Protocol and richer tooling support (including OpenAPI integration and compatibility with LangChain/CrewAI) mean your ADK agents can collaborate seamlessly with external systems and frameworks. Check it out - https://lnkd.in/dxYyYHcP #GoogleADK #AIAgents #AgentBuilder #GenAI #VertexAI

Everyone's talking about AI agents, but few understand why building them at scale was a nightmare until now. Here's the problem no one talks about: Gen 1 was monolithic agents. One agent trying to do everything. It worked... until it didn't. Give it 10+ tasks and it starts hallucinating, losing context, and making decisions that don't make sense. Gen 2 was manual multi-agent. Developers writing custom code to make FlightAgent talk to HotelAgent, WeatherAgent coordinate with RootAgent. Every single agent interaction needed its own integration. Pure chaos at scale. Google saw this mess and introduced ADK (Agent Development Kit). Here's what ADK actually stands for: 1️⃣ A = Agent Specialized AI workers for specific tasks. FlightAgent handles flights. HotelAgent handles hotels. Each agent has its own instructions, tools, and capabilities. No confusion. 2️⃣ D = Development The framework for building these agents. Code-first approach in Python, Java, or Go. Workflow agents (Sequential, Parallel, Loop). Parent-child hierarchies. Pre-built + custom tool integration. 3️⃣ K = Kit Everything you need in one package. Deployment to Vertex AI, Cloud Run, or local. Session management for memory across conversations. Replaces scattered libraries with one standardized system. Think of ADK like Lego blocks for AI agent teams. Traditional Multi-Agent Approach: • Custom glue code for every interaction • No standardized communication • Debugging is a nightmare • Scaling means rewriting everything ADK Approach: • Built-in agent orchestration • Standard patterns for agent coordination • Deploy anywhere with containerization • Scale without rebuilding What makes ADK different from other frameworks: It's the same toolkit that powers agents inside Google's Agentspace and Customer Engagement Suite. Now it's open-source. Model-agnostic: Works with Gemini, but also supports GPT-5, Claude, Mistral through LiteLLM integration. Deployment-agnostic: Run locally, deploy to Vertex AI Agent Engine, use Cloud Run, or your own Docker/GKE infrastructure. This is how agent development starts feeling like actual software development: structured, modular, testable. Over to you: Are you building single agents or ready to experiment with multi-agent systems?

Everyone is talking about AI agents, but very few people actually break down the technical architecture that makes them work. To make sense of it, I put together the 7-layer technical architecture of agentic AI systems. Think of it as a stack where each layer builds on top of the other, from the raw infrastructure all the way to the applications we interact with. 1. Infrastructure and Execution Environment This is the foundation. It includes APIs, GPUs, TPUs, orchestration engines like Airflow or Prefect, monitoring tools like Prometheus, and cloud storage systems such as S3 or GCS. Without this base, nothing else runs. 2. Agent Communication and Networking Once you have infrastructure, agents need to talk to each other and to the environment. This layer covers frameworks for multi-agent systems, memory management (short-term and long-term), communication protocols, embedding stores like Pinecone, and action APIs. 3. Protocol and Interoperability This is where standardization comes in. Protocols like Agent-to-Agent (A2A), Model Context Protocol (MCP), Agent Negotiation Protocol (ANP), and open gateways allow different agents and tools to interact in a consistent way. Without this layer, you end up with isolated systems that cannot coordinate. 4. Tool Orchestration and Enrichment Agents are powerful because they can use tools. This layer enables retrieval-augmented generation, vector databases such as Chroma or FAISS, function calling through LangChain or OpenAI tools, web browsing modules, and plugin frameworks. It is what allows agents to enrich their reasoning with external knowledge and execution capabilities. 5. Cognitive Processing and Reasoning This is the brain of the system. Agents need planning engines, decision-making modules, error handling, self-improvement loops, guardrails, and ethical AI mechanisms. Without reasoning, an agent is just a connector of inputs and outputs. 6. Memory Architecture and Context Modeling Intelligent behavior requires memory. This layer includes short-term and long-term memory, identity and preference modules, emotional context, behavioral modeling, and goal trackers. Memory is what allows agents to adapt and become more effective over time. 7. Intelligent Agent Application Finally, this is where it all comes together. Applications include personal assistants, content creation tools, e-commerce agents, workflow automation, research assistants, and compliance agents. These are the systems that people and businesses actually interact with, built on top of the layers below. When you put these seven layers together, you can see agentic AI not as a single tool but as an entire ecosystem. Each layer is necessary, and skipping one often leads to fragile or incomplete solutions. ---- ✅ I post real stories and lessons from data and AI. Follow me and join the newsletter at www.theravitshow.com

Multi-agent AI is quickly moving from experimentation to real system architecture. And that transition requires something many teams are just starting to appreciate: Design patterns. Check out this new book, which tackles this head-on: 📘 Agentic Architectural Patterns for Building Multi-Agent Systems by Ali Arsanjani, PhD and Juan Pablo Bustos (Foreword by Thomas Kurian, Google Cloud CEO) The timing could not be better. As enterprises move beyond GenAI prototypes, the real challenge becomes turning LLM-powered ideas into scalable, production-ready systems. What stands out about this book is its focus on architecture and engineering discipline, not just experimentation. It explores how to design intelligent agent systems using proven patterns across: 🔹RAG pipelines 🔹LLMOps and AgentOps 🔹fine-tuning and in-context learning 🔹multi-agent orchestration One particularly interesting concept is the hierarchical multi-agent architecture, where orchestrator agents coordinate specialized agents to manage complex workflows. This model aligns closely with how large-scale distributed systems have evolved over the past decades. The book also dives into practical engineering considerations that many teams struggle with: 🔸handling instruction drift and coordination challenges 🔸designing systems using the three layers of the agentic stack: – function calling – tool protocols (MCP) – agent-to-agent collaboration (A2A) 🔸implementing observability with lifecycle callbacks for traceability and debugging 🔸building fault-tolerant AI systems that can operate in enterprise environments It also references frameworks developers are already exploring today, including: • ADK (Agent Development Kit) • LangGraph • CrewAI This shift is important. The next wave of AI systems will not be single prompts or even single models. They will be coordinated networks of agents, operating with clear orchestration, governance, and observability. And that means we need to start thinking like AI systems architects, not just prompt engineers. As Thomas Kurian puts it in the foreword, this evolution demands engineering discipline alongside vision. Curious how others here are approaching this. Are you already experimenting with hierarchical multi-agent architectures, or are most teams still building single-agent workflows? #AgenticAI #MultiAgentSystems #LLMOps #AgentOps #RAG #AIArchitecture

🚀 Are you facing challenges moving your AI Agents from POC to Production? You might want to explore ADK Over the past few months, I’ve been exploring how AI agents are moving from isolated demos to real, production-grade systems. What’s making that possible is the emergence of a new foundational layer, the Agent Development Kit (ADK), supported by the Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication. The easiest way to understand this is, like how SDKs helped developers build software applications, ADK does something similar for AI, but instead of building apps, you’re building agents that can act, collaborate, and reason across systems. And the real benefit comes when you combine ADK with: 👉 MCP, that ensures that agents retain and share context consistently, even as they move between models or tasks. 👉 A2A, that allows these agents to talk to each other, passing insights, delegating work, and completing workflows together. I believe that this combination of ADK + MCP + A2A is quietly redefining how multi-agent systems are designed. More importantly, it’s helping teams move proof-of-concepts to production faster and at lower cost, because the orchestration, context management, and communication layers are already built in. Let us take a simple example, hiring automation for recruitment, to explain this. With ADK, you can design specialized agents for each stage: resume screening, interview scheduling, feedback collection, and offer generation. ◾ ADK provides the structure to build and orchestrate these agents. ◾ MCP ensures every agent operates with shared context such as a candidate’s details, interview notes, and communication history. ◾ A2A enables these agents to coordinate, for example, the screening agent handing shortlisted candidates to the scheduling agent automatically. What you get is not another chatbot but a coordinated, context-aware system that handles repetitive work autonomously, freeing recruiters to focus on decisions that matter. And because this setup sits on a well-defined architecture, teams can transition from POC to production with far less engineering overhead and significantly lower cost than traditional one-off integrations. I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

Google just released a full-stack toolkit for building AI agents.. and it’s a big deal. 🚀 Until now, building production-grade agents has felt like duct-taping together libraries: One for logic, another for tools, and almost nothing for evaluation or deployment. That changes with Google’s new open-source Agent Development Kit (ADK), an end-to-end operating system for building, testing, and shipping intelligent agents. Here’s why this release stands out: 🔧 Code-first, developer-focused Built for serious devs who need version control, custom logic, and robust testing. 🤖 Multi-agent, by design Easily spin up systems where agents collaborate or specialize across tasks—right out of the box. 🧪 Goes beyond building Most frameworks stop at the prototype. ADK includes tools for evaluating performance and deploying workflows into production. 🧩 Flexible orchestration Define custom flows using built-in agents, or wire up your own with dynamic routing logic. 💻 Great local dev experience CLI + Web UI make it easy to build, test, and debug your agents locally—before pushing to prod. Bonus: It’s cloud-friendly (of course it works well with Google Cloud), but supports any third-party models and tools, so you’re not locked in. To get started: pip install google-adk GitHub repo is linked in the comments👇

You don't have an AI agent problem. You have an architecture decision problem. Most founders think picking an AI agent framework is like picking a database - just choose the most popular one and figure it out later. That's how you end up with a brilliant demo that fails every security audit. After helping 50+ teams move AI agents from prototype to production, here's what actually works: The Architecture Decision Tree: Your Primary Constraint Determines Your Architecture: SECURITY first → Orchestrated or Hierarchical SPEED TO MARKET → Tool-Using or Event-Driven COMPLIANCE first → Memory-Augmented with governance AUTONOMY first → Goal-Driven with guardrails Then Match to Your Scale: Small Team (<10): Tool-Using or Event-Driven Mid-Size (10-50): Orchestrated or Multi-Agent Enterprise (50+): Hierarchical or MCP-Based The 10 Major Architectures - What You Need to Know: High Security Risk (needs guardrails): ↳ Goal-Driven/Autonomous (AutoGPT) - Research and exploration ↳ Swarm Intelligence (CrewAI Swarm) - Collaborative but unpredictable ↳ Memory-Augmented (LangGraph) - Personalization with data governance Medium Security Risk (manageable): ↳ Event-Driven (Zapier AI) - Workflow automation ↳ Hierarchical (AutoGen) - Complex projects with clear delegation ↳ Tool-Using (ChatGPT Tools) - Practical business apps ↳ Planning-Based (ReAct) - Quality-focused workflows ↳ Multi-Agent (CrewAI) - Specialized team coordination Low Security Risk (enterprise-ready): ↳ Orchestrated Systems (LangChain) - Centralized control for regulated industries ↳ MCP-Based (LlamaIndex MCP) - Future-proof interoperability What Actually Matters: The architecture you choose today determines your security posture, compliance overhead, and scaling costs for the next 2-3 years. Most teams choose based on demos. Smart teams choose based on their constraints. The Real Question: Not "which architecture is best?" but "which architecture serves my specific use case, security requirements, and team capabilities?" The visual below (credit to Prem) shows these 10 styles at a glance. Use it as a starting point for the architecture conversation your team needs to have. What's your take? Which architecture are you building with, and what drove that decision? P.S. If you're vibe-coding agents right now without thinking about architecture - you're probably defaulting to Goal-Driven or Tool-Using. That's fine for prototypes. But the transition to production requires intentional architectural choices, not accidental ones.

Stop building single AI agents. Start building AI Agent teams. Google ADK's multi-agent patterns let you orchestrate specialized agents like a well-coordinated team. Single agents hit limits fast. They try to do everything and end up doing nothing well. Solution? Delegate tasks to multiple specialized agents that work together. 3 Multi-agent pattern you can build with Google ADK with opensource step-by-step code tutorials: 1. Sequential Agents: ↳ Execute agents one after another in strict order ↳ Perfect for business analysis pipelines ↳ Each step builds on the previous one Code tutorial 👉 https://lnkd.in/dySsFiNi 2. Loop Agents: ↳ Iterate until conditions are met ↳ Ideal for refinement and revision tasks ↳ Shared state across iterations Code tutorial 👉 https://lnkd.in/dxwa48NN 3. Parallel Agents: ↳ Run multiple agents concurrently ↳ Dramatically speeds up independent tasks ↳ Each agent writes to shared state safely Code tutorial 👉 https://lnkd.in/drn5yTHt The best part? It's 100% Open Source. Link to the GitHub repo with full Google ADK Crash Course in comments!

🚨 Breaking : Google just open-sourced its own AI Agent framework It's the same one powering Google's own AI products ! It's called ADK (Agent Development Kit) A code-first framework for building, deploying, and orchestrating AI agents That lets you build agents the way you build software With real code, real tests, real CI/CD The core philosophy : → Code-first development : Python, Java, TypeScript, Go → Event-driven runtime, not request-response → Version control, testing, CI/CD all work natively → It's the same framework powering Google's own products (Agentspace, Customer Engagement Suite) The agent primitives are clean : → LlmAgent for reasoning → SequentialAgent, ParallelAgent, LoopAgent for deterministic pipelines → AgentTool : use agents as tools inside other agents → LLM-driven routing for dynamic task delegation → A2A protocol for remote agent-to-agent communication across services The tool ecosystem is where it gets ridiculous : → Google Search, Code Execution built-in → Full MCP support (Google Maps, BigQuery MCP servers) → Point it at any OpenAPI spec → auto-generates tools → Direct LangChain, LlamaIndex, CrewAI, LangGraph integration → Human-in-the-loop confirmation before tool execution → Long-running async tools with background execution And it streams bidirectional audio and video out of the box 📌 Quick setup : 1. Install: pip install google-adk 2. Create a project: adk create my_agent 3. Add your API key to the .env file: GOOGLE_API_KEY=your_key_here 4. Run it: adk web (launches a local web UI) or adk run my_agent (CLI) 🔗 Links : The full docs : google.github.io/adk-docs Repo : https://lnkd.in/e3VeKWDS

🚀 𝗚𝗼𝗼𝗴𝗹𝗲 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗔𝗗𝗞: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗞𝗶𝘁 – 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 𝗳𝗼𝗿 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀! At Google Cloud Next 2025, Google unveiled ADK (Agent Development Kit)—an open-source framework purpose-built for developers creating production-grade multi-agent applications. This is not just another SDK—this is the framework behind Google’s own Agentspace and Customer Engagement Suite (CES), now available for everyone. 🔍 Why should you care? If you're building complex, intelligent systems with multiple LLMs or agents that must collaborate, delegate, and reason, ADK provides the full-stack tools to do it—all the way from development to deployment. 🧠 Core Pillars of ADK: 🔹 Multi-Agent by Design: Compose modular, hierarchical agents that delegate intelligently. 🔹 Model Flexibility: Use Gemini, Vertex AI Model Garden models, or any model via LiteLLM (Anthropic, Mistral, Meta, etc.). 🔹 Tooling Power: Use built-in tools (search, code execution), third-party tools (LangChain, LlamaIndex), or even other agents (LangGraph, CrewAI). 🔹 Multimodal Streaming: Built-in audio/video streaming for conversational agents. Just a few lines of code. 🔹 Rich Developer UX: Web UI, CLI, Python APIs. Visual step-by-step execution. 🔹 Built-in Evaluation: Define test cases and verify agent performance before deploying. 🔹 Easy Deployment: Containerize or deploy on Vertex AI’s managed runtime. 🧪 Example Use Case: A Weather Agent that Delegates With just a few lines of Python, you can build: ☞ A WeatherAgent that answers weather queries using a custom tool. ☞ A GreetingAgent and FarewellAgent that auto-handle greetings and goodbyes. ☞ All agents work together hierarchically, thanks to ADK’s intelligent delegation and orchestration. Here’s a peek: root_agent = Agent( name="weather_agent", model="gemini-2.0-flash-exp", tools=[get_weather], sub_agents=[greeting_agent, farewell_agent], description="Provide weather info, delegate greetings/farewells.", ) 🌐 Optimized for Google Cloud, Works Everywhere 📌 Deep integration with Vertex AI, Gemini, BigQuery, Apigee, and over 100+ connectors. 📌 Build once, deploy anywhere. 💡 Final Thoughts: Google’s ADK marks a pivotal shift from LLM-centric applications to multi-agent systems. This is ideal for those of us building modular, scalable, and intelligent AI workflows across diverse domains—from customer support to security monitoring and beyond. 🎯 If you’re serious about building agentic AI applications, now is the time to explore ADK. 📖 Dive in: Official ADK Docs - https://lnkd.in/gUVUBafE #AI #MultiAgent #LLM #GenAI #VertexAI #GoogleCloudNext #AgentDevelopmentKit #AutonomousAgents #OpenSource #Gemini #LangChain #MLOps #AIFramework #CTOInsights