Multi-Agent Architecture for AI Development in ADK

I found the missing piece for building AI agent teams that actually collaborate! Common Ground is an open-source framework for creating teams of AI agents that tackle complex research and analysis tasks through true collaboration. Think of it as simulating a real consulting team: a Partner agent handles user interaction, a Principal agent breaks down complex problems, and specialized Associate agents execute the work. Key Features: • Advanced multi-agent architecture with Partner-Principal-Associate roles • Full observability with real-time Flow, Kanban, and Timeline views • Model agnostic with built-in Gemini integration via LiteLLM • Extensible tooling through Model Context Protocol (MCP) • Built-in project management and auto-updating RAG system The breakthrough? It transforms you from a passive prompter into an active "pilot in the cockpit" with deep visibility into not just what agents are doing, but why they're doing it. Perfect for building agents that handle multi-step workflows and strategic collaboration beyond simple command-response chains. It's 100% open-source. Link to the repo in the comments! ___ Connect with me → Shubham Saboo I share daily AI tips and opensource tutorials on AI Agents, RAG and MCP.

🎯 The Developer Is Now The Orchestra Conductor Four weeks ago, as I became familiar with Claude Code and adopted it as the coding assistant of choice, I came to realize that its evolution would fundamentally shift my role from hands-on-keyboard pair-programmer to agent manager. Possibly, orchestra conductor. This week, July 25 proved that prediction right—Anthropic's official sub-agents launch just made multi-agent development workflows production-ready … almost overnight. 🔧 What I'm seeing in practice: The DEVELOPER → REVIEWER → VERIFIER → GIT-MANAGER process of development workspace compliance I've been refining is now officially supported. Instead of co-authoring code, I'm designing agent personalities. ⚡ The technical breakthrough: Separate context windows per agent have solved the coordination nightmare. • No more context pollution • No more community workarounds • Just clean, specialized AI teams working in parallel 💡 Here's what most miss: This isn't about replacing developers—it's about elevating the developer who can think like an architect and manage the development process. I spend my time now on: ▶ Architecture decisions ▶ Quality gates ▶ Strategic orchestration Meanwhile, my agent fleet handles implementation details. The cognitive load has shifted from syntax to systems thinking. 📊 Real numbers: Anthropic's own teams process hundreds of code additions in minutes using specialized sub-agents. Their dev teams run autonomous loops—code, test, iterate—with human oversight at commit points. 🎯 The nuanced reality: Human involvement is still critical. Someone needs to design the agent personalities, manage the handoffs, and maintain quality standards. That someone is the developer who understands both code and coordination. We're not coding less; we're architecting more. The future belongs to developers who master agent orchestration, not those clinging to individual contribution. Lest anyone consider this a slight on the incredible, cutting-edge work of Reuven Cohen, let me counter that sustained success delivering production code using frameworks like claude-flow, requires the kind of depth of knowledge, experience and skills he and others like Adrian Cockcroft bring to the party. 🔮 What's next?: Within months, job descriptions will shift from "senior developer" to "senior agent-based development manager." The question isn't whether you can code — it's whether you can think in terms of design patterns and architecture, then incorporate your skills in agent management for high-speed software development. Are you ready to put down the keyboard and pick up the conductor's baton? 🎼 #ArtificialIntelligence #TechLeadership #SoftwareDevelopment #SoftwareDevelopment #MultiAgentSystems

Where does the name “aggregate intellect” come from anyway? 5 years ago when I was incorporating aggregate intellect, I was looking for a fitting name. I didn’t know what my company would do exactly, but I knew that it would have something to do with “collective intelligence” and AI given that it was being born out of the AI community I had built.   Credit should go to my partner for the name because I was stuck finding a name that abbreviates to a.i. AND means “collective intelligence”; and she came up with “aggregate intellect”. It is a mouthful, but I immediately liked it, it had everything I wanted! Now reflecting on the past 5 years, have we gotten closer to the vision I had? Some people recently started talking about “intelligence 3.0” as the super-intelligent machines that surpass human capabilities. That’s a fairly wishy washy topic for me and I dont want to get into it. But a sub-topic of it that is more specifically focusing on the collective intelligence of human-machine systems is quite interesting! That is one of the areas that we are very deeply interested in and keenly focused on. Our research is exploring several interesting ideas in that space and we are ramping up to do some interesting experiments with our academic collaborators at McGill University and University of Toronto. Some of the interesting concepts that exist in the space are: 1. Expert in the loop intelligence: the essential question here is creating AI systems, as point solutions, that collaborate with humans by taking care of the mundane tasks and delegating all the important decisions to the right human expert for robust and successful execution. Delegation often is based on thresholding the confidence of the system in handling a task. 2. Multi-agent LLM systems: the main question here is designing a system with an array of expert models each of which are particularly good at one task, and using LLMs’ ability to communicate via code and data, to coordinate and manage task execution and communication of observations. Most of the existing multi-agent LLM projects are currently focused on primarily LLM based agents and exclude humans in their execution loop.  3. Mixture of experts (MoEs): This is a slightly more demanding approach and it’s rumored to be the architecture of GPT4. MoEs consist of a gating (routing?) network and a range of expert models that are all trained together. Effectively, through training, the network learns how to delegate subtasks to the right expert models, and how to combine the outputs of those for the execution of the primary task. I think the right solution is a combination of all 3 (and potentially other approaches like reinforcement learning). The final product would be a system that includes humans as operators and quality gate keepers, multi-agent LLM systems as workflow handlers where explainability is necessary, and MoEs where most efficient task handling is the priority.

🤖 Instead of talking about AI agents on stage, I stayed home and let them chat to each other and code. 🛠️ We open-sourced a tool we built to let Claude agents work like a real dev team. It’s called CodeByAgents. 🧩 Each agent owns a repo (web, API, worker, admin). You give a high-level task — like “@frontend and @backend you figure out how to add a login flow” — and they: • plan it • write the code • sync through the filesystem • and commit 🧱 No orchestrator. No vector DB. No plugins. Just Claude Code SDK (for now), prompt chains, and local files. ⚙️ It started as a hack. We’ve been using it internally. Now it’s public. 🎬 Demo video included — and yes, the narration is AI too. Didn't re-record with the color fix //shrugg 🧠 Builders are always torn between building and socializing. This week, I chose to build. 🙌 Huge thanks to AI Tinkerers — I’ve learned a lot from this community. Would love to share this live next time, if there’s room. 💸 Anthropic is great. Until you run 4 agents and realize you just spent lunch money on ls. Looking to ad Google Gemini CLI support next, we would love a hand. PRs welcome 🙏 👇 If this resonates, feel free to: ⭐️ star the repo 🧩 contribute 📬 follow my newsletter 👨💻 check out my GitHub for more tools like this github repo: https://lnkd.in/gSkehbTy

🤖 New Guide: AI Agent Orchestration Patterns on the Azure Architecture Center Single AI agents often hit their limits with complex tasks. The future belongs to multi-agent orchestrations that break problems into specialized, collaborative units, that work together in ways that mimic human collaboration techniques. Our new comprehensive guide on Microsoft Learn covers five fundamental orchestration patterns: 🔗 Sequential: Chain agents for multi-stage processes ⚡ Concurrent: Multiple specialists tackle the same problem simultaneously 💬 Group chat: Agents collaborate through structured conversations for decision-making 🤝 Handoff: Intelligent routing where agents delegate to the most appropriate specialist 🎯 Magentic: For open-ended problems where the solution path needs to be discovered Perfect for architects moving beyond monolithic agent architectures. This article is brought to Microsoft Learn by the Azure Patterns & Practices team, with the help of some awesome subject matter experts. Shoutout to: Clayton Siemens, Davide Antelmo, Eric Zhu, Hema Alaganandam, James Lee, Mahdi Setayesh, Mark Taylor, Ritesh Modi, Samantha Brown, Shawn Henry, Tao Chen, and Yaniv Vaknin Read the full guide: https://lnkd.in/gEz8pMMd cc: Hans Yang, Luke Nyswonger, Martin Ekuan #AI #MachineLearning #Azure #SoftwareArchitecture #AgentOrchestration #MicrosoftLearn #SemanticKernel #AzureAIFoundry