Collaborative AI Development Protocols

𝗔𝟮𝗔 (𝗔𝗴𝗲𝗻𝘁-𝘁𝗼-𝗔𝗴𝗲𝗻𝘁) 𝗮𝗻𝗱 𝗠𝗖𝗣 (𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹) are two emerging protocols designed to facilitate advanced AI agent systems, but they serve distinct roles and are often used together in modern agentic architectures. 𝗛𝗼𝘄 𝗧𝗵𝗲𝘆 𝗪𝗼𝗿𝗸 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿 Rather than being competitors, 𝗔𝟮𝗔 𝗮𝗻𝗱 𝗠𝗖𝗣 𝗮𝗿𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝗿𝘆 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹𝘀 that address different layers of the agent ecosystem: • 𝗔𝟮𝗔 is about agents collaborating, delegating tasks, and sharing results across a distributed network. For example, an orchestrating agent might delegate subtasks to specialized agents (analytics, HR, finance) via A2A25. • 𝗠𝗖𝗣 is about giving an agent (often an LLM) structured access to external tools and data. Within an agent, MCP is used to invoke functions, fetch documents, or perform computations as needed.    𝗧𝘆𝗽𝗶𝗰𝗮𝗹 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: • A user submits a complex request. • The orchestrating agent uses 𝗔𝟮𝗔 to delegate subtasks to other agents. • One of those agents uses 𝗠𝗖𝗣 internally to access tools or data. • Results are returned via A2A, enabling end-to-end collaboration25.    𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁 𝗦𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 • 𝗔𝟮𝗔 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Multi-agent collaboration and orchestration   Handling complex, multi-domain workflows   Allowing independent scaling and updating of agents   Supporting long-running, asynchronous tasks54 • 𝗠𝗖𝗣 𝗲𝘅𝗰𝗲𝗹𝘀 𝗮𝘁:   Structured tool and data integration for LLMs   Standardizing access to diverse resources   Transparent, auditable execution steps   Single-agent scenarios needing a precise tool    𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗔𝗻𝗮𝗹𝗼𝗴𝘆 • 𝗠𝗖𝗣 is like a 𝘶𝘯𝘪𝘷𝘦𝘳𝘴𝘢𝘭 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘰𝘳 (USB-C port) between an agent and its tools/data. • 𝗔𝟮𝗔 is like a 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘤𝘢𝘣𝘭𝘦 connecting multiple agents, enabling them to form a collaborative team.    𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 • 𝗔𝟮𝗔 introduces many endpoints and requires robust authentication and authorization (OAuth2.0, API keys). • 𝗠𝗖𝗣 needs careful sandboxing of tool calls to prevent prompt injection or tool poisoning. Both are built with enterprise security in mind.    𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 • 𝗔𝟮𝗔: Google, Salesforce, SAP, LangChain, Atlassian, Cohere, and others are building A2A-enabled agents. • 𝗠𝗖𝗣: Anthropic (Claude Desktop), Zed, Cursor AI, and tool-based LLM UIs.   Modern agentic systems often combine both: 𝗔𝟮𝗔 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, 𝗠𝗖𝗣 𝗳𝗼𝗿 𝗶𝗻𝘁𝗿𝗮-𝗮𝗴𝗲𝗻𝘁 𝘁𝗼𝗼𝗹 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻. This layered approach supports scalable, composable, and secure AI applications.

If you are building AI agents or learning about them, then you should keep these best practices in mind 👇 Building agentic systems isn’t just about chaining prompts anymore, it’s about designing robust, interpretable, and production-grade systems that interact with tools, humans, and other agents in complex environments. Here are 10 essential design principles you need to know: ➡️ Modular Architectures Separate planning, reasoning, perception, and actuation. This makes your agents more interpretable and easier to debug. Think planner-executor separation in LangGraph or CogAgent-style designs. ➡️ Tool-Use APIs via MCP or Open Function Calling Adopt the Model Context Protocol (MCP) or OpenAI’s Function Calling to interface safely with external tools. These standard interfaces provide strong typing, parameter validation, and consistent execution behavior. ➡️ Long-Term & Working Memory Memory is non-optional for non-trivial agents. Use hybrid memory stacks, vector search tools like MemGPT or Marqo for retrieval, combined with structured memory systems like LlamaIndex agents for factual consistency. ➡️ Reflection & Self-Critique Loops Implement agent self-evaluation using ReAct, Reflexion, or emerging techniques like Voyager-style curriculum refinement. Reflection improves reasoning and helps correct hallucinated chains of thought. ➡️ Planning with Hierarchies Use hierarchical planning: a high-level planner for task decomposition and a low-level executor to interact with tools. This improves reusability and modularity, especially in multi-step or multi-modal workflows. ➡️ Multi-Agent Collaboration Use protocols like AutoGen, A2A, or ChatDev to support agent-to-agent negotiation, subtask allocation, and cooperative planning. This is foundational for open-ended workflows and enterprise-scale orchestration. ➡️ Simulation + Eval Harnesses Always test in simulation. Use benchmarks like ToolBench, SWE-agent, or AgentBoard to validate agent performance before production. This minimizes surprises and surfaces regressions early. ➡️ Safety & Alignment Layers Don’t ship agents without guardrails. Use tools like Llama Guard v4, Prompt Shield, and role-based access controls. Add structured rate-limiting to prevent overuse or sensitive tool invocation. ➡️ Cost-Aware Agent Execution Implement token budgeting, step count tracking, and execution metrics. Especially in multi-agent settings, costs can grow exponentially if unbounded. ➡️ Human-in-the-Loop Orchestration Always have an escalation path. Add override triggers, fallback LLMs, or route to human-in-the-loop for edge cases and critical decision points. This protects quality and trust. PS: If you are interested to learn more about AI Agents and MCP, join the hands-on workshop, I am hosting on 31st May: https://lnkd.in/dWyiN89z If you found this insightful, share this with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI insights and educational content.

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.

🚀 Why Model Context Protocol (MCP) could change the way we build AI Agents When I was delivering a session on Multi Agent AI Ecosystem at Huddle, an event organized by Kerala Startup Mission last year, a question came up —"How can we build AI agents that not only connect but also work together ?". A few days later, in another session with a NASSCOM group of fellow AI enthusiasts, the same debate resurfaced. In both the forums, we all acknowledged the difficulty and agreed that the protocols we had - like Knowledge Query and Manipulation Language (KQML) and Foundation for Intelligent Physical Agents (FIPA)—helped, but they had their limitations. 👉 This is why Model Context Protocol (MCP) is getting so much attention now. Building an AI agent ecosystem today is like running a company where different teams—marketing, engineering, and finance—each work in silos. They all have valuable data, but without a shared project management system, things get duplicated, key insights get lost, and efficiency drops. Now imagine this analogy with AI models. Each large language model (LLM) has its own way of processing and storing context. They don’t naturally share information or build on each other’s knowledge. This makes multi-agent collaboration difficult. This reminds me of how the internet worked before Transmission Control Protocol/Internet Protocol (TCP/IP). Back then, different networks couldn’t talk to each other efficiently. TCP/IP changed that by creating a standard protocol, making seamless communication possible. MCP is doing something similar for AI agents. What does MCP solve? 🔹 Context persistence – AI agents won’t forget past interactions, making them more useful over time. 🔹 Efficient Multi-Agent workflows – Agents can divide work intelligently instead of repeating efforts. 🔹 Standardized communication – Different AI models can work together without compatibility issues. 👉 How is MCP different from other protocols? We did have AI communication protocols before—KQML, FIPA, RESTful APIs, and Simple Public Key Infrastructure (SPKI/SDSI)— that were designed for specific communication needs. But these don’t handle shared memory or deep agent collaboration like MCP does. MCP is built for LLM-based AI agents, ensuring they can store, retrieve, and build on context dynamically—just like how humans remember and build upon past experiences in a conversation. Just like TCP/IP enabled the internet, I strongly believe that MCP can unlock a new era of autonomous AI ecosystems. Instead of isolated models generating responses independently, we’ll have AI agents that work together, share knowledge, and continuously learn from one another. The needle has moved beyond "smart AI" to --> "AI that truly collaborates". I write about #artificialintelligence | #technology | #startups | #mentoring | #leadership | #financialindependence   PS: All views are personal Vignesh Kumar

🚀 Agentic AI is Accelerating—Are You Ready? #AI agents are now doubling their capabilities every 7 months. To keep pace, we need robust #standards that ensure these agents can communicate and collaborate effectively. In my latest article, I delve into how Anthropic's Model Context Protocol (#MCP) and Google's Agent-to-Agent (#A2A) protocol are revolutionizing agentic AI development. These protocols are not just technical specifications; they're the building blocks for a future where AI agents work seamlessly across diverse systems. 🔍 What you'll discover: * How MCP standardizes AI's interaction with #data, #tools, and #resources. * The role of A2A in facilitating secure and efficient inter-agent communication. * How these protocols complement each other to create a cohesive AI #ecosystem. * Emerging #standards and #frameworks shaping the future of agentic AI. If you're involved in AI development, product #strategy, or #innovation, this read is essential. #AgenticAI #GenAI #MCP #A2A #AIStandards #AITrends #AIAgents #ArtificialIntelligence #Innovation #TechLeadership

Everyone's arguing A2A vs MCP they're missing the point entirely... Most teams think they need to pick one protocol for their AI agents. That's not how this works. Reality 1: A2A handles agent collaboration. Think conference room where agents negotiate and coordinate complex workflows → not just single tasks. Reality 2: MCP connects agents to tools. Your agent needs database access? API calls? → That's MCP's workshop model in action. Reality 3: Enterprise security isn't equal. A2A ships with OAuth-level authentication built-in. MCP → needs additional configuration for secure remote access. The real difference: A2A (Google's Agent-to-Agent): → Agents operate independently, share selectively → Long-running, complex workflows → Built-in enterprise authentication → Discovery through "Agent Cards" MCP (Model Context Protocol): → Client-server architecture → Precise tool/resource access → Structured JSON schemas → Single-shot functions excel here Smart teams aren't choosing—they're combining. A2A orchestrates your agent swarm → MCP gives them tools to actually work. The truth: You need both protocols to build production-grade AI agents. One without the other → like having either steering or wheels. Choose both → Ship faster.

The latest boost to Anthropic's Model Context Protocol (MCP) is today's news that OpenAI will support it. This Andreessen Horowitz MCP map shows the nascent MCP market landscape now 4 months since its launch. The map comes from a new A16Z post "A Deep Dive Into MCP and the Future of AI Tooling" (link in comments). A few of the most interesting points: AI agents are gaining autonomy through MCP chaining. MCP enables AI agents to select and chain tools based on context, allowing complex workflows without custom code for each system. This shifts tool integration from static APIs to dynamic, scenario-driven interactions shaped by the agent’s goals. MCP is enabling IDEs to become multi-tool hubs. Tools like Cursor turn into “everything apps” by supporting multiple MCP servers: developers can check databases, generate images, or debug live environments—all from their IDE. This local-first, developer-centric use case currently dominates MCP adoption. New creative workflows ar emerge for non-devs. MCP clients like Claude Desktop and tools like Blender now allow non-technical users to create 3D models using natural language. These net-new use cases signal a future of AI-powered creativity beyond code-heavy environments. Developers can skip boilerplates with doc-to-tool conversion. Developers can auto-generate MCP servers directly from documentation or APIs, reducing integration time and boosting accessibility. This means agents can instantly use tools without manual setup, streamlining development workflows. A marketplace layer is forming. Ecosystems like Mintlify’s mcpt, Smithery, and OpenTools are emerging as the “npm for MCP,” enabling discovery and sharing of servers. These platforms are crucial to scaling tool accessibility and making AI workflows more dynamic. The protocol lacks key infrastructure. MCP currently lacks built-in authentication, authorization, and multi-tenant support, limiting its use in enterprise and remote settings. Developers must roll their own security models, which slows broader adoption and complicates scaling. A new competition model for APIs is emerging. If MCP agents dynamically select tools, API providers must optimize for discoverability, speed, and cost—outpacing traditional adoption metrics. Documentation and tool quality will determine which APIs agents choose in real-time. This fundamental new layer in agentic AI is creating a whole new ecosystem and market which will rapidly evolve in coming months. This is just a snapshot.

HTTP Unlocked the Web. Agent Protocols Will Unlock the Next Age of AI. 🚀 Hey everyone! 👋 Remember when the internet was a collection of isolated servers? It wasn't until protocols like TCP/IP and HTTP standardized the rules of communication that the web truly exploded in value. We're on the verge of a similar transformation with AI. In 2025, LLM agents are rapidly moving beyond research demos. They're making their way into real products:- "𝙨𝙪𝙢𝙢𝙖𝙧𝙞𝙯𝙞𝙣𝙜 𝙡𝙚𝙜𝙖𝙡 𝙙𝙤𝙘𝙨, 𝙖𝙪𝙩𝙤𝙢𝙖𝙩𝙞𝙣𝙜 𝙘𝙪𝙨𝙩𝙤𝙢𝙚𝙧 𝙨𝙪𝙥𝙥𝙤𝙧𝙩, 𝙜𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙣𝙜 𝙋𝙍𝘿𝙨, 𝙖𝙣𝙙 𝙚𝙫𝙚𝙣 𝙤𝙧𝙘𝙝𝙚𝙨𝙩𝙧𝙖𝙩𝙞𝙣𝙜 𝙤𝙩𝙝𝙚𝙧 𝙩𝙤𝙤𝙡𝙨" on your behalf. But here's the catch: 𝐭𝐡𝐞𝐲 𝐨𝐟𝐭𝐞𝐧 𝐨𝐩𝐞𝐫𝐚𝐭𝐞 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬. Every major AI vendor is building its own agent stack: 🤖 OpenAI has its Assistant API and Code Interpreter. 🧡 Anthropic is pushing MCP (Model Context Protocol) for tool access. 🕹️ Google is piloting A2A for agent-to-agent interaction. 🎙️ Countless startups are launching custom wrapper agents with proprietary APIs. We’re repeating the same pattern we saw with the early internet – fragmented, brittle systems that struggle to "𝐭𝐚𝐥𝐤" to each other. The true power of AI agents, much like the internet, lies in their ability to collaborate and interoperate. Imagine an Agent Internet. We’re now seeing early protocols aiming to build this foundation: 👉 MCP (Model Context Protocol) by Anthropic: Enables agents to call tools with rich, structured context. Think of it as an external memory interface, crucial for grounded reasoning. 👉 A2A (Agent-to-Agent) by Google: Defines how agents collaborate, pass tasks, and negotiate – a building block for multi-agent workflows. 👉 And others like #ANP, #ACP, #Agora & #LMOS are exploring coordination, standardized messaging, and even decentralized agent marketplaces. This is incredibly exciting! But it also highlights a crucial need: 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝗯𝗿𝗶𝗱𝗴𝗲 𝘁𝗵𝗲𝘀𝗲 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 𝗮𝗻𝗱 𝗺𝗮𝗸𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 That's where tools like motia come into the picture. As I demoed in my recent video about "𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭" (check out the video attached below!), Motia's event-driven architecture is inherently designed for this kind of modularity and interoperability. ☆You can write steps in TypeScript, Python, Ruby, etc. ★Each step is an independent worker, communicating via events. ⭐︎You can easily plug in different AI models (OpenAI, Gemini, local models) or external tools. 🔗 https://lnkd.in/e_YSHHic This approach is a step towards building agents that aren't locked into a single vendor's ecosystem, preparing us for the "𝗔𝗴𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗻𝗲𝘁." #AI #GenAI #Agents #MCP #A2A #Internet #LLM

The MCP Revolution: Why This Boring Protocol May Change Everything About AI-- "MCP is a standardized way for AI systems to talk to each other—and to your data. Instead of every AI provider using their own proprietary connection methods (forcing developers to build custom integrations for each), MCP creates a universal language that any AI can use to access, query, and interact with business tools, repositories, and software. Why You Should Care --  Three reasons: 1. Unified Connections = Faster Development Before MCP, if you wanted your AI assistant to connect to Salesforce, then Slack, then your custom database, you needed three different integration methods. Each one required specialized knowledge, unique error handling, and separate maintenance. With MCP, connect once, connect everywhere. Development time just got slashed by 70%. 2. Standardized Data Exchange = Better Systems Not only can systems connect more easily, but they all speak the same language when exchanging information. The practical upshot? AI systems that are more reliable, more interoperable, and less likely to break when you need them most. 3. Unified Context Model = Smarter AI The real magic happens with context. MCPs standardize how conversation history and user preferences are maintained across interactions. No more AI assistants that forget what you just told them when they switch tools. This isn’t just convenient—it’s the difference between an AI that feels broken and one that feels intelligent. What This Means For Your Business -- If you’re working on AI agents and agentic systems, MCP’s emergence as a standard has several immediate implications: For the enterprise: You can build AI systems without fear of vendor lock-in. If ChatGPT doesn’t suit your needs next year, you can swap in Claude or any MCP-compatible model without rebuilding your architecture. For developers: Learn one protocol, connect to everything. The MCP ecosystem will expand rapidly now that the big players are on board. For startups: The barrier to entry just dropped significantly. You can build specialized services that plug into any MCP-compatible system without asking users to adopt another proprietary platform. What To Do About It Now-- If you’re considering AI agents, take these steps immediately: Ask vendors about MCP support. If your AI tools aren’t built to be MCP-compatible, ask why. If the answer isn’t strategic, it’s probably technical debt. Design for modularity. Prioritize tools and platforms that separate agents from services. That flexibility will pay off when you want to scale or switch vendors. Plan for distributed systems. MCP assumes multiple servers. If your IT team isn’t thinking in terms of distributed orchestration, it’s time to level up. Train your teams. MCP isn’t just for engineers. Product owners, architects, and technical marketers all need to understand what this unlocks—and what it demands." ~@shellypalmer

𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝘀𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝗺𝗶𝗹𝗹𝗶𝗼𝗻𝘀 𝗼𝗳 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝘁𝗮𝗹𝗸 𝘁𝗼 𝗲𝗮𝗰𝗵 𝗼𝘁𝗵𝗲𝗿 — 𝘀𝗲𝗰𝘂𝗿𝗲𝗹𝘆, 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝘆 𝗮𝗻𝗱 𝘃𝗲𝗻𝗱𝗼𝗿-𝗻𝗲𝘂𝘁𝗿𝗮𝗹? Multi-agent networks and agent-to-agent communication are set to become some of the most important topics in AI over the next few years. A2A - the Agent-to-Agent Protocol from Google, launched yesterday, could be an important building block for the future. It could be the missing layer that finally makes multi-agent AI work at scale. It’s open-source by default and already backed by 50+ players — including Salesforce, LangChain, and SAP. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔2𝗔? A2A is an open standard that lets AI agents: - communicate   - coordinate   - and complete tasks together — across orgs, tech stacks and frameworks. So it works basically like: → One agent sends a task   → Another agent completes it   → No brittle integrations. No vendor lock-in. No proprietary walls. 𝗛𝗼𝘄𝗲𝘃𝗲𝗿, 𝘁𝗵𝗲𝗿𝗲’𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — 𝗔2𝗔 ≠ 𝗠𝗖𝗣. A2A is not a replacement for MCP. It complements it. MCP (Model Context Protocol) connects agents to tools, APIs and enterprise systems. A2A connects agents to each other across organizational and technical boundaries Think of it like this: → A2A = agents talking to agents → MCP = agents accessing tools and resources Both are designed to work together as part of a broader, interoperable agent architecture. (see diagram below.) --- Multi-agent networks and agent-to-agent communication will become increasingly important topics in AI over the next few years. And the Google release is a strong signal that we now have an open standard designed specifically for this. Most agent-based systems today are constrained by brittle integrations and closed ecosystems. A2A introduces a shared language for agents to collaborate — in a way that can actually scale. But for couse, it’s early and only time will tell if it becomes the standard. But for now, this is a meaningful step forward. Here’s the full announcement and more info: https://lnkd.in/dkCxu-kb