How AI is revolutionizing federal mission execution with multi-agent systems.

Another BIG update from the Gemini API: File Search Tool is now live! What this means: You can now upload and query documents directly — allowing Gemini to find, understand, and extract relevant info from files instantly. This bridges the gap between chat and structured document search, making data retrieval smarter and faster. Takeaways: 🔍 Instantly search and reference uploaded files. 📂 Supports multiple formats (PDFs, Docs, etc.). 🤖 Enables contextual answers from your own documents. ⚙️ Perfect for developers building AI assistants, knowledge systems, or enterprise search tools. #GeminiAPI #GoogleAI #AIDevelopment #FileSearch #GenAI #AIUpdate #Developers #TechInnovation #AIIntegration #ArtificialIntelligence #PromptEngineering #AIAutomation #DataRetrieval #LLMTools #AIFeatures #APIDevelopment #TechNews #MachineLearning #AIForBusiness #GoogleGemini

🚀 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗔𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿 𝟮 𝗺𝗶𝗻𝘂𝘁𝗲𝘀  Your AI tools deserve a happy ending. 𝘌𝘷𝘦𝘳 𝘧𝘦𝘭𝘵 𝘭𝘪𝘬𝘦 𝘺𝘰𝘶𝘳 𝘓𝘓𝘔𝘴 𝘢𝘯𝘥 𝘙𝘈𝘎 𝘥𝘢𝘵𝘢 𝘴𝘰𝘶𝘳𝘤𝘦𝘴 𝘢𝘳𝘦 𝘨𝘩𝘰𝘴𝘵𝘪𝘯𝘨 𝘦𝘢𝘤𝘩 𝘰𝘵𝘩𝘦𝘳? Different APIs, endless auth flows, and more “𝘸𝘩𝘺 𝘸𝘰𝘯’𝘵 𝘵𝘩𝘪𝘴 𝘤𝘰𝘯𝘯𝘦𝘤𝘵?!” moments than you can count? 😩 𝗠𝗲𝗲𝘁 𝗦𝘁𝗼𝗿𝗺 𝗠𝗖𝗣 — your enterprise-grade gateway that makes Large Language Models and AI tools actually talk (and cooperate). 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝘀𝘁𝗼𝗿𝗺-𝗹𝗲𝘃𝗲𝗹 𝗰𝗼𝗼𝗹 🌩️ ⚡ 𝗢𝗻𝗲 𝗚𝗮𝘁𝗲𝘄𝗮𝘆, 𝗭𝗲𝗿𝗼 𝗖𝗵𝗮𝗼𝘀: Connect Claude Desktop, Cline, or Claude Code — instantly. 🧩 𝟭𝟬𝟬+ 𝗩𝗲𝗿𝗶𝗳𝗶𝗲𝗱 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿𝘀: GitHub, Slack, Brave Search, Notion, Airtable & more. 🔒 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: OAuth2, API keys, encrypted creds — all built-in. 👀 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Watch every request, response, and “oops” in action. 🧠 𝗟𝗟𝗠 + 𝗥𝗔𝗚 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: Context sharing for smarter, faster AI output. 𝗜𝘁’𝘀 𝗹𝗶𝗸𝗲 𝗭𝗮𝗽𝗶𝗲𝗿 𝗳𝗼𝗿 𝗔𝗜 — 𝗯𝘂𝘁 𝘄𝗶𝘁𝗵 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿𝘀. 𝘍𝘳𝘰𝘮 “𝘩𝘦𝘭𝘭𝘰 𝘸𝘰𝘳𝘭𝘥” 𝘵𝘰 𝘦𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦-𝘳𝘦𝘢𝘥𝘺 𝘪𝘯 𝘮𝘪𝘯𝘶𝘵𝘦𝘴. 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗔𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆 𝗶𝗻 𝘂𝗻𝗱𝗲𝗿 𝟮 𝗺𝗶𝗻𝘂𝘁𝗲𝘀: 🔗 https://tryit.cc/ykA81J Your AI workflow will thank you later. 😊 #StormMCP #AIIntegration #RAG #LLM #AIGateway #AIDevelopment #EnterpriseAI #Anthropic #MCP #Developers #AIAutomation #AItools #TechInnovation #SecureAI #FutureOfWork #APISimplified #Productivity #ZapierForAI #StormPlatform

Exploring the GitHub Repository: The Context Company's Observatory https://lnkd.in/gqavF3bn Transform Your Development Experience with The Context Company 🌟 At The Context Company, we're revolutionizing agent observability with a passion for Developer Experience (DX) at our core. Our open-source Local Mode empowers developers to run our tool seamlessly—no accounts or API keys required! Key Features: Local-First Approach: Easily integrate the framework in your projects. Simple Setup: Just a few steps to get started: Install dependencies Add instrumentation to Next.js 🚀 Enable telemetry for enhanced AI SDK calls Privacy-Centric: We collect only limited, anonymous usage data—no sensitive info! Explore our comprehensive documentation for detailed guidance and elevate your AI applications. 🔗 Interested in bettering your development experience? Share your insights and let’s discuss how we can help propel your projects forward! #ArtificialIntelligence #DeveloperExperience #OpenSource Source link https://lnkd.in/gqavF3bn

AI tools have transformed how developers code — but how do we measure that transformation? 🤔 That’s the problem GitKraken Insights aims to solve. The new platform helps teams understand how AI impacts developer productivity, code quality, and workflow efficiency — all while respecting developer privacy. It combines DORA metrics, AI impact tracking, and developer feedback to reveal where AI truly adds value — and where friction still exists. For open-source maintainers and contributors, this could mean smarter data on how automation, Copilot, and AI-based reviews influence contribution velocity and technical debt. Transparency without surveillance. Context without bias. Insights that elevate developer experience for everyone involved. #OpenSource #GitHubInsights #GitKraken #DeveloperExperience #AIMetrics #SoftwareEngineering

🚀 The future of #observability is MCP tools! 🤖 With the proliferation of #AI agents and the opening of the ecosystem to new integrations, the fundamentals of #monitoring, understanding, and optimizing software are transforming. Intelligent agents are becoming an integral part of the software development lifecycle (SDLC), analyzing, diagnosing, and improving systems autonomously in real time. 🔗 Continue reading the full article at the following link: https://lnkd.in/gzG8mPgj

🚨 Critical AI Development Warning: Do NOT Build Production Systems on "Gemini 3.0 Pro Preview" A new model checkpoint, labeled "Gemini 3.0 Pro Preview," has been circulating in limited, unofficial rollouts on aggregators and in some developer environments like Vertex AI. This is a strategic, "gray-scale" preview—it is not a General Availability (GA) or public release, and there is no official documentation from Google. A critical warning for all developers and automation agencies: DO NOT build any client-facing or production-grade systems on this unofficial endpoint. Workflows built on preview models are entirely unsupported and can break at any moment, without warning, leading to system failure and client issues. For stability and reliability, you must continue to use the officially supported and documented Gemini 2.x models for all production builds. Distinguish between experimental endpoints and stable, supported releases. Building on unofficial models is a high-risk decision that can compromise your client work and reputation. Stick to the official API until a formal GA release is announced. #Gemini3 #Gemini3Pro #AIEthics #AITechnology #ProductionReadyAI #GenAI #LLMs #AIStrategy #DeveloperWarning #VertexAI #GoogleAI #ModelStability

AI agents are moving from demos to production. But how do you actually build agentic systems that work at scale? At QCon AI New York (Dec 16-17), practitioners share real-world lessons from building and operationalizing AI agents in enterprise environments: from MCP implementation to multi-agent platforms to production-scale automation. Five sessions exploring the full spectrum of agentic AI: 🔹 Jake Mannix, Technical Fellow @Walmart Global Tech — Real Experience Building Agentic Systems 🔹 Karthik Ramgopal & Prince Valluri @LinkedIn — Platform Teams Enabling AI: MCP/Multi-Agentic Tools Across LinkedIn 🔹 Julie Qiu, Uber Tech Lead @Google Cloud SDK — AI Tooling with MCP Servers 🔹 Bonnie Xu, Software Engineer @OpenAI — AI Agents to Make Sense of Data at OpenAI 🔹 Tracy Bannon, Software Architect and Researcher @The MITRE Corporation — Agents, Architecture, & Amnesia: Becoming AI-Native Without Losing Our Minds (Keynote) These aren't just theoretical explorations. They're lessons from teams already running agents in production: dealing with context engineering, authentication, model governance, and the shift from AI as a tool to AI as a collaborator. 🚨 Early bird pricing ends November 11. 🔗 See the full QCon AI schedule: https://bit.ly/4qAgv6r #QConAI #AIAgents #ProductionAI #EnterpriseAI

🚀 𝗗𝗮𝘆 𝟭: 𝗙𝗿𝗼𝗺 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗔𝗰𝘁𝗶𝗼𝗻 Today, I stopped 𝘫𝘶𝘴𝘵 𝘱𝘳𝘰𝘮𝘱𝘵𝘪𝘯𝘨 — and 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 that can 𝗽𝗹𝗮𝗻, 𝗿𝗲𝗮𝘀𝗼𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁. 🤖💡 In the 𝗚𝗼𝗼𝗴𝗹𝗲 × 𝗞𝗮𝗴𝗴𝗹𝗲 𝟱-𝗗𝗮𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗜𝗻𝘁𝗲𝗻𝘀𝗶𝘃𝗲 𝗖𝗼𝘂𝗿𝘀𝗲, we kicked off with the foundations of what makes a system truly 𝘢𝘨𝘦𝘯𝘵𝘪𝘤. Here’s what I learned 👇 🧠 𝗔𝗴𝗲𝗻𝘁𝘀 = 𝗕𝗿𝗮𝗶𝗻 (𝗠𝗼𝗱𝗲𝗹) + 𝗛𝗮𝗻𝗱𝘀 (𝗧𝗼𝗼𝗹𝘀) + 𝗡𝗲𝗿𝘃𝗼𝘂𝘀 𝗦𝘆𝘀𝘁𝗲𝗺 (𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻) 🔁 The 𝟱-𝘀𝘁𝗲𝗽 𝗹𝗼𝗼𝗽: 𝘔𝘪𝘴𝘴𝘪𝘰𝘯 → 𝘚𝘤𝘢𝘯 → 𝘗𝘭𝘢𝘯 → 𝘈𝘤𝘵 → 𝘖𝘣𝘴𝘦𝘳𝘷𝘦 🧩 Why 𝗥𝗔𝗚 + 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗖𝗮𝗹𝗹𝗶𝗻𝗴 makes responses grounded & actionable 🤝 How 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 (Coordinator + Specialists) scale complex tasks 🛡️ And 𝘄𝗵𝘆 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 + 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 = “𝗔𝗴𝗲𝗻𝘁 𝗢𝗽𝘀” 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 for reliable systems Mini-win: I can now design an agent that reads data, plans next steps, calls APIs, and even asks for my approval before acting. ✅ For anyone who wants to explore the code and notes from today’s session — 📂 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗺𝘆 𝗗𝗮𝘆 𝟭 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀 𝗵𝗲𝗿𝗲: 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼 → https://lnkd.in/gMfcziDj I’ll be documenting this 5-day journey — from concepts to real-world applications — to demystify how agents think, decide, and deliver. #AIAgents #GenerativeAI #GoogleAI #Kaggle #LLMs #RAG #MLOps #AgentOps #AICommunity #LearningJourney #ArtificialIntelligence #OpenSource

Yesterday, I gave an AI agent a complex debugging task and walked away. Three hours later, it had completed 11 deployment iterations without asking me a single question. It tested the code. Identified encryption failures. Researched solutions. Modified the implementation. Redeployed. Found new issues. Adapted its approach. Persisted until everything worked. This wasn't a chatbot answering questions. This was an agent doing work. After 18 months of hands-on experimentation with AI agents—building coding agents, research agents, and production SaaS applications, I've learned something profound: 𝐓𝐡𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐚 𝐜𝐡𝐚𝐭𝐛𝐨𝐭 𝐚𝐧𝐝 𝐚𝐧 𝐚𝐠𝐞𝐧𝐭 𝐢𝐬𝐧'𝐭 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞. 𝐈𝐭'𝐬 𝐚𝐮𝐭𝐨𝐧𝐨𝐦𝐲. A chatbot waits for your questions and provides answers. An agent takes your goal and autonomously works toward achieving it—iterating through failures, learning from mistakes, and persisting until the job is done. 𝐈 𝐜𝐚𝐥𝐥 𝐭𝐡𝐢𝐬 𝐭𝐡𝐞 "𝐀𝐮𝐭𝐨𝐥𝐨𝐨𝐩" 𝐚𝐧𝐝 𝐢𝐭'𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐡𝐨𝐰 𝐰𝐞 𝐛𝐮𝐢𝐥𝐝 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞. I𝐧 𝐦𝐲 𝐥𝐚𝐭𝐞𝐬𝐭 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐫𝐭𝐢𝐜𝐥𝐞 (conducted under the COTRUGLI Business School initiative), 𝐈 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧: ✅ How agents evolved from research labs to production systems ✅ What industry leaders (OpenAI, Anthropic, Google, Microsoft) actually mean by "AI agent" ✅ The 5 levels of agent maturity (and why Level 3-4 is sufficient to revolutionize work) ✅ 18 months of practical lessons from building agents across domains ✅ Why November 2024's Model Context Protocol changed everything The infrastructure is here. The tools exist. What remains is learning to orchestrate them. Will you learn to orchestrate agents, or be orchestrated by those who do? Read the complete research: https://lnkd.in/dx2e_RpT #AIAgents #ArtificialIntelligence #Automation #FutureOfWork #Research #COTRUGLI

Just spent the weekend at The AI Alliance's Developer Workshop, and it was easily one of the most hands-on AI learning experiences I've had. Over two days, we went from building basic RAG applications to creating agent-to-agent systems. What I appreciated most was that this wasn't just another talk-heavy conference—we actually built things. Day 1 started with GraphRAG, where we scraped real website data and built agents that could reason about structured relationships. Then we moved into Model Context Protocol (MCP), learning how to give agents secure access to enterprise data sources. We worked with tools like AllyCat, Milvus, Neo4j, and Llama LLMs—getting our hands dirty with the actual implementation details. Day 2 got into advanced orchestration frameworks and agent-to-agent communication. The most interesting part was exploring how agents might transact with each other through marketplaces, and what governance patterns we need to make these systems trustworthy and accountable. The practical focus made all the difference. Instead of just hearing about these concepts, I left with working prototypes and a much clearer understanding of where the technical challenges actually are. Big thanks to The AI Alliance and TechEquity for keeping this accessible and building a genuine developer community in the Bay Area. If you're working on agentic systems or exploring MCP implementations, I'd love to hear what challenges you're running into. #AIAgents #MachineLearning #DeveloperCommunity #AIAlliance #MCP #GraphRAG #BayAreaTech