Mike Torti

AI agents are generating big hype, but how can you successfully embed them in your enterprise? Join Andra Buica, our Principal AI Solutions Engineer, as she explores the most impactful #AgenticAutomation use cases: http://spr.ly/6041ADPg1

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AI is revolutionizing science by helping researchers decode electrons, create new materials, and even "talk" to trees. See how we're helping science move faster than ever: https://msft.it/6047sBIEx

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The NFL Combine has taken a major leap forward by integrating a real-time AI assistant directly into its talent evaluation process. Now, coaches and scouts can instantly access insights on hundreds of elite athletes—turning hours of manual analysis into seconds. This innovation brings powerful, natural language AI to the field, helping teams ask complex questions and get meaningful answers in the moment. It’s a game-changer for decision-making in high-pressure environments. AI isn’t just transforming business—it’s reshaping sports. #NFL #AI #Innovation #RealTimeInsights #NextGenScouting https://lnkd.in/gXFtU7Ma

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If you’re watching AI move from demos to decisions, keep an eye on multi-agent systems (MAS). Instead of one monolithic model, you orchestrate a team of specialized agents—planner, researcher, optimizer, critic—each doing a focused job and coordinating toward an outcome. Why this matters: many real-world problems are cooperative by nature. Think power grids balancing demand, ports scheduling ships, or cities timing traffic lights. MAS mirrors how these systems actually work: many actors, partial information, dynamic constraints. A simple field guide - Roles: Planner (goal + task graph), Researcher (evidence/tools), Optimizer (constraints/OR solver), Critic (verification/safety), Executor (actions/integrations) - Workflow: Sense → Plan → Negotiate → Act → Verify - Coordination patterns: Blackboard (shared memory), Market/Auction (bidding), Hierarchical (controller + workers) Where this gets practical - Climate & energy: agent “retailers” and “producers” balancing supply/demand in simulations like PowerTAC point to smarter grid strategies (http://www.powertac.org/) - Logistics: port/yard/vehicle agents plan and re-plan around disruptions; pair agents with OR-Tools for routing/scheduling (https://lnkd.in/gCEFSW6r) - Traffic management: decentralized agents adapt signals in simulators such as SUMO (https://lnkd.in/gjvKk4up) - Resilience: multi-agent allocators triage resources across hospitals or shelters under constraints How to start without boiling the ocean 1) Pick a thin-slice outcome (e.g., ETA prediction + re-routing for a single lane or route) 2) Define roles and a minimal contract: what each agent sees, says, and decides 3) Choose orchestration: LangGraph for explicit state machines (https://lnkd.in/gPPGXEt3) or AutoGen for conversational coordination (https://lnkd.in/g-3xnmxT) 4) Add guardrails: tool whitelists, budget/time limits, critics for safety checks 5) Test in simulation first: Mesa for agent-based modeling (https://lnkd.in/gyHE6crp) or PettingZoo for multi-agent RL environments (https://lnkd.in/gUfeKh3W) 6) Observe everything: logs, cost-per-decision, time-to-decision, error taxonomy What’s next - Agent-to-agent protocols will standardize tool use and negotiation - Digital twins become the default sandbox before hitting production - KPIs shift from model accuracy to decision latency and reliability under change Curious where MAS could fit your stack? What’s the smallest proof-of-value you’d try in 30 days? Share your ideas or challenges below—let’s learn from each other. Follow for more such content. #ArtificialIntelligence,#Technology,#Innovation,#Business,#Sustainability,#MultiAgentSystems,#AIforGood,#Logistics, #TrafficManagement

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Great to see Microsoft announcing support for Google's open Agent2Agent (A2A) protocol which enables interoperability between AI agents across various platforms, clouds, and organizations. By adopting open protocols like A2A and MCP, industry can catalyze the growth of the "agentic web," where AI systems communicate and collaborate effectively https://lnkd.in/gagbHv65

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What's the #1 Blind Spot in Most AI Strategies? Everyone is racing to strike gold with AI. 🚀 But in this frantic rush, a critical question is emerging—one that separates the trailblazers from those who get lost in the wild: How do you actually manage it all? It’s no longer just about building the most powerful models. It’s about building trust. It’s about having the visibility, control, and confidence to scale responsibly without losing your way. This is the new frontier, and navigating it is the key to lasting success. Industry experts at IDC have been mapping this very terrain. In their rigorous 2024 MarketScape evaluation for AI Governance Platforms, they analyzed the entire landscape and named Google a Leader. 🏆 Why? It’s about a comprehensive platform that empowers everyone—from data scientists to business leaders—to innovate with integrity and clarity. 💡 Curious to see the blueprint that defines a Leader in AI Governance? Discover the framework for building responsible, scalable AI that fosters trust and drives real-world results. See the full IDC MarketScape report excerpt via the link 🔗 Below: 👇 #AIGovernance #ArtificialIntelligence #MachineLearning #VertexAI #DigitalTransformation #Innovation #TechLeadership #GoogleCloud #GooglePublicSector

Training and serving advanced models pushes the limits of traditional GPU stacks. Read Reuters' coverage on how our next-gen AI chips and software aim to boost performance and efficiency for builders. https://msft.it/6040QjI1n

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Explore the top 10 trends shaping the next wave of AI innovation and data infrastructure in the Confluent 2026 Predictions Report. Get your copy. https://lnkd.in/gwsWw_Tp

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AI can’t be powerful unless it’s reliable. That means deterministic grounding, high‑quality data, and context‑aware interpretation. In his annual observability predictions, Dynatrace CTO Bernd Greifeneder explains how organizations can prevent system‑scale hallucinations and ensure agentic AI behaves predictably — even as complexity grows. Read Bernd’s blog detailing his six predictions for 2026. https://lnkd.in/dFRwz5Gz

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