Seth Daluga

AMD and Oracle Cloud (OCI) to accelerate drug discovery with Absci, a clinical-stage biotech firm advancing novel therapeutics design with Gen AI. Learn more: https://lnkd.in/g5_Ytjj4 #TogetherWeAdvance_life

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The Foundation of Enterprise Native AI Transformation Enterprise Data Platform Readiness is not just a prerequisite. It is the foundation that determines how far and how fast an organization can move with Native AI. When data lives in silos, AI stays trapped in pilots. But when platforms are unified, governed, and scalable, they unlock the full potential of intelligent and autonomous operations. This isn’t about upgrading a data warehouse. It is about designing a system that feeds AI continuously, learns from every interaction, and adapts across the enterprise as conditions change. https://lnkd.in/eUekn_pC #dataplatforms #vectordb #pinecone #milvus #chroma #weaviate #Qdrant #faiss #opensearch

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AI is transforming sales, but not replacing salespeople. The real competitive edge? Combining AI insights with human trust, credibility, and leadership, especially in complex enterprise deals. #AI #Sales https://lnkd.in/gRUTpJv5

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Misaligned priorities between CIOs and I&O leaders can stall progress: https://gtnr.it/4lU26iN Build trust, clarify strategy ownership, and focus on future-ready infrastructure, digital transformation and cloud optimization to deliver business value. Learn how to stay in lockstep 🔒 Gartner for IT | #GartnerIT #Infrastructure #BusinessAlignment #CloudOptimization

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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 visual! Seemingly simple steps with the right guidance. If you can take the first big step with AI Assistants, the next phase (Simple AI Agents) will impact your team noticeably and by then you're half way to fully optimized systems and an early competitive advantage.

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