How Fieldy accelerated development with Atla integration

Ethan Mollick’s recent article, “Real AI Agents and Real Work,” signals a turning point: AI agents are now completing multi-hour expert tasks that once required human specialists. At XMPro, this is more than theory... it’s what our Multi-Agent Generative Systems (MAGS) have been built to do in the most demanding industrial and mission-critical environments. Our agents continuously monitor thousands of sensors, predict failures, optimize production lines, and keep human experts in control through governed autonomy. Key takeaways from Mollick’s findings... and how MAGS delivers them today: ✅ Real economic value: Agents performing high-impact tasks such as predictive maintenance and process optimization. ✅ Tasks, not jobs: Agents augment specialists rather than replace them, freeing time for strategic decisions. ✅ Industrial-grade architecture: 92% of MAGS intelligence lives in business process governance, not just an LLM wrapper. ✅ Governed autonomy: Rules of Engagement, objective functions, and human-in-the-loop oversight keep operations safe and aligned. The research shows what’s possible. XMPro is proving what’s deployable... reliable, explainable, and built for the complex, safety-critical world of industry. 📄 Read our full analysis of Mollick’s article and how it validates XMPro’s architecture: 

Why do most AI agents feel so... soulless? You automate a workflow. It works. Users tolerate it. But nobody's excited to use it. Tonight at AI Camp Austin, our senior engineer Tyrone Avnit is sharing how to fix this – how to build AI agents that users genuinely enjoy interacting with, not just endure. His talk covers the gap between "it works" and "users love it": ✅ How email-aware agents can use personal context to create personality-driven interactions ✅ Real implementation examples of LLMs that surprise and delight ✅ The right way to safely access user data ✅ Transforming mundane automation into memorable product experiences This isn't theory. Tyrone will walk through actual examples of agents that changed how users think about workflow tools. Perfect for: AI builders, product teams, anyone tired of robotic automation 📍 Capital Factory, Austin ⏰ 6:00pm CT (tech talks start 6:10pm) 💰 Free Save your spot: https://lnkd.in/giSPi_ng

VIPR Solutions is set to transform workplace safety with Agentic AI, software architecture that helps predict incidents before they occur. Read more: https://lnkd.in/gVNarPVN

Did you know that many AI and automation for CX implementations fail? Not because of the tech, but because the client or vendor (or both) don't have the operationalization aspect figured out.

The 7 Ps of Intelligent AI-First Products Designing products that learn, reason, and act with purpose AI-First products aren’t just built with models — they’re designed for intelligence. As AI moves from feature to foundational expectation, the challenge isn’t whether we can add intelligence, but how we make it feel natural, trustworthy, and valuable. As I’ve been deeply thinking about this, I’ve concluded that truly Intelligent AI-First products share seven core traits — the 7 Ps. Together, they form a framework for creating products that sense, reason, remember, adapt, and act responsibly. Read more here... https://lnkd.in/gaFd8w5z #AI #ProductStrategy #AIFirst #IntelligentProducts #DigitalTransformation #Innovation #Architecture #Leadership

There is a great GenAI & Agentic AI use case for Hardware failure detection & resolution where AI can move beyond simple automation to create a truly intelligent, proactive, and self-optimizing system. Below is a conceptual Agentic AI architecture that can help reduce the total proactive failure detection and prevention. Generally GenAI to help explain the outcome of Agentic AI like Anomaly hunter agent to detail out issue and provide quick technical remediation to engineer to improve overall resolution timeline.

You’re Using Multi-Agent Systems Wrong Many people adopt multi-agent systems just to parallelize tasks or automate simple processes. The problem is that without a strategy, you miss the real potential of this technology. Multi-agents are not just “multiple models working together.” The true power lies in collaborative planning, agent communication, and distributed decision-making. Without these, you end up with AIs working in isolation, producing inconsistent results and underutilizing resources. To unlock real value, you need to design the agent ecosystem intelligently, defining clear roles, shared objectives, and interaction protocols. Only then does AI stop being just a tool and become a strategic system of collective intelligence. It’s time to rethink how you apply multi-agent systems in your company or project.

AI agents are changing how we think about system design, but they also introduce new complexity around reliability, transparency and control. Nonfunctional requirements, things like safety, explainability, and auditability matter more than ever in this next wave of AI-driven systems. I had the chance to contribute some commentary to this excellent piece on how to define those standards thoughtfully and practically here: https://lnkd.in/eyatArZW

Designing Smarter Agents: Right-Sizing Models and Tool Granularity Most “AI agents” fail for a simple reason: we throw the biggest model at every tiny task and wire up 100 tiny tools “just in case.” That’s how you get slow, expensive, unreliable systems. What worked better for us: Match model to task Reduce tool granularity Make deterministic steps... deterministic Think beyond chat Build locally, iterate fast Test for reliability Simple rule: Smarter agents aren’t “more AI.” They’re better architecture. Follow Ram Rallabandi for more insights! ✌️🔝

Transforming Supply Chains with Autonomous AI Agents: The Future of Resilience and Agility: Ensure ongoing data quality checks to create a baseline for reliable AI insights. 2. Leverage multi-agent collaboration architectures. No single agent ...