Multi-agent systems often show managerial problems: agents fail to share information, follow roles mechanically, or drift into unproductive chatting. Today let’s see why good engineering is more important than improvement of prompts.
By shifting from isolated AI pilots to a centralized enterprise framework, businesses can finally automate complex workflows and accelerate decision-making without compromising on governance or LLM independence. Here’s how we solve these scaling challenges with our enterprise-ready solution.
When every department builds its own AI agent with its own data, logic, and tools, organizations can find themselves with a "zoo" of disconnected systems. Instead of scaling, these silos cause the company to slow down. Paul Chayka, Integration and AI Solutions Expert, breaks down how to innovate responsibly by selecting the right initial use cases, and shifting from simple task automation to a coordinated multi-agent ecosystem.
For a Forbes Global 2000 client, we automated the process of matching adverse event descriptions from clinical reports with standardized MedDRA vocabulary — achieving over 95% automation, including terms that specialists previously failed to map manually. Faster and more accurate processing of clinical data reduces R&D costs, accelerates regulatory submissions, and ultimately supports faster delivery of new treatments.
The 2025 Iberian blackout showed how fragile our connected world is. When the power fails, the communication networks we rely on can go silent in a blink of an eye. There’s an ongoing discussion about resilient networks for such cases. LoRa Meshtastic seems like a strong fit for these scenarios.
Vibe coding — the practice of writing code through natural language interactions with AI — has become a hot topic across the corporate tech world. But in practice, it’s meeting a wall of cultural caution, productivity paradoxes, and real-world quality challenges. Here is our look at the current state of adoption, risks, and the emerging best practices for companies bringing AI-assisted coding into their development pipelines.
When people think of AgriTech, they often imagine drones, robotics, or biotech — but another critical frontier is preserving what’s already harvested. Despite digital advances, 10–15% of grain is still lost post-harvest. Using IoT monitoring systems offers a compelling path to reducing these losses — and that’s exactly what CropSave is built to do.
IoT delivers clear, practical benefits when applied to the right processes. From predictive maintenance in factories to patient monitoring in hospitals and smart routing in logistics, real-world examples show how businesses cut costs, improve efficiency and safety, and generate new revenue by applying IoT where it adds the most value.
Custom virtual assistants can work with internal knowledge bases, integrate with corporate systems, execute tasks, and make decisions autonomously. But without proper preparation, these projects often fall short. Where should you start to ensure your AI assistant becomes a valuable tool, not a costly misstep?
Ambient Intelligence (AmI) may sound futuristic, but it’s quietly becoming reality. By embedding AI and IoT into everyday spaces, environments can respond in real time to people’s needs. As Gartner puts it, the ambient experience pushes technology "from in-between to in the background". Take a deeper look at how this shift is happening.
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