Generative AI

Beyond APIs – How Agent-to-Agent Communication Protocols Enable Autonomous Business Networks

In-Short

CaveatWisdom

Caveat: In my previous post, we solved internal communication—your Purchase Agent talks to enterprise systems through MCP. But here’s the next challenge: your agent needs to negotiate with Supplier Agents from completely different companies. Traditional API integrations create tight coupling—every new supplier requires custom integration work, manual orchestration for multi-party negotiations, and brittle connections that break with every change. You can’t build autonomous business networks with custom APIs for each supplier.

Enterprise Procurement Reimagined – How Agentic AI with MCP Tools Transforms B2B Negotiations

In-Short

CaveatWisdom

Caveat: Your AI procurement agent needs real-time data from Logistics, Production, Stores, Finance, and Operations to make informed decisions. But here’s the trap: each enterprise system speaks a different language with proprietary APIs. Without standardized tool protocols, you end up with brittle agents that have hard-coded integrations. Every API change breaks your agent. Every new system means weeks of custom development. Your agent makes suboptimal decisions because it can’t access complete enterprise data.

Domain Driven Design in Vector Databases

In-Short

CaveatWisdom

Caveat: We’ve all been swept up by the magic of Generative AI. Add a Retrieval-Augmented Generation (RAG) pipeline with Vector Databases on top, and your AI system suddenly sounds smarter—context-aware, grounded, and business-specific. But here’s the trap: Just bolting on a vector database doesn’t mean your AI is contextually intelligent. In fact, if your RAG database is a chaotic soup of embeddings from every business unit imaginable, you may have just created a slightly faster way to serve wrong answers—with more confidence.

Industrial Evolution – Helping Engineers to Slash Break-Down Maintenance Time with Gen AI

In-Short

CaveatWisdom

Caveat: If root cause during a machine break-down maintenance is not identified with-in time and proper procedures to fix the problem safely is not followed in a challenging industrial environment then they can lead to lot of production loss and create safety concerns for maintenance personal.

Wisdom: Expert level understanding and huge experience in interpreting the problem in machines is required to troubleshoot and fix machines. By leveraging cutting edge Generative AI and IoT technologies which help in understanding root cause and fix problems, break-down maintenance time can be slashed from days to few hours.