Systems | Development | Analytics | API | Testing

RAG and Agentic AI shape how intelligent systems interact with data and users. RAG enhances LLMs by retrieving external information to improve accuracy and contextual relevance, while Agentic AI introduces autonomy, decision-making, and adaptability into AI-driven workflows. Agentic RAG combines the power of both, transforming RAG into a multi-step, autonomous, complex process that can self-improve.

Model Context Protocol (MCP), introduced by Anthropic, is a new standard that simplifies artificial intelligence (AI) integrations by providing a secure, consistent way to connect AI agents with external tools and data sources. When we saw MCP’s potential, we immediately started exploring how we could bring real-time data streaming into the mix. With our long history of supporting open source and open standards, building an MCP server was a natural fit.

At ThoughtSpot, our mission has always been to empower every decision-maker with instant insights that fuel smarter, faster decisions. Recently, we announced the launch of Spotter, our AI Analyst, which brings AI-powered insights to every user, on any question, and any dataset. This is ThoughtSpot's answer to a growing market of AI agents, and it’s our vision to make AI the new BI.

Generative AI isn’t just moving fast—it’s on turbo mode. Gartner confirms it in their popular Hype Cycle, compared to other evaluated technologies: gen AI tech is rocketing through the stages faster than anything else. In under three years, it’s already crashing into the trough of disillusionment, while prompt engineering shot to peak hype almost the second it emerged.

Nearly nine out of 10 business leaders say their organizations’ data ecosystems are ready to build and deploy AI, according to a recent survey. But 84% of the IT practitioners surveyed spend at least one hour a day fixing data problems. Seventy percent spend one to four hours a day remediating data issues, while 14% spend more than four hours each day.