Business Technology Roundtable

Every enterprise racing to deploy Agentic AI is making an infrastructure decision it may not recognize as one. When an AI agent connects to internal systems and proprietary content, the protocol governing that connection determines whether the enterprise controls the terms of access or cedes them by default. Model Context Protocol (MCP) has become the standard linking agents to backend data and tools, and it is spreading through corporate technology stacks faster than the governance frameworks meant to secure it.  For CIOs and CISOs, this stopped being a developer tooling question months ago. It is now a sovereignty question: who defines the boundary between an AI agent and a company's proprietary intelligence. The MCP Adoption Security Gap Gartner projects that by 2028, 25 percent of all enterprise Generative AI (GenAI) applications will experience at least five minor security incidents per year, up from 9 percent in 2025. That trajectory tracks closely with how fast MCP has been ...

The generative AI (GenAI) wave that began with ChatGPT's arrival in late 2022 has already started to feel like yesterday's story. A recent TBR research report on the Applied-AI and GenAI market landscape makes one thing clear: the industry is pivoting fast, and the companies that fail to adapt to agentic AI will find themselves playing catch-up in a market that rewards those who move decisively. For the uninitiated, agentic AI refers to systems that don't just respond to prompts but actively plan, execute, and iterate across complex multi-step workflows with minimal human intervention. This is no longer a futurist talking point. It is reshaping how enterprises think about automation, how IT service firms price their work, and how hyperscalers compete for the next trillion dollars in technology spending. A Market Growing at Breakneck Speed The numbers alone make a compelling case for attention. TBR estimates that combined AI and GenAI revenue across major hyperscalers, inclu...

We have now entered the AI Agentic era, according to the latest series of reports by Google's artificial intelligence (AI) researchers. The shift from passive generative AI models to autonomous AI agents that can plan, reason, and act on our behalf is the most profound digital transformation in decades. As  Applied-AI Initiatives replace deterministic code, a significant challenge has emerged. Building an AI agent is easy; however, trusting it is complex. The current AI market momentum reveals a stark last-mile gap. While a developer can spin up an AI prototype in minutes, roughly 80 percent of the effort required to reach production is consumed by the work of safety, validation, and infrastructure. The reason is simple: AI agents are non-deterministic. They can pass 100 unit tests but fail catastrophically in the field because of a flaw in their judgment, not a bug in the code. Core Architecture and the Problem-Solving Loop An Applied-AI agent is defined by the synergy of four co...