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In one month, the enterprise software industry bought its way toward agents that act rather than advise. The controls to govern them are running behind.
The Model Context Protocol has shifted from a technical curiosity to a default question in enterprise software evaluation. Following CircleCI’s June 2026 MCP server release and Databricks’ move to govern MCP services in Unity Catalog, this article explains what MCP changes for integration cost, vendor lock-in, and AI governance, and sets out the practical procurement questions technology leaders should write into their evaluations.
More than $40 billion has gone into enterprise AI, and 56% of CEOs say they have nothing to show for it – the gap has surprisingly little to do with the models
Google now processes 3.2 quadrillion tokens a month, up from 480 trillion a year ago. While vendors compete on per-token prices, the organisations pulling ahead on AI are the ones tracking a different metric entirely: tokens per business outcome. This article explains why per-token price is the wrong number to optimise, introduces tokens per resolved outcome as the unit that makes agentic AI economics legible, and sets out the three architectural patterns – task decomposition, context discipline, and evaluation-driven model selection – that create durable cost and performance advantages in agentic AI programmes.
88% of organisations confirmed or suspected AI security incidents this year, yet only 14.4% have full security approval for the agents they have deployed. Learn why agent adoption has created an entirely new attack surface and how to adapt your security and data architecture.
While nearly two-thirds of enterprises have experimented with AI agents, fewer than 10% have successfully scaled them, with 80% citing data limitations as the core obstacle. This article explores why the modern pipeline-centric data stack, designed for human analysts, struggles with autonomous AI systems, and examines the critical shift toward semantic metadata, upstream quality enforcement, and machine-readable context.
dbt Labs’ 2026 State of Analytics Engineering Report found trust in data has surged to 83% as the top organisational priority — the steepest single-year rise ever recorded — driven by 71% of data professionals concerned about incorrect AI outputs reaching stakeholders. With only 7% of enterprises saying their data is completely AI-ready and Gartner finding organisations that see returns invest four times more in data foundations, this article examines the acceleration-to-governance gap and what agent-ready data infrastructure actually requires.
UiPath’s 2026 report found 78% of executives believe operating model reinvention is essential for agentic AI. McKinsey found 80% are seeing no bottom-line impact. This article examines the structural, foundational, and governance barriers creating that gap — and what the organisations pulling ahead are doing differently.
With the launch of C3 Code and Anthropic’s Project Glasswing, Agentic AI is here. Learn why modular software architecture is a non-negotiable prerequisite to safely scale autonomous AI agents in the enterprise.
An in-depth look at why many agentic AI initiatives struggle, when AI agents add real value, and how organisations can design workflows where humans and agents work effectively together.
