And it’s a wrap! We just closed two intense weeks shaped by our AI Forums in Singapore and Sydney. The events revealed both the extraordinary momentum behind enterprise AI and the scale of the reinvention still ahead. Across the two cities, we brought together close to 400 business and technology leaders. In the tech track, […]

Most enterprises govern the systems around AI agents, not the agents themselves. They control credentials, log tool calls, and route high-risk actions to a human approver. These are useful controls, but they were designed for software that follows instructions. Agents reason at runtime, and that reasoning can turn compliant actions into a noncompliant outcome that […]

As AI agents take on critical business workflows, enterprise technology is shifting toward a stack comprised not of applications, but of cognitive skills. This creates three architectural concerns around building agents, orchestrating them in business processes, and governing them across the enterprise. The first two concerns have established platform categories: the build and orchestrate planes. The third, the agent control plane, remains an emerging architectural layer and represents a growing gap in enterprise readiness. This report introduces the three-plane model and makes the case for a distinct, independent control plane.

B2B’s structured workflows, high transaction volumes, and existing policy infrastructure make it the optimal early deployment context for agentic payments to automate finance operations, streamline working capital management, and enable new machine-to-machine (M2M) transaction models. As supporting technology protocols and risk controls mature, finance and technology leaders must understand where agentic payments create value and build a pragmatic strategy for adoption and execution.

AI agents are reshaping how work gets done, blurring the line between human and machine capabilities. A cognitive operating model provides a framework for designing, governing, and orchestrating shared cognitive skills across people, AI agents, and hybrid teams to unlock more adaptive, scalable, and effective ways of working.

Enterprise agentic AI architectures are shaped by distinct decisions that organizations often resolve implicitly through default vendor configurations or team habits. Implicit resolution produces architectures that resist scaling, governance, and cost control. Organizations that take explicit positions across all 20 decisions build coherent, governable agentic systems. This report provides a decision framework for choosing agentic AI architectures.

As AI and agentic technologies make inroads into the tech stack, enterprise operating models are beginning to transform. Organizations that navigate this journey must replace legacy use-case-based framing of AI development in favor of newer mental models. This report introduces a skills-based framing that supports the design and build of composable, governable, and durable agentic tech stacks.

You can use agentic control plane solutions to govern a fragmented agent estate, contain the risk that agent autonomy introduces, and keep production agents observable and accountable. But to realize these benefits, you’ll first have to select from a diverse set of vendors that vary by size, type of offering, geography, and use case differentiation. Enterprise architecture and AI leaders should use this report to understand the value they can expect from an agentic control plane solution vendor, learn how vendors differ, and investigate options based on size and market focus.

Tools like OpenClaw, Claude Cowork, and Perplexity Computer signal a shift from prompting to delegated execution. As these persistent AI agents hold context, call tools, and keep work moving, enterprises must redesign roles, authority, architecture, context, and experimentation models. This report explains what is changing, why it matters, and how leaders can capture the upside before shadow adoption sets the operating model for them.

Most enterprises are chasing agentic AI; almost none have caught it. Forrester’s report, “The State Of Agentic AI, 2026,” digs into why investment isn’t turning into scale, why orchestration and governance lag ambition, and what separates the companies pulling ahead.