Block is re-architecting its entire business by treating all functions—from payments to HR—as a collection of capabilities. These are unified and accessed through a central AI agent middleware layer (Goose), orchestrating workflows across previously siloed product and corporate functions.

As businesses deploy multiple AI agents across various platforms, a new operations role will become necessary. This "Agent Manager" will be responsible for ensuring the AI workforce functions correctly—preventing hallucinations, validating data sources, and maintaining agent performance and integration.

Becoming an "agentic enterprise" requires a foundational shift to an AI-first, conversational way of working. It involves augmenting every employee's workflow with AI assistance for faster decisions, all built upon a foundation of trusted, accessible data that powers the entire system.

The 'agents vs. applications' debate is a false dichotomy. Future applications will be sophisticated, orchestrated systems that embed agentic capabilities. They will feature multiple LLMs, deterministic logic, and robust permission models, representing an evolution of software, not a replacement of it.

A new software paradigm, "agent-native architecture," treats AI as a core component, not an add-on. This progresses in levels: the agent can do any UI action, trigger any backend code, and finally, perform any developer task like writing and deploying new code, enabling user-driven app customization.

Legacy systems like CRMs will lose their central role. A new, dynamic 'agent layer' will sit above them, interpreting user intent and executing tasks across multiple tools. This layer, which collapses the distance between intent and action, will become the primary place where work gets done.

The company leveraged its deep expertise in application integration (its "pre-AI era" business) to build a foundational layer for AI agents, providing the necessary hooks and data pipelines for them to function effectively.

The current market of specialized AI agents for narrow tasks, like specific sales versus support conversations, will not last. The industry is moving towards singular agents or orchestration layers that manage the entire customer lifecycle, threatening the viability of siloed, single-purpose startups.

Salesforce's Chief AI Scientist explains that a true enterprise agent comprises four key parts: Memory (RAG), a Brain (reasoning engine), Actuators (API calls), and an Interface. A simple LLM is insufficient for enterprise tasks; the surrounding infrastructure provides the real functionality.