As companies deploy numerous task-specific AI agents (e.g., payroll, payments), the user experience risks fragmentation. Xero's solution is a 'super agent' that manages all sub-agents, orchestrating actions, transferring information, and applying user preferences globally to create a cohesive system.

The overhead of maintaining personal AI agents is too high for most employees. The successful model, seen at Shopify and Ramp, is a centralized, company-wide "super-agent" managed by a dedicated team, ensuring it remains reliable and useful for everyone.

After successfully deploying three or four AI agents, companies will encounter a new challenge: the agents have data conflicts and provide inconsistent answers. The solution, which is still nascent, is a "meta-agent" or orchestration layer to manage them.

As AI evolves from single-task tools to autonomous agents, the human role transforms. Instead of simply using AI, professionals will need to manage and oversee multiple AI agents, ensuring their actions are safe, ethical, and aligned with business goals, acting as a critical control layer.

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.

The context switching required to manage numerous AI agents is immense. Each agent functions differently, with its own interface, language, and needs, creating a mental burden equivalent to managing a large team of diverse individuals.

The next frontier in AI is not just developing individual agents, but orchestrating teams of them. Users will move from dialoguing with a single chatbot to managing multiple agents working in parallel on complex, long-running workflows. This becomes a new core skill for knowledge workers.

Encouraging unmanaged creation of AI agents—or "agent sprawl"—results in conflicting outputs and fragmented customer messaging. With different agents accessing different data sources, companies get inconsistent answers to simple questions like company ARR, undermining strategic alignment.

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.