As individuals and companies deploy numerous specialized AI agents, managing them via simple interfaces like Telegram becomes untenable. This creates a demand for sophisticated "Mission Control" dashboards to monitor agent health (e.g., heartbeats, cron jobs), track persistent information, and manage the entire agent fleet effectively.

Despite industry talk, there is currently no software that can orchestrate and manage various third-party AI agents from different vendors. Teams must manage each agent in its own siloed interface, creating significant operational overhead.

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.

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.

True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

Instead of serial tasking, advanced users are becoming "agent jockeys," managing multiple AI instances simultaneously. Each agent performs a complex task in the background (e.g., ad generation, outreach), requiring the user to context-switch and manage a portfolio of automated workstreams to maximize output.

Building a single AI tool is not enough. The real value lies in becoming the 'conductor,' creating a system that orchestrates multiple specialized AI agents to complete complex workflows. Whoever owns this coordination layer owns the entire value flow.

The race in enterprise AI isn't just about agent capabilities, but about owning the central dashboard where employees direct agents across all applications (Salesforce, Jira, etc.). Companies like OpenAI and Microsoft are vying to become this primary interface, controlling the customer relationship and relegating other apps to the background.

The durable investment opportunities in agentic AI tooling fall into three categories that will persist across model generations. These are: 1) connecting agents to data for better context, 2) orchestrating and coordinating parallel agents, and 3) providing observability and monitoring to debug inevitable failures.