The agent explosion is coming
Agents are independently handling end-to-end processes across lead generation, supply chain optimization, customer support, and financial reconciliation. A mid-sized organization could easily run 4,000 agents, each making decisions that affect revenue, compliance, and customer experience.
The transformation toward an agent-driven enterprise is inevitable. The economic benefits are too significant to ignore, and the potential is becoming a reality faster than most predicted. The problem? Most businesses and their underlying infrastructure are not prepared for this shift. Early adopters have found unlocking AI initiatives at scale to be extremely challenging.
The reliability gap that’s holding AI back
Companies are investing heavily in AI, but the returns aren't materializing. According to recent research from Boston Consulting Group, 60% of companies report minimal revenue and cost gains despite substantial investment. However, the leaders reported they achieved five times the revenue increases and three times the cost reductions. Clearly, there is a massive premium for being a leader.
What separates the leaders from the pack isn't how much they're spending or which models they're using. Before scaling AI deployment, these “future-built” companies put critical data infrastructure capabilities in place. They invested in the foundational work that enables AI to function reliably.
A framework for agent reliability: The four quadrants
To understand how and where enterprise AI can fail, consider four critical quadrants: models, tools, context, and governance.
Take a simple example: an agent that orders you pizza. The model interprets your request ("get me a pizza"). The tool executes the action (calling the Domino's or Pizza Hut API). Context provides personalization (you tend to order pepperoni on Friday nights at 7pm). Governance validates the outcome (did the pizza actually arrive?).
Each dimension represents a potential failure point: