Modern enterprises are excited by AI agents—software bots that can plan, reason, and act autonomously. A recent MIT Sloan/BCG survey found 35% of companies had deployed AI agents by 2025 (with another 44% planning to soon).
Yet this enthusiasm masks a stark production gap. Industry analysts warn that most agentic AI initiatives stall at the prototype stage. Gartner predicts over 40% of agent projects will be canceled by 2027 due to spiraling complexity and unclear ROI. Only roughly 11% of firms report AI agents in full production.
The challenges are manifold: models alone are insufficient without robust orchestration, memory, security, and monitoring layers. This report breaks down the engineering stack needed to bridge the gap, with data-backed insights at every layer.