Originally published in Forbes Technology Council
Arango CEO Shekhar Iyer shares his perspective on why the real cost of agentic AI is not just infrastructure spend, but the engineering effort required to rebuild business context across fragmented systems.
As enterprises move AI agents from pilots into production, many leaders focus on visible costs such as GPU usage, token consumption and infrastructure. Shekhar argues that the larger hidden cost is often misallocated engineering effort: teams spending too much time stitching together disconnected systems instead of building features that create business value.
AI agents need accurate, real-time business context to operate reliably. When that context is scattered across systems, teams must continuously rebuild the data architecture needed to support AI. This drives higher integration costs, greater operational overhead, more latency and increased token consumption.
To avoid this trap, leaders should design data architectures for production scale from day one and measure how much engineering time is spent rebuilding context across fragmented systems. Organizations that address data and context architecture early will be better positioned to reduce costs and scale AI successfully.
Key Takeaways
- The hidden cost of agentic AI is often engineering effort spent rebuilding business context across fragmented systems.
- AI pilots frequently break at production scale when they lack a long-term data and context architecture.
- Leaders should measure data-plumbing effort directly and design AI data architectures for production from day one.
