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Companies are rushing AI agents into production — and many of them will fail. But the reason has nothing to do with their AI models.
On day two of VB Transform 2025, industry leaders shared hard-won lessons from deploying AI agents at scale. A panel moderated by Joanne Chen, general partner at Foundation Capital, included Shawn Malhotra, CTO at Rocket Companies, which uses agents across the home ownership journey from mortgage underwriting to customer chat; Shailesh Nalawadi, head of product at Sendbird, which builds agentic customer service experiences for companies across multiple verticals; and Thys Waanders, SVP of AI transformation at Cognigy, whose platform automates customer experiences for large enterprise contact centers.
Their shared discovery: Companies that build evaluation and orchestration infrastructure first are successful, while those rushing to production with powerful models fail at scale.
The ROI reality: Beyond simple cost cutting
A key part of engineering AI agent for success is understanding the return on investment (ROI). Early AI agent deployments focused on cost reduction. While that remains a key component, enterprise leaders now report more complex ROI patterns that demand different technical architectures.
Cost reduction wins
Malhotra shared the most dramatic cost example from Rocket Companies. “We had an engineer [who] in about two days of work was able to build a simple agent to handle a very niche problem called ‘transfer tax calculations’ in the mortgage underwriting part of the process. And that two days of effort saved us a million dollars a year in expense,” he said.
For Cognigy, Waanders noted that cost per call is a key metric. He said that if AI agents are used to automate parts of those calls, it’s possible to reduce the average handling time per call.
Revenue generation methods
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