Confidence in agentic AI: Why eval infrastructure must come first

10 months ago

AI agents are being deployed to save human capital and transform business operations.
Rocket Companies saw a 3x increase in website conversion rates using AI agents.
An AI agent built in two days saved Rocket $1 million annually by automating mortgage underwriting tasks.
AI agents saved Rocket over a million team member hours in 2024, allowing employees to focus on client needs.
Team members at Rocket handled 50% more clients due to AI-driven efficiency gains.
Engineering teams are shifting from deterministic software engineering to probabilistic AI approaches.
LLMs have improved, making AI agents more predictable, but challenges remain in model orchestration and scalability.
Scaling AI agents involves solving technical problems like latency and agent routing.
Companies initially build AI agents in-house but struggle with maintenance and evolving infrastructure.
Agentic AI complexity will grow, requiring robust checks, human oversight, and monitoring systems.
Evaluating AI agents requires pre-built test infrastructure and continuous validation against benchmarks.
Non-deterministic AI behavior necessitates large-scale simulation and scenario testing for reliability.

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