Building Trust in Agentic AI Starts with Strong Evaluation
▼ Summary
– Organizations are deploying AI agents to save human capital and drive transformational business outcomes, such as increasing website conversion rates and automating specialized tasks.
– AI agents at Rocket Companies saved over a million team member hours in 2024, enabling employees to focus on high-value tasks and handle more clients efficiently.
– Engineering teams must shift from deterministic software development to probabilistic approaches when working with LLMs, which now offer more predictable behavior but require careful orchestration.
– Companies initially build AI agents in-house but face challenges in scaling, maintaining, and updating them, leading to reliance on specialized vendor expertise.
– Preparing for AI agent complexity requires robust evaluation infrastructure, human oversight for critical processes, and large-scale simulation to ensure reliability.
The rise of agentic AI is reshaping business operations, but success hinges on robust evaluation frameworks and strategic implementation. Companies deploying these intelligent systems are discovering their potential extends far beyond simple automation, they’re unlocking new levels of efficiency, customer engagement, and revenue growth.
Financial services firm Rocket Companies provides a compelling case study in AI agent deployment. Their conversational interfaces tripled website conversion rates, while specialized agents automated complex tasks like mortgage tax calculations, delivering $1 million in annual savings from just two days of development work. More importantly, these tools freed employees from repetitive tasks, enabling them to handle 50% more clients while focusing on high-value interactions.
Scaling AI agents introduces unique technical hurdles that differ from traditional software development. Unlike deterministic systems, large language models (LLMs) operate probabilistically, the same input can yield different outputs. Modern models have improved predictability, but challenges remain in orchestrating multiple agents, managing latency, and ensuring consistent performance across millions of interactions.
Three critical considerations emerge for organizations implementing agentic AI:
- Specialization beats generic solutions
- Observability becomes mission-critical
- Evaluation frameworks require new approache
The path forward involves balancing innovation with governance. As agent networks grow more sophisticated, interacting with each other and expanding across use cases, companies must invest in both technical infrastructure and ethical safeguards. The organizations that master this balance will unlock AI’s full potential while maintaining operational control and customer trust.
(Source: VentureBeat)
