In this episode of Inference Time Tactics, Rob, Cooper, and Byron explore Salesforce’s CRMArena-Pro benchmark and what it reveals about the limits of enterprise AI agents. They share why benchmark scores often fail in production, how inference-time tactics like best-of-N can improve reliability, and what NeuroMetric is building to make eval easier—from an ITC Test Engine to a drag-and-drop interface for rapid visualization and experimentation.
 
We talked about:
 

Why Salesforce’s CRMArena-Pro benchmark highlights the gap between lab benchmarks and real-world agent reliability.
How leading models perform inconsistently across single-turn and multi-turn enterprise tasks.
Why benchmark scores are weak predictors of operational success in production.
The role of inference-time tactics in reducing variance and improving stability.
NeuroMetric’s new platform: ITC Test Engine and drag-and-drop interface for experimentation.
Challenges in building agentic systems, from database integration to managing multi-prompt complexity.
Why large language models’ stochastic nature conflicts with business demands for reliability.
Latency, cost, and rate limits as major bottlenecks in scaling agentic workflows.
The limits of “vibe testing” and why rigorous evaluation frameworks are essential.
How Google’s Stacks tool speeds up evaluation with LLM-as-judge, and why it still falls short for enterprise needs.




Resources Mentioned:
CRMArena-Pro from Saleforce:
https://www.salesforce.com/blog/crmarena-pro/  
 
Connect with Neurometric:
Website: https://www.neurometric.ai/ 
Substack: https://neurometric.substack.com/ 
X: https://x.com/neurometric/ 
Bluesky: https://bsky.app/profile/neurometric.bsky.social
 
Hosts:
Rob May
https://x.com/robmay 
https://www.linkedin.com/in/robmay
 
Calvin Cooper
https://x.com/cooper_nyc_ 
https://www.linkedin.com/in/coopernyc
 
Guest/s:
Byron Galbraith
https://x.com/bgalbraith 
https://www.linkedin.com/in/byrongalbraith

Ver todos los episodios