Would your AI agent get the job? A performance review framework for enterprise agents

Official Schedule Context

There are dozens of ways to build an enterprise AI agent: agentic frameworks, direct LLM APIs,

conversational AI platforms, vertical SaaS. They all claim to do the job. But how do you actually

compare them on the same task, with the same data, against the same KPIs? This session presents a

vendor-agnostic evaluation framework that treats AI agents the way enterprises treat new hires: set

the role, define success criteria, run candidates through identical scenarios, and measure outcomes.

The architecture uses any LLM to track positive and negative drift across agents against weighted

goals, monitoring everything from hallucination rates and token consumption to user sentiment and

conversation quality. Inputs are standardized. Outputs are both quantitative (accuracy, cost, hours

saved) and qualitative (tone, clarity). The methodology supports continuous evaluation, not just

pre-deployment benchmarks, but ongoing performance reviews that can compare agent work against human

baselines. Walk away with a concrete, repeatable process for answering the only question that

matters: which agent actually does the job?

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