Performance & Quality Checks

• Evals track metrics like accuracy, latency, virtualization and user satisfaction across all AI-driven tasks. In financial reconciliation, for instance, they verify that invoice classifications match correct ledger codes, ensuring near-zero mismatches. In contact center operations if AI starts over-relying on humans (or vice versa), Evals adjust those thresholds to maintain business goals are met. 
  
  

• They often leverage six sigma frameworks, gauging how well the AI synthesizes data, whether communication is clear, and if the overall process meets enterprise standards.
  
  

Compliance & Bias Audits

• Evals focus on the regulatory side—be it SOX compliance in finance or GDPR for data handling—ensuring automated decisions don’t break rules or policies.
  
  

• They also scan for subtler signs of bias, like an LLM inadvertently funneling certain customers into longer wait times, and escalate any red flags for immediate corrective action.
  
  

Feedback Loops & RLHF (Reinforcement Learning from Human Feedback)

• By gathering real-world feedback, Evals feed misclassifications or misunderstandings back into the model. For instance, if an LLM keeps mismatching invoice line items, Evals collect these corrections from finance teams and retrain the AI to adapt. Over time, the Agents “learns” from these corrections, staying both compliant and highly accurate.
  
  

Monitoring Agentic Evolution

• Modern AI systems aren’t static; they evolve in real-time. Evals keep a pulse on the direction and speed of these changes, ensuring newly acquired behaviors don’t drift outside established guardrails. If an agent’s adjustments start conflicting with compliance or business rules, Evals intervene to restore balance.