At humaineeti, we systematically measure, improve and maintain the quality of LLM applications and AI agents throughout the Agent SDLC.
During development we collaborate extensively with business teams to gather and generate ground truth datasets to proceed with manual evaluation. We harness results of manual evaluations by scoring critical-to-quality metrics like correctness, completeness, tool call effectiveness, safety among others.
Our evaluation-driven development ensures that human-in-the-loop controls are effectively applied to tackle the challenge of building high-quality LLM/Agentic applications.
At humaineeti we follow evaluation flywheel of:
Automated collection and logging of every agentic invocation and interaction.
Human verification using ground truth datasets provided by the business.
Scoring across correctness, completeness, safety, and tool call effectiveness.
LLM Judges to inspect common failure modes.
Combining human expertise with LLM-based evaluation for comprehensive quality assurance.