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.
Evaluation Flywheel
At humaineeti we follow evaluation flywheel of:
This flywheel is powered by our Eval@Core accelerator — auto-collect traces, grounded verification, response quality scoring, and a custom scorer framework that turns evaluation into a continuous, iterative loop.
Auto Collect Traces
Automated collection and logging of every agentic invocation and interaction.
Human-in-the-Loop Grounded Verification
Human verification using ground truth datasets provided by the business.
Response Quality Assessment
Scoring across correctness, completeness, safety, and tool call effectiveness.
LLM Judges
LLM Judges to inspect common failure modes.
Human LLM-as-a-Judge Collaboration
Combining human expertise with LLM-based evaluation for comprehensive quality assurance.
Related Resources
- Agent Eval for Drift & Hallucination — Techniques to detect and mitigate drift and hallucination in AI agent outputs.
- Agent Skills vs Frontier LLMs — Learn why agent architecture and skill design matter more than model size alone.
- LLMOps in Production — A practical guide to operationalizing LLM applications at enterprise scale.