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
Four stages, every project. Powered by our Eval@Core accelerator — auto-collect traces, ground-truth verification, response quality scoring, and a custom scorer framework that turns evaluation into a continuous loop.
- 01TraceAuto-collect every agentic invocation and interaction.
- 02VerifyGround-truth verification — humans-in-the-loop, with LLM-as-a-Judge support.
- 03ScoreCorrectness, completeness, safety, tool-call effectiveness — all four, every loop.
- 04RetrainFindings feed model retraining and agent redesign. The loop closes; the work continues.
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.