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.
The Four Metrics
Every loop scored against the same four metrics, every time. Numbers, not vibes.
Correctness
Did the agent return the right answer? Measured against ground-truth datasets co-built with business teams during development — the same datasets that later anchor regression testing in production.
Completeness
Did the agent finish the task end-to-end, or stop halfway? Multi-step workflows, tool chains, and clarifying-question loops are all scored for trajectory completion, not just final-answer correctness.
Safety
Hallucinations, jailbreaks, prompt injection, PII leakage, unsafe tool calls. Every invocation is screened. Failures route to human-in-the-loop review and feed the next retraining cycle.
Tool-Call Effectiveness
For agentic systems, model quality is necessary but not sufficient. We score whether the right tool was selected, called with valid arguments, and its result correctly interpreted — the failure modes that LLM-only evals miss.
Frequently Asked
Why are agent evaluations critical?
An agent is only as good as its evaluations. Without them, AI quality is unverifiable, drift goes undetected, and hallucinations reach production. humaineeti scores, retrains, and governs every loop in the Agent SDLC.
What is the evaluation flywheel?
An auto-collect-traces, grounded-verification, response-quality-scoring loop with custom scorer frameworks. Evaluations feed back into model retraining and agent design — a continuous improvement cycle that closes the gap between prototype quality and production quality.
What metrics do you measure?
Correctness, completeness, safety, and tool-call effectiveness — across the full Agent SDLC from prototype to production. Custom scorers extend the framework for domain-specific quality bars.
What is LLM-as-a-Judge?
An evaluation pattern where an LLM scores another LLM's outputs against a rubric. humaineeti combines LLM-as-a-Judge with human-in-the-loop verification and ground-truth datasets for higher-confidence scoring — the LLM scales coverage; humans anchor truth.
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.