AI Engineering · Agent Evaluations Agents are only as good as their evaluations.
We systematically measure, improve and maintain the quality of LLM applications and AI agents — scoring, retraining and governing every loop of the agent software development lifecycle (SDLC), from prototype to production.
Why it matters
Without evaluations, quality is unverifiable.
Evaluation-driven development puts human-in-the-loop controls at the hardest points in building LLM and agentic applications.
- 01Without evaluations, AI quality is unverifiable, drift goes undetected and hallucinations reach production.
- 02During development, we work with your business teams to gather and generate ground-truth datasets for manual evaluation.
- 03We score manual evaluation results against critical-to-quality metrics — correctness, completeness, tool-call effectiveness and safety.
- 04The same ground-truth datasets later anchor regression testing in production.
The four metrics
Quality measured in numbers, not impressions.
We score every loop against the same four metrics, every time.
Correctness
Did the agent return the right answer? We measure it against ground-truth datasets built with your business teams during development.
Completeness
Did the agent finish the task, or stop halfway? We score multi-step workflows, tool chains and clarifying-question loops for trajectory completion, not just final-answer correctness.
Safety
Hallucinations, jailbreaks, prompt injection, PII leakage and unsafe tool calls. We screen every invocation; failures route to human-in-the-loop review and feed the next retraining cycle.
Tool-call effectiveness
Did the agent select the right tool, call it with valid arguments and interpret the result correctly? These are the failure modes LLM-only evaluations miss.
Evaluation flywheel
Trace. Verify. Score. Retrain.
Four stages on every project — trace, verify, score and retrain — turn evaluation into a continuous loop.
/ 01
Trace
Automatically collect every agentic invocation and interaction.
/ 02
Verify
Verify against ground truth with humans in the loop, supported by LLM-as-a-Judge.
/ 03
Score
Score correctness, completeness, safety and tool-call effectiveness — all four, on every loop.
/ 04
Retrain
Feed findings into model retraining and agent redesign. The loop closes; the work continues.
What the practice runs
Evaluation as a continuous loop.
Auto-collected traces
Automated collection and logging of every agentic invocation and interaction.
Human-in-the-loop grounded verification
Human verification against ground-truth datasets provided by the business.
Response quality assessment
Scoring across correctness, completeness, safety and tool-call effectiveness.
LLM judges
LLM judges that inspect common failure modes.
Human and LLM-as-a-Judge collaboration
Human expertise combined with LLM-based evaluation for comprehensive quality assurance.
Custom scorer framework
Custom scorers that extend the framework to domain-specific quality bars.
Where it fits
The quality gate across the agent SDLC.
Agent Evaluations scores the agents we build for the Future of Work, grades retrieval and generation in RAG systems, and sets the quality gates in the GenAI Delivery Factory.
FAQ
Frequently asked.
01Why 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. We score, retrain, and govern every loop of the agent SDLC.
02What is the evaluation flywheel?
A loop of automated trace collection, grounded verification and response quality scoring, extended by 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.
03What 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 to domain-specific quality bars.
04What is LLM-as-a-Judge?
An evaluation pattern in which one LLM scores another LLM’s outputs against a rubric. At humaineeti, we combine 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 guides
Keep reading.
Agent Evaluations · 6 min
Agent Eval to Detect Drift & Prevent Hallucination
Why continuous evaluation is the only reliable defence against AI systems that silently degrade in production.
Read the guide
Agentic AI · 7 min
Agent Skills vs Frontier LLMs
Frontier models keep getting smarter. Here's why that makes structured agent skills more important, not less.
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GenAI Operations · 7 min
LLMOps in Production
Monitoring, evaluation, guardrails, and governance for enterprise generative AI at scale.
Read the guideNext step
Know what your agents actually do.
Bring an agent in prototype or production. We’ll show you how traces, ground-truth verification and scoring turn its quality into numbers you can govern.