AgentKai helps test, evaluate and improve AI systems.
Independent AI Evaluation, AI Red Teaming and LLM Quality Assurance for chatbots, RAG systems, AI agents and user-facing AI applications.
Stochastic Systems Need Stochastic Testing
Traditional Software
Traditional software is built on strict logic. For a given input, the system executes code and generates a single, predictable output.
AI & LLM Systems
AI systems are probabilistic. For a single input, models navigate massive parameter spaces, producing variable, dynamic outputs.
"Instead of checking one fixed expected output, AI evaluation looks at behavior across many possible outputs, prompts, edge cases, and failure modes."
What AgentKai Evaluates
We rigorously challenge and inspect AI systems across five primary pillars.
Chatbots & Assistants
Evaluating conversational flow, user experience, instructional boundaries, and safety behaviors.
RAG Architectures
Testing grounding, context retrieval precision, hallucination rates, and source traceability.
AI Agents
Assessing workflow planning, tool call accuracy, context retention, and error recovery loops.
Content Workflows
Reviewing AI-generated outputs for quality, stylistic consistency, tone, and formatting constraints.
Security & Safety
Executing adversarial tests including prompt injection exploits, jailbreaks, and instructions override.
Our Three-Stage Methodology
Challenge
Test systems with adversarial prompts, edge cases, contradictory instructions, context manipulation, and unusual user scenarios.
Evaluate
Analyze failures, severity, reproducibility, user impact, business risk, and likely root causes.
Improve
Recommend prompt improvements, retrieval adjustments, workflow guards, active monitoring, and regression tests.
Featured Lab Notebooks
Deterministic vs Stochastic Systems
Detailing why traditional software testing methodologies fall short when confronted with probabilistic AI behaviors and how behavioral evaluation bridges the gap.
Getting Started with Promptfoo
Setting up automated evaluation pipelines for LLM application outputs, writing custom assertions, and analyzing local evaluation metrics.
Prompt Injection Basics
Analyzing direct and indirect vulnerability vectors, staging proof-of-concept injection exploits, and building robust prompt guardrails.
Ready to stress-test your AI application?
Interested in an independent review of your AI assistant, chatbot, RAG system, or agent workflow? Get in touch to discuss practical AI evaluation, red teaming, and quality assessment.