Test Your AI Agents Before They Act Autonomously

The Agentic AI Safety Assessment is genai.qa’s most specialized engagement - a 7-10 day deep assessment of autonomous AI agents that tests the unique failure modes that exist only when AI systems make decisions and take actions independently.

Why Agent Testing Is Different

AI agents are fundamentally different from chatbots and copilots. A chatbot generates text. An agent takes actions. It calls APIs, executes code, modifies databases, sends emails, and makes multi-step decisions autonomously. When an agent fails, the failure has real-world consequences - financial transactions executed incorrectly, data modified without authorization, external services called with wrong parameters, or cascading failures that amplify a single error into a system-wide incident.

The testing methodology for agents must account for these differences. Standard LLM evaluation - hallucination rate, coherence, relevance - is necessary but insufficient. Agent testing requires evaluating decision-making quality, tool selection correctness, planning robustness, boundary enforcement under adversarial conditions, and the effectiveness of human-in-the-loop escape hatches.

What We Test

Tool use correctness - Does the agent select the right tool for each task? Does it pass correct parameters? Does it handle tool failures gracefully? We test the full matrix: right tool/wrong params, wrong tool/right params, wrong tool/wrong params, and tool selection under ambiguous conditions.

Multi-step planning - Does the agent construct valid multi-step plans? Does it recover when an intermediate step fails? Does it detect when a plan is leading to an unsafe outcome? We test planning robustness across 50+ scenarios.

Safety boundary enforcement - Can adversarial inputs cause the agent to exceed its authorized scope? Can it be manipulated into calling tools it should not call, accessing data it should not access, or taking actions it should not take? We test every boundary under adversarial pressure.

Runaway and loop detection - Does the agent detect and halt cascading failures? Are there effective cost caps and iteration limits? We test loop scenarios where the agent could run indefinitely, consuming API credits and amplifying errors.

Human-in-the-loop effectiveness - Are the escape hatches real? When the agent encounters an uncertain or high-stakes decision, does it actually pause and request human input? Or does the human oversight mechanism exist only in the architecture diagram?

This is the highest-complexity GenAI QA engagement we offer, and the one where the gap between testing and not testing carries the greatest financial and operational risk.

Book a free scope call to discuss your agent’s specific safety testing requirements.