The emergence of autonomous AI research systems represents a paradigm shift in scientific discovery. Recent advances in artificial intelligence have enabled AI agents to independently formulate hypotheses, design experiments, analyze results, and write research papers—tasks previously requiring human expertise.
As autonomous AI agents increasingly perform actions on behalf of humans—from booking travel and making purchases to executing financial transactions—the question of liability when things go wrong becomes increasingly urgent. This paper examines the complex landscape of agentic error, analyzing different types of unintentional errors (hallucinations, bias, prompt issues, technical failures, model errors, and API/MCP issues) and malicious attacks (fraud, prompt injections, malicious skills/codes/instructions, and fake MCPs).