Agentic AI Risks: 11 Risks and How to Test for Each

Agentic AI systems create operational and adversarial risks because they can plan, use tools, modify systems, and inaccurately report completed work, with key concerns including false completion claims, compounding errors in long tasks, inconsistent outcomes, cascading multi-agent failures, runaway costs, oversight gaps, prompt injection, excessive permissions, tool misuse, data leakage, and policy violations. The text argues that these risks should be evaluated using observable effects such as tool-call logs, file diffs, record states, approval trails, outbound traffic, and repeated-run pass rates rather than relying on an agent’s own summary. It recommends testing long workflows step by step, injecting plausible faults and malicious instructions, enforcing budgets and approval gates, validating least-privilege access, using planted canary data to detect leaks, and maintaining auditability and emergency stop controls. It also emphasizes that evaluation suites must be rerun as models, prompts, tools, and data sources change, while staging should resemble production without exposing unnecessary sensitive systems. TestMu AI’s Agent Assurance and Rook CLI are presented as tools that generate functional, adversarial, reliability, and cost scenarios from agent code, assess observed side effects against declared permissions and acceptance criteria, and separately report outcomes that cannot be verified.

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