AI Agent Testing: Systematically validate planning, memory, tool use, and failure recovery
Evaluate an AI agent's planning, memory, tool feedback, and stopping decisions across multi-step tasks and recovery paths.
Quality Analyst, Not Quality Assurance.
Page 2
Evaluate an AI agent's planning, memory, tool feedback, and stopping decisions across multi-step tasks and recovery paths.
A source-grounded review of the two PHP2Shell flaws, their impact and disclosure timeline, AI-assisted research, and the response expected from website and device operators.
Agent Memory Testing field guide: review memory scope, retrieval, staleness, deletion, consent, and evidence across conversations.
An introduction to common screen readers, getting started, and questions to ask while testing headings, forms, dynamic content, and links.
Follow a complete Prompt-to-Skill example and learn how sample evaluation, a quality loop, and a team pilot can capture an AI testing method.
Trace secret exposure across configuration, logs, build output, and rotation evidence without accessing real credentials.
A reproducible comparison method with illustrative outputs for one login requirement; repeated model runs are needed to measure stability and revision time.
A source-led review of Melissa Fisher and Rosie Sherry's discussion on QMS, friction mapping, product risk, quality orchestration, and AI-era collaboration, with interview views separated from engineering extensions.
Review API contracts and sanitized evidence for identity, object access, inputs, errors, and rate limits; prepare checks pending authorization.
Review session creation, fixation, expiry, revocation, concurrent use, and cross-device security boundaries.
Use requirement traceability, risk coverage, and assertion review to find omissions, unsupported assumptions, unexecutable steps, and false passes in AI-generated test cases.
Use a login example to give AI project context, generate API and UI automation, and verify assertions, data isolation, and execution results.