QA tips, tutorials, and product updates
Microsoft's voluntary exit offer to 60,000 eligible employees raises questions about AI, automation, and QA careers. Explore the impact on software testing and the skills testers need.
Read Article →
Learn how to test AI agent stop conditions, detect infinite loops, prevent repeated tool calls, verify task completion, and test safe termination.
Read
Learn how to test AI agent tool selection, argument accuracy, unnecessary calls, tool results, failure handling, and end-to-end task completion.
Read
Learn how to test AI agent handoffs, including context loss, incorrect routing, conflicting results, duplicate actions, retries, and multi-agent workflow reliability.
Read
Learn how to test AI agent identity confusion, delegated permissions, session isolation, service account risks, authorization boundaries, and security regression.
ReadLearn how to test AI agent memory poisoning, including malicious memory writes, cross-session attacks, trust boundaries, unsafe actions, detection, and recovery.
ReadLearn how to test AI agent memory, including retrieval accuracy, outdated information, forgetting, cross-user isolation, tool execution, and regression testing.
Read
Learn how AI agent behavioral regression testing detects changes in tool usage, execution paths, state, side effects, safety, recovery, cost, and agent consistency.
ReadLearn how AI agent trajectory testing evaluates tool calls, decisions, state changes, recovery, safety, efficiency, and execution paths—not just final results.
ReadLearn how AI agent sandbox testing safely evaluates autonomous AI, tool boundaries, permissions, failures, prompt injection, recovery, side effects, and production readiness.
ReadLearn how AI agent recovery testing validates retries, failure handling, state reconciliation, rollback, escalation, idempotency, and safe recovery from agent failures.
ReadLearn how AI agent authorization testing verifies permissions, least privilege, tool access, delegation, tenant isolation, and unauthorized actions in AI systems.
ReadLearn how AI side-effect testing can verify autonomous software actions, detect unintended changes, prevent duplicate operations, validate tool usage, and improve AI agent safety.
ReadLearn how AI state-based testing can discover hidden bugs between application states, generate transition scenarios, improve state coverage, and test complex workflows.
ReadLearn how AI user journey testing can validate complete customer workflows, discover missing scenarios, test variations, analyze failures, and improve end-to-end software quality.
ReadLearn how AI release risk scoring helps QA teams assess code changes, test results, coverage gaps, business impact, dependencies, and historical defects before deployment.
Read