Traditional quality engineering asks whether the software does what it was specified to do. AI systems add questions traditional test cases were not written to answer: Is the answer grounded? Did the tool call stay inside permissions? What happens when the model is wrong? How do cost and latency behave under real use?

LVC builds quality into the release for both conventional software and AI. That includes functional QA, automation, API and mobile testing, performance, and accessibility — and, where the system warrants it, AI, LLM, and agent testing.

Agent testing is especially concrete. An agent that can act needs tests for the action: the tools it may call, the approvals it must wait for, and the audit trail left behind.

Evaluation does not end at launch. Accuracy, groundedness, tool execution, cost, and latency are operating measures. A testing strategy that stops at a demo will not protect a production workflow.