Multi-Agent Orchestration for QA: Workflows and Frameworks
Learn multi-agent orchestration for QA. See when one agent is enough, how an orchestrator controls each step and when a framework is really useful.
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1,677 articles · page 7 of 140LLM Provider Adapter: Switch AI Models Without Rewriting Code
Build an LLM provider adapter in TypeScript so your QA agent can switch AI models, test with a fake provider and retry invalid output safely.
LLM Structured Output: Validate AI Responses with a Schema
Learn LLM structured output for QA agents. Validate AI responses with a schema, handle broken JSON and catch wrong values that a schema cannot see.
Build an AI Agent from Scratch: Your First One-Tool QA Agent
Learn to build an AI agent from scratch in TypeScript with no framework. Create a safe one-tool QA agent that reads test results and summarises failures.
The AI Agent Loop Explained: Think, Act, Observe and Stop
Understand the AI agent loop step by step: think, call a tool, read the result and stop. See runnable TypeScript with a fake model and safe stop limits.
AI Agent Building Blocks: Role, Context, Tools and Guardrails
Learn the AI agent building blocks with QA examples: role, context, goal, skills, tools, constraints, guardrails, memory, workflow and output schema.
AI Agent vs Chatbot vs Automation Script: What Testers Should Know
Understand AI agent vs chatbot vs automation script in simple words, with QA examples, and learn when a tester should not build an agent at all.
LLM Regression Testing in CI: Evaluation Tools That Catch Drift
Learn LLM regression testing in CI. Compare prompt and model versions, use evaluation tools such as promptfoo and set quality gates that catch drift.
Prompt Injection Testing: Jailbreaks, Data Leakage and Bias
Learn prompt injection testing safely. Understand jailbreaks, data leakage and bias, use a canary to detect leaks and test only systems you may test.
Testing AI Agents: Tool Calls, Loops and Stop Conditions
Learn testing AI agents as a QA engineer. Check tool choice, arguments, loops, stop conditions and human approvals before an agent can take real actions.
Testing RAG Applications: Retrieval, Grounding and Answers
Learn testing RAG applications step by step. Check document retrieval and answer grounding separately, measure precision and recall, and find the real fault.
LLM Evaluation Metrics for Testers: Faithfulness, Relevance and Hallucination Rate
Learn LLM evaluation metrics for testers: faithfulness, relevance and hallucination rate, with simple calculations and advice on using AI judges safely.
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