developers
Voice AI agents handle phone calls, customer support, and outbound outreach using LLMs. Testing them is fundamentally different from testing chat applications: you're testing speech recognition accuracy, conversation flow correctness, tool call execution from voice context, and latency — all in a medium where failures are immediately obvious to
developers
Structured outputs (JSON mode, response schemas) make LLMs return parseable data instead of free text. They don't make LLMs deterministic. Testing structured outputs means validating schema compliance, semantic correctness, edge case handling, and behavior when the model can't produce valid output — none of which JSON validation
developers
LLM tool calls (function calling) are the integration layer between your AI app and the real world — database queries, API calls, external services. They're also where most AI application bugs hide. This guide covers how to test tool call selection, argument generation, error handling, and multi-step tool
developers
LLM streaming delivers tokens as they're generated, using Server-Sent Events (SSE). This improves perceived performance but introduces new failure modes: partial content on disconnect, chunk reassembly bugs, race conditions in streaming UI, and client-side buffering issues. Testing streaming requires a dedicated approach beyond standard API testing.
developers
AI memory systems (Mem0, Zep, Pinecone-based, or custom vector stores) let agents remember information across conversations. Testing them requires more than verifying that data is stored and retrieved — you need to test retrieval relevance, memory conflicts, context injection into prompts, and what happens when memories degrade or contradict. Key
developers
Every time you change a prompt, update a model, or adjust your context injection logic, you risk a silent regression — the LLM starts producing subtly different outputs that break downstream behavior. Without CI tests for your prompts, you find these regressions in production from user complaints. This guide shows how
developers
AI coding tools change the economics of software development — but they don't change which things break. Auth boundaries, payment edge cases, and data isolation bugs are just as catastrophic when AI wrote the code as when a human did. What changes is the failure pattern: AI-generated code
developers
AI agents fail differently than traditional software — they don't throw exceptions, they confidently take the wrong action or loop forever. Testing them requires a different checklist: goal verification, tool call auditing, loop detection, hallucination guardrails, and end-to-end behavioral tests that simulate realistic user scenarios. This guide
developers
* Claude Artifacts are interactive HTML/JS/React apps that generate inside the Claude.ai chat window in seconds * Claude can write the code, but it can't test runtime behavior — form logic, edge cases, and mobile layout can all break silently * Non-technical users are the biggest risk group:
developers
* GitHub Copilot Workspace turns GitHub issues into working PRs — automatically planning, writing, and committing code changes * It can't run your app or verify user-facing behavior — that gap needs a separate test layer * AI-generated code creates invisible regressions in flows it didn't touch * HelpMeTest adds
developers
* Aider writes code at CLI speed, but it has no visibility into whether the running app behaves correctly. * Code that compiles and passes unit tests can still break the user experience in ways Aider will never catch. * HelpMeTest adds a behavioral QA layer: plain English tests that run against your
developers
Replit Agent 3 can build and self-test your app during development — navigating through it like a real user to catch issues before you deploy. What it can't do is keep testing your app after it's live. HelpMeTest picks up where Replit's testing ends: