TestMu AI Blogs

Context engineering for AI agents: what to include and exclude, the four failure modes, core strategies, advanced techniques, and how to measure it.

13 LLM evaluation tools compared for 2026: what each one measures, where it fits in the lifecycle, and how to pick one for your architecture and privacy needs.

How LLM hallucination detection works: groundedness scoring, semantic entropy, judge models and fine-tuned detectors compared, with where each one fails.

Kane CLI drives iOS Simulators and Android Emulators from plain English objectives. See the setup command, the device flags, and the evidence each run seals.

MCP vs Agent Skills compared on what you author, where it runs, and how each one fails, with a measured breakdown of 71 skills and when QA teams need both.

We measured self-healing against manual test maintenance across 9 real cloud runs, then priced both arms on shared inputs to show where the break-even sits.

Agent-native is a claim, not a feature. Seven checks you can run during a trial to test whether a vendor's tool works with no human at the screen.

How to run E2E tests in pull requests without stalling code review: set a time budget, control flakiness, gate on real devices, and show reviewers the cause.

Measured defect rates for AI-generated code range from 8% to 68% depending on what each study counted. What actually breaks, and which test gate catches it.

Claude Code is Anthropic's agentic coding tool for the terminal. Learn how a session works, where it runs, and how skills, MCP, hooks and subagents extend it.

Codex skills put your team's conventions in a SKILL.md the agent loads on demand. How to write one, make it trigger reliably, and verify Codex followed it.

Make Claude Code, Cursor, Copilot and Codex test their own work: the file each agent reads, a stop hook that runs your tests, and a real browser check.

Agent-native CI explained: the diagnosis loop, the workflow_run architecture, guardrails against a hidden regression, and what still needs a human to decide.

Agentic AI architecture splits planner, generator, and evaluator roles. Why a generator cannot grade its own output, and how to wire an independent evaluator.

Agentic automation gives software a goal instead of a script. See how it works, where it fits, and what our cloud runs showed actually breaks when a UI changes.

An agent-run test suite costs money on every execution because inference is metered, unlike a scripted suite that is expensive to write but nearly free to run afterward. Four measured browser checks against TestMu AI playgrounds took between 29.8 and 47.0 seconds each, a mean of 37.4 seconds per flow.

AGENTS.md works best as a testing contract: the exact commands an agent must run, what done means, how to handle a failing suite, and what it must never touch.

Claude Code hooks fire on lifecycle events, not on model judgement. Events, matcher syntax, exit codes, and how to run a real browser check from a Stop hook.

Claude Code plugins bundle skills, agents, hooks, and MCP servers into one installable unit. Learn plugin.json, marketplaces, install scopes, and verification.

Claude Code subagents explained: context isolation, the complete frontmatter reference, where definitions live, and how to verify what one actually shipped.