
How We Handle LLM Drift: A Ten-Phase Task Lifecycle
The task lifecycle we run at Imagineers to capture chain of thought and beat session compaction on long-running agent tasks.
Better prompts plateau. Context engineering breaks through by assembling the right information into every LLM test generation call.
Running Playwright tests with an LLM gives you per-run analytics. But how do you track patterns across runs and build a full test analytics platform with LLMs?
16 test failures. 2 root causes. $0.05. Your CI already knows what broke. This workflow makes it tell you.
Structured ARIA labels and component docs give AI coding agents everything they need to generate deterministic Playwright tests -- no vision models required.
Why code generation beats visual agents for scalable test automation, and how to strategically use computer vision for edge cases where the DOM is inaccessible.
A 10-part series translating Google’s Context Engineering whitepaper into hands-on QA practice. Each post builds a working tool.
Frontier models ship faster than teams can adapt. This series is how we keep up—and help you do the same.
Foundations
Posts 1–3 · What is Context Engineering and why should QA teams care?
Building QA Memory Systems
Posts 4–7 · Hands-on builds: from test failure memory to self-healing tests
Scaling & Operating
Posts 8–10 · Making it production-ready: multi-agent, monitoring, massive scale
Work with us
Short engagements to upgrade test automation with LLMs.
Start a conversation →Krishnanand B
Founding Imagineer
11+ years building test automation systems. From Selenium scripts to AI-native platforms.
Work with us →