Agents I have designed and shipped for QE workflows — one public, the rest running inside organisation. The internal agents are described at a high level only; source and prompts stay with the client.
Standalone LLM-based code review tool. Paste a GitHub or GitLab URL and the agent scans the diff for style, naming, POM, and framework-convention violations. Pluggable LLM backends (Claude, Gemini, OpenAI, Ollama), structured JSON output, GitHub Actions wiring.
Reads business requirements from Confluence, generates compliance-format acceptance criteria and pushes them into the matching Jira ticket. Cuts BA-to-QE handoff time.
Analyses Jira acceptance criteria, generates Gherkin feature files, and detects existing step definitions to prevent duplication. Reduced feature authoring cycle from 3 days to 18 hours.
Inspects DOM for new UI flows. Reuses existing Page Object components and locators when possible, otherwise generates framework-conformant new ones and auto-maps feature files to glue and step definitions.
Analyses commit diffs for impacted Salesforce modules
(@opp, @lead, @pardot), triggers a targeted subset for
fast PR feedback, then runs full @regression as second-pass safety.
Hardened version of the public Code Review Agent, scoped to internal banking microservice repos. Runs against GitLab merge-request diffs, enforces POM and framework standards, posts inline comments. Adopted across 4 active QE repos.
Across the agents above: 78% regression cycle reduction (5 days → 11 hrs), 25% drop in production defects, 60% improvement in MTTD for critical issues, and SDET onboarding time cut from 3 weeks to 10 days.