Srednoff Playwright Agent Visual QA

Playwright Agent Visual QA

Structure Playwright QA around real user states, reproducible screenshots and an explicit coverage ledger.

A successful page load hides broken dialogs, mobile overflow, stale baselines, and failed requests. Reviewers cannot reconstruct which build, browser, data, and interaction produced a reported defect.

Who This Is For

Frontend QA leads and product engineers who need a reproducible browser evidence pack for a release candidate, not an agent's unsupported impression of a screenshot.

What You Get

• A standalone, domain-specific agent workflow with decision rules and stop conditions.
• Evidence requirements, structured report guidance and missing-data handling.
• Synthetic worked examples and supporting reference material.
• Optional offline evidence-precheck source where the licensed delivery allows execution; not an automatic artifact scanner.

Expected Deliverables

• run_manifest: revision, URL, environment, timestamp, browser/OS, viewport, locale, fixture seed, and capture settings
• coverage_ledger: journey/state/browser/viewport, PASS/FAIL/NOT_TESTED, assertion IDs, evidence references, and skip reason
• findings: ID, severity, confidence, user impact, steps, expected/actual, console/network context, recommendation, and validation
• visual_review: baseline/candidate references, compatibility notes, masks, reviewer decision, and unresolved differences
• handoff: regression-test candidates, affected files if changed, rerun commands, rollback scope, owners, and remaining risks

How The Workflow Proceeds

1. Create a run manifest binding revision, URL, timestamp, browser version, OS, locale, viewport, and test-data seed; record any mismatch before running a journey.
2. Expand each journey into initial, loading, success, empty, validation-error, and recovery states where applicable; identify which states require human visual review.
3. Select existing project tests first, then use bounded Playwright CLI or MCP sessions with isolated contexts and semantic locators for uncovered interactions.
4. Run behavior assertions before capturing evidence: check navigation, persisted state, focus transitions, and relevant API outcomes, not just element visibility.

The remaining workflow covers validation, failure recovery and an evidence-linked handoff.

Bring These Inputs

• Candidate URL or local start command, immutable build revision, allowed hosts, and confirmation of the test environment.
• Critical user journeys with expected outcomes, representative data, authentication roles, and permitted side effects.
• Required desktop/mobile viewport and browser matrix, including whether physical-device testing is required.
• Evidence storage location and rules for redacting screenshots, traces, network data, and account information.

Scope And Compatibility

This is a workflow toolkit used by your connected AI agent, not a hosted service, installed API client, autonomous executor or security/compliance certification. You supply the model, authorized tools and project evidence. It does not provide access to advertising, analytics, billing or production accounts. No business outcome or token saving is guaranteed.

Agensi delivery is agent-only. Do not save or export the proprietary source. If local helper execution is unavailable, the agent follows the review workflow and marks the helper NOT_EXECUTED; no local-script execution is promised for this channel.

Validation Transparency

Packaged offline supplied-evidence checks: PASS. Retained synthetic text-response cases: 8/8; scope PASS_SCOPED. These tests are not real-client task trials or marketplace-runtime certification.

Commercial Use And Support

Use project-specific outputs in personal, business and client work under the included Commercial License 1.0. Historical MIT and third-party rights remain unchanged; mandatory marketplace terms prevail. No proprietary source redistribution is included. Publisher: Ivan Srednoff / Srednoff.Agency.

FAQ

Will it make changes automatically? No. Analysis does not authorize spending, publication, deployment or account mutations.

Are examples client results? No. Examples are explicitly synthetic DEMO DATA.

Does it require Srednoff OS? No. The workflow is standalone; external tools and subscriptions are not included.

Is the launch price a proven optimum? No. It is a scoped introductory price informed by comparable offers, not sales forecasts or fake discounts.