We’ve been quiet over the last three months, focusing on delivering major improvements to our platform. This update covers a lot — use the list below to jump to what matters most.

• Analytics Engine - faster, more granular, scalable, and supporting upcoming features

• Test Explorer - new columns, new API, new controls

• Error Explorer - making sense of Playwright errors

• Custom Metrics - report any custom metric to Currents

• Data Redaction for Trace Files

• Jira Integration Improvements

• MCP

• UI changes

New Analytics Engine

We’ve completed a major refactor of our analytics engine. Over 800 million rows were migrated to a new ClickHouse cluster — smoothly and without major incidents.

Results:

• Sub-100ms P90 latency;

• More detailed, real-time insights (see Test Explorer and Error Explorer)

This overhaul was essential to meet growing demand for fast, accurate, and granular data. The new infrastructure also unlocks several upcoming capabilities:

• AI-powered insights across Currents Dashboard, GitHub PRs, CLI, Slack, email, and reports

• Enhanced context for AI workflows — test history, health metrics, and trends

• Flexible, expressive querying for fully customizable reports

• Real-time health data for powering Currents Actions, MCP, and REST API integrations

Test Explorer Improvements

Test Explorer remains one of our most-used features, helping teams quickly identify unstable or flaky tests. With the recent release, it now goes beyond snapshots to reveal trends and behavioural changes over time. You can now easily see:

• Which tests recently started to flake or fail

• Whether your fixes actually reduced failures

• Failure Rate Change — shows how failure rates shifted between two periods (e.g., last 30 days vs. previous 30 days)

  Flakiness Rate Change — compares test instability over time

Read more in Test Explorer documentation.

In addition, we’ve introduced a new /tests HTTP REST API resource that allows you to fetch Test Explorer data programmatically.

Making Sense of Playwright Errors with Error Explorer

CI pipelines generate a flood of test results, and meaningful insights are often buried in noise. Error Explorer classifies and enriches Playwright errors, transforming raw messages into structured, searchable data.

For example consider exploring the top errors affecting your runs and seeing this:

Error: expect(locator).toBeVisible() failed

This message is too generic — it can be associated with different CSS selectors and doesn’t tell the full story, nor does it allow you to take action to actually remove the errors. Wouldn’t it be better to have a more precise, application-aware data? For example:

• What CSS selectors or components cause the most CI failures?

• Are login issues due to hidden or disabled buttons?

• How many failures are infrastructure-related vs. actual test issues?

When a test fails, the raw error message and stack trace are parsed by Currents’ Error Classification Engine. It enriches every captured test error with structured fields that turn unstructured text into searchable, tokenized data.

• Target (e.g. CSS selector, URL)

• Action (e.g. click, toBeVisible)

• Category (e.g. Assertion, Timeout)

This structured data enables exploration from multiple angles — similar to using GROUP BY in SQL.

Now, instead of guessing what hides behind Error: expect(locator).toBeVisible() failed you can see that:

• Most tests failed because [data-testid=fg-loader] was not visible

• [data-testid=table-tbody….] visibility is the second most-common failure reason

Explore Playwright Error Explorer documentation.

Custom Test Metrics in Currents

Playwright has evolved beyond testing — it’s now a platform for performance, accessibility, and coverage insights. Our new analytics engine lets you track custom numeric metrics tied to your tests, such as:

• Accessibility score

• Lighthouse web vitals

• Performance metrics

• Resource usage metrics

You can attach numeric metric values to a test (as an annotation) to track it in Currents. See Playwright Custom Test Metrics documentation.

Here’s an example of sending resource usage metrics to Currents:

// Add annotations with a custom metric
test
    .info()
    .annotations.push([
    {
      type: "currents:metric",
      description: JSON.stringify({
        name: "memory_usage",
        value: getMemoryUsage(),
        type: "float",
        unit: "mb",
      })
    }, {
      type: "currents:metric",
      description: JSON.stringify({
        name: "cpu_usage",
        value: getCPUUsage(),
        type: "float",
        unit: "%",
      }),
    }]);

These metrics are automatically processed and surfaced in Currents charts — complete with filtering, aggregation, and trend analysis.

Data Redaction for Trace Files

To strengthen privacy and compliance for our Enterprise Customers, we launched an automatic removal of secrets (tokens, passwords, API keys) from trace files. This feature is available for customers on Enterprise plan.

Benefits:

• Prevents accidental exposure of credentials

• Simplifies GDPR and SOC 2 compliance

• Reduces risk during debugging and collaboration

Read more about Data Redaction.

Improved Jira Integration

A couple of months ago we rolled out our native integration with Jira. The recent release includes a couple of improvements based on customer feedback:

• Create or link an existing Jira Issue when browsing test results

• Show previously linked Jira Issues

• Show Jira Issue details when browsing a test

• Add comment / failure details (with Markdown support) to previously linked issues

See the updated Jira Integration documentation.

Currents MCP Server 2.0

The new analytics platform enabled us to release MCP Server 2.0 released. See the Currents MCP Server documentation.

You can now ask your agent question about your testing suite:

• What were the top flaky tests in the last 30 days — analyze CI test history to identify and resolve flakiness

• What were the slowest specs this week — find the slowest files across recent CI executions

Moreover, you can use AI Agents + Currents MCP server to perform intelligent tasks

• Fix all my flaky tests — investigate patterns in your CI pipeline failures, create a plan, and suggest fixes

• Fix this test — pull the last CI test results and suggest fixes

UI Improvements

Beyond the major infrastructure and API updates, we’ve refreshed parts of the dashboard UI — addressing many of the annoyances reported by our users.

We’re deeply grateful for everyone who shares feedback, challenges, and ideas. Your input directly shapes Currents’ roadmap — and the next wave of features is already underway.