Low maintenance automation

Agent task review hero-selection

The world's most advanced testing harness

The engineering underneath: independent, reliable, resilient.

  • Per-task LLM routing

    The best model for each testing job. You don't need to worry about upgrades, prompt rewrites, version pinning, or evals.

  • Reliable tools for your agent

    An MCP for the full testing loop. Broad tool coverage, progressive disclosure, idempotent writes, token-efficient, streaming with keepalive, OAuth.

  • Self-healing off a model, not a selector

    Every element is modeled as dozens of weighted attributes, so a page change re-identifies it by best overall match, falling back to locating it visually when attributes aren't enough.

  • Structural objectivity

    An LLM told to make a test pass will edit the DOM or weaken the assertion to get there. mabl runs your deployed app in a real browser, in a separate context, and checks it against assertions you defined separately.

  • Your intent, translated

    You describe what to test and set your guardrails; mabl's tuned metaprompts turn that into the structured instructions the agent runs on, scoped per agent with mabl's own guardrails winning on conflict. You get an effective agent without prompt-engineering it yourself.

  • Deterministic first, LLM when needed

    Cheap deterministic locators run first; the model engages only when they fail. More efficiency, lower cost, faster.

Rely on resilient tests

Multi-signal find model

Tests identify elements by inspecting their attributes and find history, falling back to a visual description when DOM signals are insufficient.

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Agentic auto-healing

Tests stay passing through unexpected selector changes and UI shifts while preserving original intent.

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Intelligent wait

Tests wait for a stable, ready state based on your app’s historical loading behavior, avoiding hard-coded wait steps and false failures.

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Fix real issues, fast

Conversational results analysis

Quickly understand test failures with analysis and recommendations, optimized for corrective action.

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Agentic test editing

Update tests using plain language and curated failure analyses as input context, from your agent or the UI.

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Agent instructions

Define situation-specific guidance for how mabl should update tests, so agentic modifications adhere to your best practices.

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Monitor and act on trends

Quality score reporting

Spot flaky and broken tests at a glance with composite quality scores that combine pass rate, stability, and reliability.

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Automatic failure categorization

Results are automatically labeled by failure category, sourced from a pre-defined set and customizable by your team.

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Suite health monitoring

Track pass rates, stability, and performance across your apps, environments, and teams.

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Spend 86% less time on suite maintenance