AI Agents
Each agent has a clear scope, shared context contract, and composable workflow behavior.
Maps application boundaries, services, and dependencies before test planning begins.
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Workflow example: Reads service catalog and traces, then builds a runtime-aware dependency graph.
Ingests technical context and keeps test reasoning grounded in current system behavior.
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Workflow example: Indexes architecture docs and logs, then publishes refreshed context bundles.
Builds prioritized test plans aligned to release risk and change impact.
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Workflow example: Creates plan variants and selects the most cost-efficient coverage strategy.
Runs browser workflows with resilient selectors and state-aware retries.
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Workflow example: Executes user journeys and captures evidence when behavior diverges from expectation.
Validates API contracts, business logic, and cross-service interactions.
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Workflow example: Generates requests, verifies responses, and flags payload schema deviations.
Checks persistence integrity, event payload quality, and downstream consistency.
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Workflow example: Compares expected data contracts against actual storage and event stream outputs.
Clusters failures and identifies probable root causes across multi-layer signals.
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Workflow example: Correlates runtime evidence and pinpoints service, data, or policy bottlenecks.
Builds actionable tasks that developers can apply quickly inside delivery workflows.
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Workflow example: Converts diagnostics into engineering actions with impact and ownership mapping.
Tracks quality trends, release readiness, and operational test intelligence.
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Workflow example: Aggregates quality data and highlights quality drift before release windows.
Repairs brittle tests and updates execution patterns based on validated changes.
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Workflow example: Validates potential fixes in a sandbox before applying approved updates.