Engineering Pattern Note

UiPath

NYSE: PATH

Diagnostic and risk-anticipation systems at platform scale.

A pattern note describing work delivered by Bogdan Codreanu (Lazyleaf CTO) during his employment at UiPath.

Work delivered while employed at UiPath (NYSE: PATH) between July 2019 and February 2022. Published as engineering pattern reference. UiPath has not endorsed this publication.

UiPath operates one of the largest enterprise automation platforms. Bogdan Codreanu designed and built a profile-driven diagnostic system bundled with the main product, triaging runtime issues across desktop client, execution engine, activity packages, and server-side components. The system has been in production since 2020.

The diagnostic surface of an enterprise automation platform is enormous. The support team had expertise but lacked data. Tickets sat for days or weeks while engineers walked users through manual extraction steps.

The brief: capture environment data in a single pass, redact sensitive information by default, tailor collection profiles to the symptom, scale across the full surface, and operate within the main product.

Pattern tags

Engineering PatternSoftware EngineeringAIPlatform Scale

The approach

01

Parallel Sweep Architecture

What we built

Configurable collectors running in parallel over registry entries, event logs, processes, environment variables, log files, installed components, configuration files, and custom file globs.

02

Trigger Mechanisms

What we built

System triggers on uncaught exceptions or on demand. Preview layer with edit and redact capabilities before packaging as a .zip archive.

03

Profile-Based Collection

What we built

Profiles are serialisable JSON documents that define which collectors run, what data they capture, and how results are structured. This makes diagnostic runs reproducible and shareable across support teams.

What Lazyleaf carries forward

Diagnostic surface as a first-class design concern

Isolated collectors with strict contracts

Default-on redaction for sensitive data

Profile-based reproducibility

Operational handover as a design requirement

This pattern applies to

Platform diagnosticsProfile-driven data collectionAI pipeline monitoringCross-component triageSensitive-data redactionExtensible collector architecturesSupport-engineering tooling

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