Pengo Workbench

Stop waiting for AI to magically get better.

Find the structure. Use it to observe, control, and report what AI systems actually do.

The Workbench helps you inspect unclear asks, visible failure surfaces, recurring structural Patterns, and diagnostic Lenses before jumping to blame, scores, or governance claims.

Current Index

Issues: 115
AI-Adjacent Issues: 16
Patterns: 33
Lenses: 20
Categories: 14

Workbench Areas

Start Here

Understand why AI workflow failures often come from structural conditions.
Check Input

Narrow the ask before blaming the model.
Gap Explorer

Paste Detected Gaps and explore related Patterns and useful Lenses.
Issues

Start with the visible failure surface.
AI-Adjacent Issues

Browse problems users may experience as AI failures, but which may involve tools, apps, permissions, runtime behavior, configuration, or integrations.
Patterns

Find the recurring structural form behind the problem.
Lenses

Apply diagnostic views to inspect Patterns from different angles.
Ontology Changelog

Review public changes to Issues, Patterns, Lenses, and Categories.
Search

Find Workbench references by symptoms, gap terms, names, or codes.
Examples

See the structural language applied to real AI-work situations.
Field Notes

Read informal observations, implementation notes, and structural breakdowns.
Articles

Read long-form explanations of Workbench concepts and boundaries.
Videos

Watch walkthroughs, explainers, and demonstrations.

What This Is

The Workbench is not a chatbot, risk score, or governance platform. It is a structured set of protocols, indexes, examples, field notes, and machine-readable references for inspecting where AI work and systems lose structure.