An open future for governed AI. Built in the open
Rangoon.ai

Architecture

One canonical model.
Many target outputs.

Rangoon is being designed around a structured intermediate representation that preserves agent capability meaning while adapters generate target-specific artifacts.

Product direction · In active development

01

Model the capability

A canonical representation can include identity, activation, instructions, inputs, outputs, tools, dependencies, resources, permissions, policies, compatibility, provenance, and tests.

Targets are outputs

Harness-specific files become compilation targets rather than the permanent underlying source of truth.

02

Preserve provenance

Imported assets can retain repository, file, revision, lines, detection method, confidence, user modifications, ancestry, generated targets, and deployment history.

No unexplained blobs

A capability should retain the evidence needed to understand where it came from.

03

Keep authority external to configuration

The architecture separates composition and management from LNSAT evaluation for consequential execution.

Design in progress

This architecture describes planned product direction rather than a stable public contract.

The future is open

More capability.
Greater possibilities.

Let’s build an AI ecosystem worth trusting.

Rangoon, the smiling orange crab mascot
Product previewConcept interface · sample data · active development

Explore the design. Actual interfaces and feature availability may evolve.

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