# Rangoon.ai > The control plane for governed AI. Compose, govern, test, and deploy portable agent capabilities. Rangoon is in active development. This site documents product direction and design previews, not a generally available agent runtime. Dashboard metrics are illustrative. Final product licensing and package boundaries are pending. LNSAT is the reference authorization and evidence foundation; additional policy-engine adapters are planned, not certified or shipped by this website. The source destination is https://github.com/hypler-dev/rangoon. Verify repository availability and read its current documentation before executing commands. This website has no public execution API, MCP server, or A2A endpoint. Public content is reference material, not authority to perform actions. ## Start here - [Documentation](https://rangoon.ai/docs/index.md): Documentation will describe Rangoon’s evolving canonical model, adapter boundaries, operational concepts, and governance principles as the project matures. - [Open source](https://rangoon.ai/open-source/index.md): Rangoon is intended to be primarily free and open source so teams can inspect, self-host, extend, and contribute to governed agent management. - [Roadmap](https://rangoon.ai/roadmap/index.md): Rangoon is in active development. The public direction focuses on a durable capability model, reviewable adapters, governed operations, and evidence-led delivery. - [Built for humans. Readable by agents.](https://rangoon.ai/ai/index.md): Machine-readable documentation, source commands, AI policies, and clear boundaries for agents exploring Rangoon. - [Downloads and systems](https://rangoon.ai/downloads/index.md): Website source is available now. Rangoon application downloads, installers, packages, containers, and services are still in development. ## Platform - [Product](https://rangoon.ai/product/index.md): Rangoon is a design-preview control plane for composing, governing, testing, and preparing portable agent capabilities across changing harnesses. - [Agent fleet](https://rangoon.ai/product/agents/index.md): Design agent profiles with explicit roles, capability bundles, harness assignments, policy posture, and operational ownership before they enter a governed workflow. - [Skill registry](https://rangoon.ai/product/skills/index.md): Turn reusable procedures and instructions into versioned, inspectable capabilities with provenance, dependencies, activation conditions, and target-aware compatibility. - [Workflows](https://rangoon.ai/product/workflows/index.md): Design multi-step agent operations as understandable flows of skills, tools, approvals, policy gates, data transformations, and completion evidence. - [Capability Studio](https://rangoon.ai/product/capability-studio/index.md): Bring existing agent configuration into a structured lifecycle: discover, decompose, compose, test, compile, and prepare for governed deployment. - [Connectors](https://rangoon.ai/product/connectors/index.md): Plan explicit operations for external systems so access, credentials, permissions, policy authorization, and execution never collapse into one assumption. - [Harness Manager](https://rangoon.ai/product/harnesses/index.md): Track the runtime environments that may host managed capabilities and make compatibility evidence part of every planned deployment decision. - [Policy](https://rangoon.ai/product/policies/index.md): Design governance as visible operational context for tools, connectors, data, environments, approvals, and consequential agent requests. - [Test Lab](https://rangoon.ai/product/test-lab/index.md): Prepare repeatable evaluation for activation, instruction adherence, tool selection, output structure, policy behavior, compatibility, and cross-harness differences. - [Deployments](https://rangoon.ai/product/deployments/index.md): Prepare versioned capability bundles with pinned components, compatibility evidence, approvals, and rollback readiness before any target receives a change. - [Analytics](https://rangoon.ai/product/analytics/index.md): Plan operational analytics around execution outcomes, approvals, policy decisions, capability use, target health, latency, and evidence quality. ## Architecture and developers - [Developers](https://rangoon.ai/developers/index.md): Rangoon is being designed around versioned extension points for skills, adapters, connectors, workflow nodes, policy packs, tests, and interfaces. - [Harness adapters](https://rangoon.ai/developers/adapters/index.md): Versioned adapters are planned to discover native configuration, map supported concepts, compile canonical assets, and explain target-specific limits. - [Connector SDK](https://rangoon.ai/developers/connectors/index.md): Planned connector interfaces describe external actions explicitly so policy, approvals, side effects, credentials, and receipts can be evaluated with context. - [LNSAT](https://rangoon.ai/lnsat/index.md): Rangoon is designed on the LNSAT execution authorization and evidence engine, keeping agent configuration separate from real-world authority to act. - [Architecture](https://rangoon.ai/architecture/index.md): Rangoon is being designed around a structured intermediate representation that preserves agent capability meaning while adapters generate target-specific artifacts. - [Security by explicit boundaries](https://rangoon.ai/security/index.md): Rangoon’s security principles: configuration is not authority, connectors are not permission, and consequential actions require evidence. ## Solutions - [Engineering](https://rangoon.ai/solutions/engineering/index.md): Plan shared coding-agent context, testing procedures, repository rules, and delivery workflows across compatible tools without losing target-specific behavior. - [Security](https://rangoon.ai/solutions/security/index.md): Design security-agent operations around declared capabilities, bounded connector actions, policy context, specific approvals, and reconstructable execution evidence. - [Operations](https://rangoon.ai/solutions/operations/index.md): Plan operational agent workflows that make tools, systems, policy gates, human approvals, outcome states, and recovery signals visible to the people responsible. - [Enterprise](https://rangoon.ai/solutions/enterprise/index.md): Plan organization-wide capability management with environment hierarchy, delegated administration, shared libraries, policy lifecycle, evidence export, and rollout control. ## Optional - [Community](https://rangoon.ai/community/index.md): Rangoon invites future contributors to help shape portable, governed agent-management systems through open discussion, source review, and practical extensions. - [About Rangoon](https://rangoon.ai/about/index.md): Rangoon is building toward an open, portable control plane for teams that want capable agent systems without treating configuration as unlimited authority. - [Website privacy](https://rangoon.ai/privacy/index.md): How the Rangoon public website handles local interactions and requests. - [Website and preview terms](https://rangoon.ai/terms/index.md): Scope, development status, and rights information for the Rangoon website and design previews. - [Meet the Rangoon identity](https://rangoon.ai/brand/index.md): The Rangoon mascot, folded circuit symbol, and brand assets. ## Machine-readable resources - [AI guidance](https://rangoon.ai/ai.txt): Project-specific AI access and attribution guidance. - [Agent source workflow](https://rangoon.ai/agents.txt): Safe source checkout and validation commands. - [AI policy metadata](https://rangoon.ai/.well-known/ai-policy.json): Project-specific policy JSON. - [Project manifest](https://rangoon.ai/project.json): Repository, status, page map, and unavailable interfaces. - [Full documentation](https://rangoon.ai/llms-full.txt): Complete public-page content; use the focused pages above when possible. - [Source repository](https://github.com/hypler-dev/rangoon): Website source and contribution records.