# Rangoon site map > Browse every public, indexable Rangoon page. The XML sitemap and project manifest use this same public-page registry. Canonical: https://rangoon.ai/sitemap/ ## Start here - [Rangoon.ai — The control plane for governed AI](https://rangoon.ai/index.md): A governed AI control plane for platform teams, government technology programs and development firms. Manage capabilities, policy, approvals, compatibility and execution evidence. - [Documentation](https://rangoon.ai/docs/index.md): Documentation explains Rangoon’s evolving capability model, authority boundaries, adapter contracts, operational evidence, and target-specific behavior. - [Open source](https://rangoon.ai/open-source/index.md): Rangoon is designed as a free and open-source foundation for organizations to inspect, self-host, adapt, and contribute across capability, adapter, and governance layers. - [Roadmap](https://rangoon.ai/roadmap/index.md): Launch scope covers a durable capability model, reviewable adapters, governed operations, extension boundaries, and evidence-led delivery. - [AI documentation and public data access](https://rangoon.ai/ai/index.md): AI-ready documentation, open public data, feeds, and explicit permission for AI ingestion and training. - [Downloads & systems](https://rangoon.ai/downloads/index.md): Deployment architecture for workstations, containers, servers, and bounded mobile roles. Review target responsibilities and release evidence; downloads open at launch. - [Marketplace preview](https://rangoon.ai/marketplace/index.md): A launch-preview catalog for skills, connectors, authority adapters, and enterprise extenders. Listings describe review requirements; none are installable or purchasable. - [Compatibility & ecosystem](https://rangoon.ai/compatibility/index.md): Explore agent runtimes, model providers, corporate software, data platforms and identity connections in Rangoon’s launch architecture. - [Rangoon site map](https://rangoon.ai/sitemap/index.md): A browsable index of Rangoon public pages, documentation, product material, and AI ecosystem reporting. ## Platform - [Product](https://rangoon.ai/product/index.md): Rangoon provides a control plane for composing, reviewing, testing, and preparing portable agent capabilities across changing harnesses and execution-authority systems. - [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 into inspectable, versioned capabilities with a declared purpose, inputs, dependencies, activation conditions, and target-aware compatibility. - [Workflows](https://rangoon.ai/product/workflows/index.md): Model multi-step agent operations as readable graphs of inputs, decisions, tools, approvals, authority checks, outcomes, and 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): Define explicit external-system operations so a connector’s inputs, credentials, side effects, policy context, and receipts can be reviewed as separate facts. - [Harness Manager](https://rangoon.ai/product/harnesses/index.md): Track runtime environments as evidence-bearing targets, then adapt capabilities deliberately instead of assuming that similar-looking agent tools behave the same way. - [Policy](https://rangoon.ai/product/policies/index.md): Make policy inputs visible where capabilities and operations are assembled, while keeping execution-time authority distinct from a policy result or runtime configuration. - [Test Lab](https://rangoon.ai/product/test-lab/index.md): Plan repeatable evaluation for capability activation, behavior, tool selection, output structure, policy handling, compatibility, and target-specific failure modes. - [Deployments](https://rangoon.ai/product/deployments/index.md): Prepare exact, reviewable capability bundles with target evidence, dependencies, approvals, activation intent, and a recovery path before any environment changes. - [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 defines reviewable contracts for skills, adapters, connectors, workflow nodes, authority integrations, tests, and interfaces before they reach a workspace. - [Harness adapters](https://rangoon.ai/developers/adapters/index.md): Build versioned boundaries that discover native configuration, normalize supported concepts, compile target artifacts, and explain exactly where a harness changes the capability. - [Connector contracts](https://rangoon.ai/developers/connectors/index.md): Plan connector contracts around explicit operations, data and side-effect metadata, credential needs, policy context, authority evaluation, and receipt behavior. - [LNSAT](https://rangoon.ai/lnsat/index.md): Rangoon uses LNSAT as its reference execution-authorization and evidence engine, separating capability management from a decision to perform a specific consequential action. - [Architecture](https://rangoon.ai/architecture/index.md): Rangoon uses a canonical capability model, versioned target adapters, and an authority boundary that keeps configuration, deployment, and consequential execution from becoming the same operation. - [Standards and interoperability](https://rangoon.ai/standards/index.md): How Rangoon separates agent protocols, identity, policy evaluation, execution authority and operational evidence. Assess integration requirements against versioned release evidence. - [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): Turn repository rules, test procedures, delivery checks, and coding-agent context into reviewable assets that remain explicit about each target’s behavior and limits. - [Security](https://rangoon.ai/solutions/security/index.md): Define security-agent operations as scoped requests with declared capabilities, bounded connector actions, policy context, specific approvals, and 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. - [Domestic AI](https://rangoon.ai/solutions/domestic-ai/index.md): U.S.-first AI provider evaluation for technical teams, government and development firms. Review model selection, processing routes, permissions and execution evidence. - [Government](https://rangoon.ai/solutions/government/index.md): Rangoon provides a launch architecture for teams that need to assess agent capabilities, workload boundaries, approvals, and operational evidence against their own mission obligations. - [Development firms](https://rangoon.ai/solutions/development-firms/index.md): Rangoon provides a launch architecture for development firms that need to design, review, and hand off agent capabilities while keeping client boundaries, approvals, and versioned delivery clear. ## AI news and technical guides - [AI systems news and technical analysis](https://rangoon.ai/insights/index.md): AI ecosystem reporting and technical guides on agent systems, governance, infrastructure, and models. - [Agent systems insights](https://rangoon.ai/insights/topics/agents/index.md): Agent runtimes, handoffs, tools, and the boundaries that make them reviewable. - [Governance insights](https://rangoon.ai/insights/topics/governance/index.md): Policy, identity, authorization, approvals, and evidence for consequential work. - [Infrastructure insights](https://rangoon.ai/insights/topics/infrastructure/index.md): Model serving, sandboxes, tool services, and deployment boundaries. - [Models insights](https://rangoon.ai/insights/topics/models/index.md): Inference choices, local serving, compatibility, and operational tradeoffs. - [Editorial policy](https://rangoon.ai/insights/editorial/index.md): Rangoon Insights editorial policy for sourcing, dates, corrections, affiliation, and AI-assisted illustration. - [LNSAT: execution authority for AI agents](https://rangoon.ai/insights/lnsat-execution-authority-for-ai-agents/index.md): How Rangoon separates capability management from LNSAT execution authority, exact action packets, approvals, receipts, and state reconciliation. - [Claude Sonnet 5.5: model upgrades require release controls](https://rangoon.ai/insights/anthropic-claude-sonnet-5-5-release-engineering/index.md): Anthropic released Claude Sonnet 5.5 on September 28. Its arrival is a reminder to treat model upgrades as measurable, reversible application changes. - [Docker Cloud Sandboxes: moving agent workloads to managed compute](https://rangoon.ai/insights/docker-cloud-sandboxes-agent-isolation/index.md): Docker's Cloud Sandboxes extend its microVM model to managed compute. Teams now need to test portability, spend controls, and workflow handoffs. - [Canada and LawZero: planned investments and sovereignty claims](https://rangoon.ai/insights/canada-lawzero-sovereign-ai-investment/index.md): Canada and Germany announced planned investments in LawZero. The announcement makes it useful to separate local capacity, research control, and data handling. - [GSA OneGov: consumption-based AI procurement](https://rangoon.ai/insights/gsa-onegov-ai-procurement/index.md): GSA announced a new consumption-based OpenAI agreement for federal agencies. The practical challenge is governing usage, data handling, and evaluation. - [Gemini 3.8 Flash: model migration requires API controls](https://rangoon.ai/insights/google-gemini-3-8-flash-ga-migration/index.md): Google released Gemini 3.8 Flash on September 2. The GA milestone puts version choice, lifecycle tracking, and workload testing back in focus. - [MCP's stateless core changes tool-service deployment](https://rangoon.ai/insights/mcp-stateless-core-tool-infrastructure/index.md): MCP's July specification moved its core to stateless operation. The change makes ordinary web infrastructure a better fit for tool-service migration. - [DOE Genesis Mission: partner commitments and delivered capacity](https://rangoon.ai/insights/doe-genesis-partner-commitments/index.md): DOE reported more than $800 million in Genesis Mission partner commitments. The important distinction is between pledged capacity and delivered research capability. - [OpenShell and NemoClaw separate agent and runtime controls](https://rangoon.ai/insights/nvidia-openshell-nemoclaw-boundaries/index.md): NVIDIA's OpenShell and NemoClaw split the agent harness from its runtime controls. That separation gives security reviews clearer seams to inspect. - [A2A v1.0 defines a stable agent handoff protocol](https://rangoon.ai/insights/a2a-v1-stable-agent-handoffs/index.md): A2A v1.0 stabilized an open protocol for agent-to-agent work. The milestone makes interfaces and version migration more concrete for builders. - [NIST's 2025 AISI-to-CAISI change and the current CAISSI mission](https://rangoon.ai/insights/nist-aisi-caisi-rebrand-evaluation-mission/index.md): Review the 2025 AISI-to-CAISI transition, NIST’s current CAISSI mission, and what published evaluation methods mean for technical and procurement teams. - [Docker Model Runner and the local inference boundary](https://rangoon.ai/insights/docker-model-runner-local-inference/index.md): A practical guide to local model serving with Docker Model Runner, resource checks, API exposure, isolation, and evaluation discipline in practice. - [MCP tools: useful transport, separate authority](https://rangoon.ai/insights/mcp-tools-authorization-boundaries/index.md): Understand MCP hosts, clients, servers, tools, consent, and Rangoon’s exact-action authorization boundary before connecting agent systems safely. - [Ollama and vLLM: selecting an inference deployment](https://rangoon.ai/insights/ollama-vllm-inference-tradeoffs/index.md): Evaluate Ollama and vLLM by workload, hardware, API behavior, observability, and governance instead of treating local inference as one mode. - [OPA, Cedar, identity, and the exact action](https://rangoon.ai/insights/opa-cedar-identity-exact-authority/index.md): Compare policy engines and identity providers with execution authority, approvals, receipts, and the narrow scope of one consequential action. ## Project information - [Community](https://rangoon.ai/community/index.md): Rangoon invites future contributors to shape portable, governed agent-management systems through source review, practical extension work, and clear operational evidence. - [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. ## Discovery resources - [XML sitemap](https://rangoon.ai/sitemap.xml) - [Project manifest](https://rangoon.ai/project.json) - [Search index](https://rangoon.ai/search.json) - [AI documentation index](https://rangoon.ai/llms.txt) ## More information - [Documentation index](https://rangoon.ai/llms.txt) - [AI access and policies](https://rangoon.ai/ai/) - [Source repository](https://github.com/hypler-dev/rangoon)