Workspai.dev
The official learning path for Workspace Intelligence.
Workspai.dev is the public knowledge portal for Workspace Intelligence: the evidence-backed architecture layer that helps humans, CI, IDEs, and AI agents understand the same software system.
Workspai turns repositories, projects, dependencies, rules, changes, and evidence into shared understanding for developers, CI, IDEs, and AI agents.
This site teaches the architecture. It is not the cloud app, not a dashboard, and not a repository upload surface.
Learning path
1. Concepts
Understand why Workspace Intelligence is different from RAG, memory, repository indexing, and chat.
2. Software System Understanding
Learn the structure, relationships, intent, evolution, trust, and reasoning layers an AI needs.
3. Mental Models
Reason about shared system models, evidence-backed context, workflow boundaries, and agent authority.
4. Architecture
Study the input layer, Workspace Intelligence core, and consumers.
5. Workspace Model
See how projects, runtimes, commands, policies, contracts, and evidence become one model.
6. Workspace Graph
Separate code graphs from a graph of the software system: structure, runtime, ownership, change, and evidence.
7. Evidence
Learn how reports, contracts, gates, freshness, and verification keep claims trustworthy.
8. Agent Grounding
Understand how AGENTS.md, context packs, skills, IDE files, and MCP access align AI tools.
9. Create and Adopt
Learn how existing projects enter through adopt/import and which supported kits can start new projects.
10. Verified Engineering Goals
Turn release, dependency-security, and coverage requests into durable definitions of done.
11. Verified AI Repair
Follow causal findings through one bounded transaction, verification, rollback, or a durable decision.
12. Agent Skills
See how runtime-aware Skills and canonical bootstrap keep model hosts aligned without duplicating authority.
13. Agent Entry and Bootstrap
Resolve any adopted project to its governing workspace and validate the bounded agent starting point.
14. Analysis, Readiness, and Verification
Separate findings, release preparation, and the final governed verdict.
15. Change Intelligence
Connect snapshots and diffs to blast radius, traces, and proof-backed explanations.
16. Release Governance
Understand contract gates, strict policy, the broader pipeline, and release authority.
17. Observation and Evaluation
Observe changes, record structured outcomes, and evaluate agent value without storing conversations.
18. Machine Integrations and Portability
Learn the MCP, export, sharing, hydration, archive, and restore boundaries.
19. Modules and Dependencies
Operate runtime-aware modules and dependency repair with checkpoint and verification boundaries.
20. Capability Coverage
Audit the enforced map from important capabilities to learning guides, commands, and contracts.
21. Contract Catalog
Inspect every versioned contract, field, artifact, producer, and consumer boundary.
22. Command Catalog
Follow every CLI command to its canonical execution, artifacts, and governing contracts.
Extended CLI domains
CLI Platform Operations
Discover live commands, frameworks, compatibility, configuration, and setup.
Workspace Operations
Resolve registry identity, connections, synchronization, policy, and foundation state.
Project Lifecycle
Operate one resolved project through its detected runtime adapter.
Governed State and Recovery
Inspect snapshots and checkpoints before restore, rollback, merge, or uninstall.
Infrastructure Operations
Plan and operate infrastructure without confusing execution with evidence.
Product Factory
Inspect private product manifests and generation plans through the CLI boundary.
AI Capabilities
Use recommendations and embeddings while keeping models outside authority boundaries.
Mirror Operations
Synchronize compatibility mirrors while preserving canonical source ownership.
Autopilot Release
Understand the explicit authorization and evidence boundary for release automation.
Publications
Essays
Category-shaping positions on repositories, AI engineering, and shared software-system truth.
Research
Evergreen requirements and evaluation models for evidence-backed software system understanding.
What this portal should help you answer
- What is Workspace Intelligence?
- What is the difference between a repository graph and a workspace graph?
- Which facts are verified, observed, inferred, stale, or unknown?
- How does a project enter the workspace: native, official, or existing?
- What context should an AI agent receive before it acts?
- Which commands create model, context, impact, verification, and release evidence?
- Which public claims are allowed by the shipped contracts?
Boundaries
Workspai.dev stays focused on public education: concepts, architecture, mental models, contracts, commands, guides, essays, research, RFCs, glossary, and release notes.
Interactive product experiences, hosted workspace operations, live repository analysis, account workflows, and commercial surfaces belong on workspai.com.
Chistiq company and portfolio pages belong on chistiq.com. RapidKit product pages belong on getrapidkit.com.