Time for Workspace Intelligence. Understand your workspace before AI starts making decisions.
Workspai.com
@workspai.com
Open-Source Workspace Intelligence for Software Systems. For developers, CI, IDEs, and AI agents. Built by @Chistiq.com. One workspace. One truth. Humans and AI aligned.
Plan a release-readiness, dependency-security, or test-coverage outcome with an explicit scope, baseline, safety constraints, required checks, evidence paths, and governed verdict. www.workspai.com/features/ver...
Verified Engineering Goals — Workspai
Plan a release-readiness, dependency-security, or test-coverage outcome with an explicit scope, baseline, safety constraints, required checks, evidence paths, and governed.
workspai.com
We just opened a contributor-friendly Workspai issue: Build a deterministic relevance fixture for natural-language Workspace Graph search. A practical task for anyone interested in graphs, local retrieval, developer tools, or AI context infrastructure. github.com/chistiq/work...
[Feature]: Add deterministic natural-language graph search relevance fixtures · Issue #14 · chistiq/workspai
Problem statement Workspai lets developers and AI agents search a Workspace Knowledge Graph without loading the complete graph artifact. The search is deterministic, local, and bounded, but we do n...
github.com
A knowledge graph isn't the source of truth. Neither is your IDE. Neither is your LLM. So what is?
AI doesn't have a coding problem. It has a context problem. Until an AI understands your entire workspace, every answer is just an educated guess.
The next generation of frameworks won't just scaffold code. They'll scaffold Workspace Intelligence. $ npm install -g workspai $ workspai create Start every project with a canonical workspace model.
Model first. Graph second. Without a canonical model, a graph is just another interpretation. www.workspai.dev/learn/worksp...
Workspace Graph — Workspai.dev
A graph of the software system, not only a code graph. Learn the evidence, architecture, contracts, and operational decisions behind this part of the…
workspai.dev
Software quality is a workspace problem. When every module shares the same workspace intelligence, quality stops depending on tribal knowledge. 52 audited modules. 99.3 average quality score. 99.8% test coverage. One workspace. One truth. www.getrapidkit.com/audits/modules
The quality of your AI starts with the quality of your workspace graph.
More files don't create more understanding. Defining the system boundary does. dev.to/chistiq/your...
Your AI Read Every File. It Still Missed the System.
Imagine Alex has an existing Next.js project here: /home/alex/code/storefront-web Enter...
dev.to
Your codebase isn't just files. It's a graph of evidence. Query it. Explain it. Export it. One workspace. One truth. www.workspai.dev/commands/wor...
workspai workspace graph — Workspai.dev
Query or export the evidence-backed workspace graph derived from the canonical model. Review its canonical invocation, governed artifacts, contracts, output…
workspai.dev
Build once. Understand everywhere. One workspace. One truth. #WorkspaceIntelligence www.workspai.dev/learn/what-i...
What Is Workspace Intelligence? — Workspai.dev
A precise definition of Workspace Intelligence as the evidence-backed understanding layer for software systems. Learn the evidence, architecture, contracts,…
workspai.dev
In 2026, the hardest distributed system isn't Kubernetes. It's getting five AI agents to agree on the same fact. #AIAgents #AgenticAI #WorkspaceIntelligence
Context drift happens when developers, CI, IDEs, and agents operate from different versions of the system. npx workspai workspace context --for-agent --json --write Refresh scoped context, safe commands, evidence, blockers, and freshness before the next AI decision. workspai.dev/docs/context...
Context vs Understanding — Workspai.dev
Why AI context is not the same as software system understanding. Learn the evidence, architecture, contracts, and operational decisions behind this part of…
workspai.dev
We keep investing in better models. Better agents. Better orchestration. But I suspect the next breakthrough won't come from execution. It'll come from giving every AI the same understanding of code, docs, APIs, architecture, and operations.
AI agents do not need more confidence. They need to know what is verified, observed, stale, or unknown. npx workspai workspace verify --strict --json Evidence before certainty. #WorkspaceIntelligence workspai.dev/docs/evidence
Evidence — Workspai.dev
How Workspace Intelligence decides which facts can be trusted. Learn the evidence, architecture, contracts, and operational decisions behind this part of the…
workspai.dev
Generate AI instructions from workspace evidence: npx workspai workspace agent-sync --write --refresh-context --preset enterprise AGENTS.md, skills, Cursor, Copilot, Claude, and an auditable customization inventory from one evidence layer. www.workspai.dev/commands/wor... #WorkspaceIntelligence
workspai workspace agent-sync — Workspai.dev
Project current workspace context into governed agent instructions, reports, skills, and integration files. Review its canonical invocation, governed…
workspai.dev
Repositories are excellent containers for versioned source. They are poor proxies for the full software system. www.workspai.dev/essays/a-rep...
A Repository Is Not a Software System — Workspai.dev
Repository-centric AI can read implementation text while still missing ownership, policy, consequence, and operational meaning.
workspai.dev
Every AI-generated PR still asks one question: Can we trust this change? That's what I've been thinking about. www.linkedin.com/pulse/every-...
Every Pull Request Creates Invisible Work
AI made writing code cheaper. It did not make understanding changes cheaper.
linkedin.com
The next AI race won't be about models. It will be about who owns the system model.
A repo maps files. A workspace model maps the operating system around them. npx workspai workspace model --json --write Projects, runtimes, commands, policies, contracts, dependency signals, and evidence in one model for humans, CI, IDEs, and agents. #WorkspaceIntelligence
Most valuable software already exists. Keep the project where it is and add the workspace layer around it: npx workspai adopt ../existing-project --json Adopt/import lets existing software enter Workspace Intelligence without becoming a framework migration project. #WorkspaceIntelligence
AI can read code. Understanding the software system is a different problem. Workspai.dev is live: the knowledge portal for Open-Source Workspace Intelligence for Software Systems. Learn the concepts, architecture, evidence, and agent grounding: workspai.dev/docs #WorkspaceIntelligence
Introducing Workspai. The RapidKit CLI is now Workspai. Open-source Workspace Intelligence for Software Systems. One workspace. One truth. Humans and AI aligned. Install: npm install -g workspai For short npx workflows, use the separate alias package: npx wspai --help
The next AI coding workflow is not only: "generate code" It is: detect blocker apply smallest fix verify result refresh evidence That repair loop belongs in the workspace, not in the user's memory.
“Context” has become too big a word. RAG retrieves. Memory remembers. Skills teach. MCP connects. Agents act. Repository intelligence explains code. Workspace intelligence should explain what the software system believes, what is stale, and what an action will affect. medium.com/workspai/wev...
We’ve Been Calling Everything “Context.” Maybe That’s the Real Problem.
Skills, RAG, memory, repository intelligence, MCP, and workspace intelligence are not the same layer. Treating them as one is why AI…
medium.com
Every coding assistant should start from the same grounding. Run: npx rapidkit workspace context --for-agent --json --write Outputs: .rapidkit/reports/workspace-context-agent.json AGENTS.md .rapidkit/AGENT-GROUNDING.md Less guessing before touching code.
