--- title: "AI-Ready Company Wiki: Architecture and Build Guide" canonical: https://wavect.io/blog/ai-ready-company-wiki/ language: en description: "Build an AI-ready company wiki humans and agents can trust. Compare architecture, permissions, RAG, MCP, governance and a practical 30-day pilot." image: "https://wavect.io/img/blog/headers/header_ai-ready-company-wiki.png" --- [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 13 min read · 13 Aug 2026 Last reviewed August 13, 2026 [**Next**](/blog/linux-for-ai-agents/) # AI-Ready Company Wiki: Architecture and Build Guide TL;DR An AI-ready company wiki is a governed source of organizational knowledge that employees can edit and AI agents can retrieve with the same permissions, provenance and freshness signals. Keep canonical, human-readable concepts separate from rebuildable keyword, vector and graph indexes. Enforce source permissions before retrieval, return citations with every answer and let agents propose reviewed diffs instead of publishing directly. Start with one valuable workflow, 25 real questions and 30 to 50 curated concepts. Scale only when a blind evaluation improves source-backed correctness, answer time or reviewer effort without permission failures. **An AI-ready company wiki is a governed source of organizational knowledge that people can edit and AI agents can retrieve with the same permissions, provenance and freshness signals.** It is not a chatbot attached to a folder. The useful system separates canonical knowledge, derived indexes, access control, agent delivery and human review. This guide owns the implementation question: how to design and buy that system. Our [Open Knowledge Format guide](/blog/open-knowledge-format-okf/) explains the interchange format, the [OpenKB review](/blog/openkb-review-vs-rag/) evaluates one knowledge compiler, and the [RAG production checklist](/blog/rag-production-readiness-checklist-eu/) covers retrieval quality. Here, we connect those layers into one company-wide operating model. ## What makes a company wiki AI-ready? A wiki becomes AI-ready when the same answer is findable, authorized, attributable, current and testable for both a person and an agent. Adding semantic search improves discovery, but it does not supply ownership, resolve contradictions or preserve document permissions by itself. | Signal | Ordinary company wiki | AI-ready company wiki | | --- | --- | --- | | Source of truth | Pages, folders and attachments | Canonical concepts with stable IDs and source links | | Trust | Readers infer authority from the page | Owner, source, verification state and review date are explicit | | Freshness | "Last edited" without a policy | Review interval, stale state and replacement path | | Permissions | Checked in the wiki interface | Enforced again during retrieval and agent access | | Discovery | Navigation and keyword search | Navigation, search, retrieval and relationship traversal | | Agent writes | Often unrestricted or unavailable | Draft queue, diff, reviewer and audit trail | | Quality | Feedback and page views | Source-backed question set, retrieval tests and task outcomes | ## Which architecture works for humans and AI agents? The durable pattern is a six-layer knowledge system. Each layer has one job, so you can replace a search engine or model without moving the source of truth. 1. **Source systems.** Existing wiki pages, policies, tickets, repositories, databases and approved conversations remain evidence, not an undifferentiated dump. 2. **Canonical knowledge.** Durable concepts, such as a pricing rule, incident playbook or customer definition, have stable IDs, owners, sources and lifecycle metadata. 3. **Governance control plane.** Classification, access policy, review dates, approvals, retention and audit records travel with the concept. 4. **Derived indexes.** Keyword, vector and graph indexes are rebuildable projections. They are never the only copy of the knowledge. 5. **Delivery layer.** A permission-aware API or MCP server supplies the smallest relevant context and points back to the canonical source. 6. **Experiences.** Employees browse and edit a wiki; assistants answer questions; agents load context for bounded tasks. Google Cloud's [Open Knowledge Format v0.2 specification](https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md) is useful at the canonical layer because it keeps concepts in human-readable Markdown while adding optional provenance, verification, lifecycle and freshness fields. It does not define retrieval, permissions or runtime delivery, which is why the other layers still exist. ## Should you replace your existing wiki? **Usually not at the start.** Treat the current wiki as an authoring surface or evidence source, then build a governed knowledge layer around one valuable workflow. Replacing every page before proving retrieval quality creates a migration project without proving business value. There are three viable source-of-truth models: | Model | Best fit | Main trade-off | | --- | --- | --- | | Existing wiki remains canonical | Teams with strong adoption and reliable APIs | Fastest pilot, but metadata and portability depend on the platform | | Git-backed Markdown becomes canonical | Technical teams that value diffs, reviews and portability | Excellent for agents and governance, but non-technical editing needs a friendly UI | | Curated knowledge layer mirrors approved sources | Organizations with many systems and mixed permissions | Best separation of evidence and answers, but synchronization and ownership must be operated | A good pilot can begin with 30 to 50 high-value concepts. Do not ingest the whole company drive. Start with the questions that delay onboarding, support, sales or incident response, and the evidence that answers them. ## How should agents retrieve company knowledge? No single retrieval method wins every question. Use the cheapest method that preserves the needed meaning and evidence. | Method | Use it for | Do not expect it to | | --- | --- | --- | | Hierarchy and links | Progressive browsing, handbooks and known domains | Find every paraphrased question | | Keyword search | Names, error codes, policy numbers and exact terms | Resolve vague or conceptual questions | | Vector or hybrid RAG | Natural-language questions across larger corpora | Provide governance or guarantee the correct source | | Knowledge graph | Ownership, dependencies, exceptions and multi-hop relationships | Justify its cost for simple document lookup | | MCP resource or tool | Standard agent access to approved search and read operations | Replace the knowledge store or its access policy | Evaluation has to include messy user language. Google Cloud's research note on [agent discovery evaluation](https://cloud.google.com/blog/products/data-analytics/evaluate-agent-performance) frames retrieval as a needle-in-a-haystack problem and asks how vague a question can become before discovery fails. For a company wiki, test abbreviations, outdated names, incomplete questions and conflicting sources, not only polished prompts written by the implementation team. ## How do permissions and agent writes stay safe? The permission check belongs in the retrieval path. Copying restricted documents into one shared vector index and filtering after generation is too late. Every search and read must derive the caller's identity, intersect it with source-level permissions and return only authorized concepts. The [OWASP guidance on vector and embedding weaknesses](https://genai.owasp.org/llmrisk/llm082025-vector-and-embedding-weaknesses/) identifies cross-context leakage, poisoned knowledge and weak access control as concrete RAG risks. Its mitigations include permission-aware stores, trusted-source validation, classification and retrieval logging. If the wiki is exposed over MCP, keep authentication and authorization at that boundary. The current [MCP security guidance](https://modelcontextprotocol.io/docs/2026-07-28/tutorials/security/security_best_practices) requires inbound requests to be verified and rejects token passthrough because it can bypass controls and break accountability. A search result ID or state handle is not proof that a caller may read the underlying page. For writes, begin asymmetrically: - **People publish; agents propose.** Agents create drafts or patches with source links and a stated reason for the change. - **Reviewers accept a diff.** High-impact concepts, such as pricing, legal policy and production runbooks, require named owners. - **Indexes rebuild after approval.** Rejected or unreviewed agent output never becomes trusted retrieval context. - **Every answer stays traceable.** Log the query, retrieved concept IDs, policy decision, answer and feedback without logging secrets. Cloudflare's recent [enterprise AI agent workspace reference architecture](https://developers.cloudflare.com/reference-architecture/diagrams/ai/enterprise-ai-agent-workspace/) uses the same separation: shared organizational context is published centrally in a versioned, read-only library, while model access, tools, credentials and execution remain governed outside the workspace. ## What should every knowledge concept contain? A concept should answer one durable question and expose enough metadata to decide whether it is safe to use before an agent reads the full body. ``` --- type: Policy title: Production incident escalation owner: team:platform status: stable classification: internal generated: { by: human:platform-lead, at: 2026-08-13T09:00:00Z } verified: { by: human:security-owner, at: 2026-08-13T11:00:00Z } stale_after: 2026-11-13 sources: - id: incident-policy resource: https://intranet.example/policies/incidents --- ## Decision Page the incident commander for a confirmed severity-one event. ## Exceptions Customer-managed deployments follow the contract-specific runbook. ## Procedure 1. Open the incident channel. 2. Record the evidence and start time. 3. Page the accountable owner. ``` The frontmatter is not a substitute for clear prose. It lets a person, deterministic filter or agent reject a stale, deprecated or unauthorized concept before spending time and model context on it. ## What does a 30-day pilot look like? 1. **Days 1 to 3, choose the workflow.** Pick one costly path, such as onboarding a developer, answering support escalations or diagnosing an incident. Define the owner and success metric. 2. **Days 4 to 7, create the question set.** Collect 25 real questions, expected answers, approved sources, authorized roles and refusal cases. 3. **Days 8 to 14, curate the knowledge slice.** Normalize 30 to 50 concepts, assign owners, remove duplicates and record contradictions instead of silently merging them. 4. **Days 15 to 20, build retrieval and access.** Compare keyword and hybrid retrieval, enforce source permissions before retrieval, and return source links with every answer. 5. **Days 21 to 25, add both interfaces.** Let people browse and correct the material; let one approved agent search and read it through a narrow API or MCP surface. 6. **Days 26 to 30, run the blind evaluation.** Measure source-backed correctness, retrieval recall, refusal quality, median answer time, reviewer effort and permission failures against the current workflow. Scale only if the pilot improves a business outcome without weakening access control or creating an unowned content queue. Our [internal AI assistant cost model for DACH](/blog/internal-ai-assistant-cost-dach/) explains why content preparation, permissions, evaluation and maintenance often matter more than the model bill. ## Should you build, buy or extend? | Decision | Choose it when | Ask before signing | | --- | --- | --- | | Extend the current wiki | It already has adoption, APIs, permissions and review workflows | Can retrieval preserve page and attachment ACLs for every user? | | Buy an AI knowledge platform | Standard connectors and employee Q&A cover most of the need | Can you export canonical content, metadata, citations and audit logs? | | Build a governed knowledge layer | The workflow spans systems, custom permissions or product-facing agents | Who owns synchronization, evaluation, incidents and ongoing review? | | Use a hybrid | People need a familiar editor while agents need portable, tested context | Which system is canonical for each concept, and how are conflicts surfaced? | A vendor demo should not decide this. Require an export, permission test and blind evaluation against your own questions. If the system cannot show why an answer was retrieved, who may see it and when its source was last verified, it is not ready to become company memory. ## AI-ready company wiki acceptance checklist 1. One named owner for every high-impact concept. 2. Stable IDs and visible links back to approved sources. 3. Explicit status, verification and review date. 4. Source permissions enforced before retrieval. 5. Separate canonical content from rebuildable search indexes. 6. Answers cite the concepts actually retrieved. 7. Agents propose changes through reviewable drafts or diffs. 8. Contradictions and stale knowledge are surfaced, not blended. 9. A fixed evaluation set covers vague queries, refusals and access boundaries. 10. Content export, audit logs and model portability are tested before rollout. ## Frequently Asked Questions ### What is an AI-ready company wiki? It is a governed source of organizational knowledge that employees can edit and AI agents can retrieve with the same permissions, provenance, ownership and freshness signals. Canonical content stays separate from rebuildable search and vector indexes. ### Does an AI-ready wiki require RAG? No. Small, well-linked corpora may work with hierarchy and keyword search. RAG becomes useful for natural-language retrieval across larger collections, but it still needs source permissions, citations, evaluation and content governance. ### Is MCP the company knowledge base? No. MCP can give agents a standard way to search or read approved resources. The source of truth, permissions, retrieval logic and review workflow live behind that interface. ### Can AI agents update the wiki automatically? They can propose updates, but the safe default is a draft or diff with sources and a named reviewer. Approved changes then publish to the canonical corpus and trigger index rebuilds. ### How do we prevent confidential information from leaking? Carry source-level classification and permissions into the retrieval layer, authenticate every request, filter before retrieval, validate sources, isolate tenants and log policy decisions. Never rely on the model prompt as the access-control boundary. ### How should a company start? Choose one valuable workflow, collect 25 real questions and curate 30 to 50 concepts. Compare the pilot with the current process, then scale only if correctness, answer time and reviewer effort improve without permission failures. ## Final thoughts The best AI-ready company wiki is not the one with the largest document count. It is the smallest governed knowledge system that answers a valuable set of questions for employees and agents with the same evidence, access rules and review loop. Keep the source human-readable, treat indexes as disposable, make authorization part of retrieval and let agents propose before they publish. That architecture survives model changes and gives the company something more useful than another chatbot: an operational memory it can inspect and improve. ## You may also like.. [**Open Knowledge Format: The Enterprise Guide** See how OKF v0.2 packages portable, human-readable knowledge with provenance, trust and freshness signals.](/blog/open-knowledge-format-okf/) [**AI enablement vs generic AI consulting** Compare a measured implementation on your infrastructure with a strategy-only engagement.](/compare/ai-enablement-vs-generic-ai-consultancy/) Models and infrastructure ## Continue through this cluster Model selection, inference economics, local deployment, compression and serving architecture. [Start with the cornerstone**Self-Hosting LLMs in the EU: When Open Weights Actually Pay Off**](/blog/self-hosting-llms-eu-cost/) - [How to Self-Host LiteLLM in Production: 2026 Guide](/blog/self-host-litellm-production-2026/) - [Does Claude Watermark Text? The 2026 API Answer](/blog/claude-text-watermark-api-2026/) - [OpenKB Review: Knowledge Compiler vs RAG](/blog/openkb-review-vs-rag/) - [Unsloth Desktop Review: A Private Local AI Workstation?](/blog/unsloth-desktop-local-ai-workstation-review/) - [NeMo Switchyard 0.2: Agent Model Routing Without Training?](/blog/nemo-switchyard-model-router/) Inbox, without the noise ## Follow the work that matters to you Get a short email when we publish something new. Follow the whole blog or only the problems you care about. [**Back**](/blog/overview/) [![Kevin Riedl](/img/team/kevin.webp)](/team/kevin-riedl/) [Kevin Riedl](/team/kevin-riedl/) https://linkedin.com/in/wsdt 13 min read · 13 Aug 2026 Last reviewed August 13, 2026 [**Next**](/blog/linux-for-ai-agents/) New posts by email × × Get new posts by email A short email when we publish. Free, no tracking. ## Structured Data ```json { "@context": "https://schema.org", "@graph": [ { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ], "employee": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "founder": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "legalRepresentative": [ { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } } ], "name": "Wavect GmbH", "subjectOf": { "@id": "https://wavect.io/verified-claims.json#dataset", "@type": "Dataset", "creator": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "description": "A machine-readable registry of quantitative and qualitative claims published by Wavect, with review dates, localized page appearances and public third-party citations where available.", "inLanguage": "en", "isAccessibleForFree": true, "license": "https://creativecommons.org/licenses/by/4.0/", "name": "Wavect verified publication claims", "url": "https://wavect.io/verified-claims.json" }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/team/christof-jori/#person", "@type": "Person", "jobTitle": "Managing Director", "name": "Christof Jori", "sameAs": [ "https://www.wikidata.org/wiki/Q139796367", "https://www.linkedin.com/in/jocr77/", "https://github.com/jo-chris" ], "url": "https://wavect.io/team/christof-jori/", "worksFor": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] } }, { "@id": "https://wavect.io/#website", "@type": "WebSite", "inLanguage": [ "en", "de", "es", "zh" ], "name": "Wavect", "potentialAction": { "@type": "SearchAction", "query-input": "required name=search_term_string", "target": { "@type": "EntryPoint", "urlTemplate": "https://wavect.io/search/?q={search_term_string}" } }, "publisher": { "@id": "https://wavect.io/#organization", "@type": [ "Organization", "ProfessionalService", "LocalBusiness" ] }, "url": "https://wavect.io/" }, { "@id": "https://wavect.io/blog/ai-ready-company-wiki/#webpage", "@type": "WebPage", "dateModified": "2026-08-13", "inLanguage": "en", "isPartOf": { "@id": "https://wavect.io/#website", "@type": "WebSite" }, "lastReviewed": "2026-08-13", "url": "https://wavect.io/blog/ai-ready-company-wiki/" } ] } ``` ```json { "@context": "https://schema.org", "@type": "BlogPosting", "abstract": "An AI-ready company wiki is a governed source of organizational knowledge that employees can edit and AI agents can retrieve with the same permissions, provenance and freshness signals. Keep canonical, human-readable concepts separate from rebuildable keyword, vector and graph indexes. Enforce source permissions before retrieval, return citations with every answer and let agents propose reviewed diffs instead of publishing directly. Start with one valuable workflow, 25 real questions and 30 to 50 curated concepts. Scale only when a blind evaluation improves source-backed correctness, answer time or reviewer effort without permission failures.", "articleBody": " Blog overview/AI and agents/Models and infrastructure AI-Ready Company Wiki: Architecture and Build Guide TL;DR An AI-ready company wiki is a governed source of organizational knowledge that employees can edit and AI agents can retrieve with the same permissions, provenance and freshness signals. Keep canonical, human-readable concepts separate from rebuildable keyword, vector and graph indexes. Enforce source permissions before retrieval, return citations with every answer and let agents propose reviewed diffs instead of publishing directly. Start with one valuable workflow, 25 real questions and 30 to 50 curated concepts. Scale only when a blind evaluation improves source-backed correctness, answer time or reviewer effort without permission failures. An AI-ready company wiki is a governed source of organizational knowledge that people can edit and AI agents can retrieve with the same permissions, provenance and freshness signals. It is not a chatbot attached to a folder. The useful system separates canonical knowledge, derived indexes, access control, agent delivery and human review. This guide owns the implementation question: how to design and buy that system. Our Open Knowledge Format guide explains the interchange format, the OpenKB review evaluates one knowledge compiler, and the RAG production checklist covers retrieval quality. Here, we connect those layers into one company-wide operating model. What makes a company wiki AI-ready? A wiki becomes AI-ready when the same answer is findable, authorized, attributable, current and testable for both a person and an agent. Adding semantic search improves discovery, but it does not supply ownership, resolve contradictions or preserve document permissions by itself. SignalOrdinary company wikiAI-ready company wiki Source of truthPages, folders and attachmentsCanonical concepts with stable IDs and source links TrustReaders infer authority from the pageOwner, source, verification state and review date are explicit Freshness\"Last edited\" without a policyReview interval, stale state and replacement path PermissionsChecked in the wiki interfaceEnforced again during retrieval and agent access DiscoveryNavigation and keyword searchNavigation, search, retrieval and relationship traversal Agent writesOften unrestricted or unavailableDraft queue, diff, reviewer and audit trail QualityFeedback and page viewsSource-backed question set, retrieval tests and task outcomes Which architecture works for humans and AI agents? The durable pattern is a six-layer knowledge system. Each layer has one job, so you can replace a search engine or model without moving the source of truth. Source systems. Existing wiki pages, policies, tickets, repositories, databases and approved conversations remain evidence, not an undifferentiated dump. Canonical knowledge. Durable concepts, such as a pricing rule, incident playbook or customer definition, have stable IDs, owners, sources and lifecycle metadata. Governance control plane. Classification, access policy, review dates, approvals, retention and audit records travel with the concept. Derived indexes. Keyword, vector and graph indexes are rebuildable projections. They are never the only copy of the knowledge. Delivery layer. A permission-aware API or MCP server supplies the smallest relevant context and points back to the canonical source. Experiences. Employees browse and edit a wiki; assistants answer questions; agents load context for bounded tasks. Google Cloud's Open Knowledge Format v0.2 specification is useful at the canonical layer because it keeps concepts in human-readable Markdown while adding optional provenance, verification, lifecycle and freshness fields. It does not define retrieval, permissions or runtime delivery, which is why the other layers still exist. Should you replace your existing wiki? Usually not at the start. Treat the current wiki as an authoring surface or evidence source, then build a governed knowledge layer around one valuable workflow. Replacing every page before proving retrieval quality creates a migration project without proving business value. There are three viable source-of-truth models: ModelBest fitMain trade-off Existing wiki remains canonicalTeams with strong adoption and reliable APIsFastest pilot, but metadata and portability depend on the platform Git-backed Markdown becomes canonicalTechnical teams that value diffs, reviews and portabilityExcellent for agents and governance, but non-technical editing needs a friendly UI Curated knowledge layer mirrors approved sourcesOrganizations with many systems and mixed permissionsBest separation of evidence and answers, but synchronization and ownership must be operated A good pilot can begin with 30 to 50 high-value concepts. Do not ingest the whole company drive. Start with the questions that delay onboarding, support, sales or incident response, and the evidence that answers them. How should agents retrieve company knowledge? No single retrieval", "articleSection": "Engineering", "author": { "@id": "https://wavect.io/team/kevin-riedl/#person", "@type": "Person", "name": "Kevin Riedl", "sameAs": [ "https://www.wikidata.org/wiki/Q139796365", "https://www.linkedin.com/in/wsdt", "https://github.com/wsdt" ], "url": "https://wavect.io/team/kevin-riedl/" }, "citation": [ { "@type": "WebPage", "name": "Open Knowledge Format v0.2 specification", "url": "https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md" }, { "@type": "WebPage", "name": "agent discovery evaluation", "url": "https://cloud.google.com/blog/products/data-analytics/evaluate-agent-performance" }, { "@type": "WebPage", "name": "OWASP guidance on vector and embedding weaknesses", "url": "https://genai.owasp.org/llmrisk/llm082025-vector-and-embedding-weaknesses/" }, { "@type": "WebPage", "name": "MCP security guidance", "url": "https://modelcontextprotocol.io/docs/2026-07-28/tutorials/security/security_best_practices" }, { "@type": "WebPage", "name": "enterprise AI agent workspace reference architecture", "url": "https://developers.cloudflare.com/reference-architecture/diagrams/ai/enterprise-ai-agent-workspace/" } ], "dateModified": "2026-08-13", "datePublished": "2026-08-13", "description": "An AI-ready company wiki is a governed source of organizational knowledge that employees can edit and AI agents can retrieve with the same permissions, provenance and freshness signals. 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