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Jarvis AIKnowledge Base

The Knowledge Base That
Powers Your Agents

Your organization's knowledge — policies, playbooks, docs, data — is only useful when your agents can reach it. Jarvis Knowledge Base surfaces content from 12 supported source types as 1 governed, agent-callable tool that integrates directly into your agentic workflows through the Agent Gateway.

Explore Capabilities
Jarvis Knowledge Base Overview
Core capabilities

Built for agents, not just chat

Standard knowledge bases surface answers. Jarvis Knowledge Base makes your enterprise content callable — a live tool that agents invoke, reason over, and act on inside governed workflows.

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Agent pipeline integration

Knowledge retrieval participates as a named step in multi-agent workflows — invoked by agents when grounded context is needed, with results flowing directly into the next action.

Multi-agent workflowsTask chainingGrounded responses
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MCP retrieval tool

Registers as an MCP server endpoint and is auto-discovered by the Agent Gateway. Any connected copilot — Claude, GitHub Copilot, VS Code, Cursor — can invoke it without additional wiring.

MCP serverAuto-discoveryZero wiring
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Multi-source intelligence

Ingests from cloud storage, wikis, databases, and file uploads. Every source is smart-chunked, vector-embedded, and metadata-tagged into a single governed knowledge layer.

Smart chunkingVector embeddingUnified index
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Document citations

Every retrieval response includes passage-level citations traceable to the source document — giving agents verifiable evidence and compliance teams a complete audit record.

Source citationsPassage-level refsAudit trail
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Embedded guardrails

PII filtering, topic detection, and message logging are built into the retrieval layer. RBAC scopes enforce which agents and teams can access which knowledge domains.

PII filteringTopic guardrailsRBAC scopes
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Automatic sync

Configurable sync schedules keep every knowledge domain current. Deletion safeguards prevent accidental mass-removal, and ACL updates propagate immediately without re-indexing.

Scheduled syncDeletion safeguardLive ACL updates
01 — MCP Retrieval Tool

Discoverable by any agent, without manual wiring

Once configured, Jarvis Knowledge Base registers itself as a named MCP tool in the Jarvis Registry. The Agent Gateway's auto-discovery sweep surfaces it alongside every other registered MCP server — so any copilot or agent connected to the gateway can invoke the knowledge base immediately.

No per-client configuration. No duplicated access management. The knowledge base tool is available wherever your agents are.

  • Registers as jarvis-knowledge-base MCP tool on setup
  • Agent Gateway auto-discovery on every startup sweep
  • One authenticated endpoint — every connected copilot
  • Tool schema and capabilities auto-generated from KB metadata
Knowledge Base MCP server connecting to any agent or copilot The Knowledge Base exposes a standard MCP server endpoint with auto-discovery and authentication. Claude, GitHub Copilot, Cursor, and custom agents all connect using the same protocol. S3 Confluence SharePoint SQL sources Knowledge Base MCP server auto-discovery authentication MCP protocol Claude GitHub Copilot Cursor Custom agent One endpoint — any copilot, standard protocol, built-in auth
02 — Agent Pipeline Integration

Knowledge retrieval as a workflow step, not a sidebar

Agents invoke the knowledge base inside a workflow the same way they invoke any other registered tool — as a step, with inputs and outputs that flow into the next action. One task can query the knowledge base multiple times, across different scopes, with each retrieval informing what happens next.

Every invocation is identity-bound, audit-logged, and traceable to a unique audit ID — so compliance teams have a complete record of what the agent retrieved and why it responded the way it did.

  • Multiple KB invocations within a single agent task
  • Retrieved context flows directly into downstream workflow steps
  • Document citations included in every retrieval response
  • Unique audit ID per invocation — traceable to source and user identity
Agent invoking the Knowledge Base as a step in a workflow A workflow task flows to an agent, which invokes the KB tool, gets cited passages back, and the result flows into a downstream governed action. Task workflow step Agent decides to query knowledge base as tool step invoke KB tool Knowledge Base retrieves passages + citations cited passages Action governed + logged 01 task arrives 02 agent decides 03 KB retrieves 04 action taken Multiple KB invocations per task — each retrieval informs the next step
03 — Access Control

Every KB query is identity-bound and domain-scoped

RBAC scopes and ACL policies from the Jarvis Governed AI layer determine which agents, teams, and users can access which knowledge domains — enforced at query time, not just at login. Legal, compliance, sales, and engineering each retrieve only from domains they're authorized for, even when sharing the same underlying index.

  • Per-domain RBAC scopes — one index, multiple access tiers
  • ACL policies inherited from the Governed AI layer — no duplicate config
  • Every retrieval logged with invoking identity, domain, and citation
  • Unauthorized queries blocked before retrieval, not after
Explore Governed AI Layer →
KB access policy · domain isolation

Knowledge domains

🔒legal-policieslegal-team only
🔒compliance-docscompliance / legal
🌐sales-playbookssales-all
🌐product-docsall-staff
Last retrieval decision

agent: compliance-checker
domain: legal-policies
result: authorized · logged

Connectors

Connect every knowledge source your organization already uses

Jarvis Knowledge Base ships with production-ready connectors for the platforms where enterprise knowledge lives. Each connection becomes a governed knowledge domain — agent-callable through a single endpoint.

Amazon S3

PDFs, DOCX, images, markdown. Cross-account IAM support.

Google Drive

Docs, Sheets, Slides — full Drive hierarchy with OAuth sync.

OneDrive

Personal and shared folders across your Microsoft 365 tenant.

SharePoint

Sites, document libraries, and team drives from Microsoft 365.

Confluence

Wikis, runbooks, and policies from Atlassian Cloud or Data Center.

Notion

Pages and databases from any Notion workspace, synced on schedule.

Web Crawler

Crawl and index public or internal web content on a configurable schedule.

Amazon OpenSearch

Existing search index as a hybrid vector + lexical knowledge source.

PostgreSQL

Tables, views, and document columns as a queryable knowledge domain.

MySQL

Relational tables and BLOB columns indexed as agent-callable knowledge.

Microsoft SQL Server

MSSQL tables — on-premises or Azure SQL, via secure credentials.

File Upload

PDF, DOCX, images up to 500 MB — OCR, chunking, and metadata extraction.

Ready to make your enterprise knowledge agent-ready?

See how Jarvis Knowledge Base registers as a governed MCP tool and integrates directly into your agentic workflows.

Available on AWS Marketplace

Jarvis Knowledge Base at a glance

The Jarvis Knowledge Base turns scattered enterprise content into a governed retrieval tool that agents can discover and call. It is built for agent pipelines rather than for a chat sidebar. These 6 rows summarise how it behaves.

Reference facts for the Jarvis Knowledge Base, covering sources, retrieval, citations, and access scope.
AspectWhat the knowledge base provides
Exposed asAn MCP retrieval tool, so any governed agent can discover and call it without bespoke wiring.
SourcesAmazon S3, Google Drive, SharePoint, OneDrive, Confluence, Notion, web crawl, OpenSearch, PostgreSQL, MySQL, SQL Server, and direct upload.
RetrievalA RAG pipeline combining search with generation, so answers are assembled from your content rather than model memory.
CitationsAnswers carry document citations, which is what makes an output checkable instead of merely plausible.
Access scopeEvery query is identity-bound and domain-scoped, so a user retrieves only from sources they may already read.
GuardrailsPII filtering, denied-topic detection, and message logging are embedded in the retrieval path.

Knowledge Base technical specifications

Retrieval is only safe when its permission model matches the source system, and only useful when answers point back at a record. These rows state both properties, plus the source coverage and the protocol involved.

Source coverage, retrieval, and access specifications for the Jarvis Knowledge Base.
SpecificationValue
Source types supported12 — Amazon S3, Google Drive, SharePoint, OneDrive, Confluence, Notion, web crawl, Amazon OpenSearch, PostgreSQL, MySQL, Microsoft SQL Server, direct upload
Upload formatsPDF, DOCX, and image files
Exposed as1 MCP tool, discoverable by any governed agent
Tool protocolModel Context Protocol (MCP) over JSON-RPC 2.0
Retrieval methodRAG pipeline combining search with generation
CitationsPassage-level references returned with every answer
Permission modelIdentity-bound and domain-scoped — 0 widening of existing source permissions
Access controlRBAC plus per-domain ACL entries
Caller identityAzure EntraID via OpenID Connect Core 1.0; SAML 2.0 supported
AuthorizationOAuth 2.0 (RFC 6749), Bearer usage per RFC 6750
Sync modelScheduled sync against the system of record, with deletion safeguards
Copies of source content0 permanent copies outside the customer account
GuardrailsPII filtering and denied-topic detection embedded in the retrieval path
Transport securityTLS 1.2 minimum, TLS 1.3 preferred (RFC 8446)
Token scopingResource Indicators for OAuth 2.0 (RFC 8707)
DelegationOAuth 2.0 Token Exchange (RFC 8693)
Public-client flowsPKCE (RFC 7636)
Identity federationOpenID Connect Core 1.0 and SAML 2.0
TelemetryOpenTelemetry OTLP — 1 span per retrieval call
Denial responsesHTTP 401 unauthenticated, HTTP 403 policy denial
Zero-trust referenceNIST SP 800-207 (2020)
AI governance referenceNIST AI RMF 1.0 (2023)
Marketplaces2 — AWS Marketplace and Azure Marketplace
Contract tiers3 standard tiers, USD 18,000–60,000
Runtimes3 managed Kubernetes services — Amazon EKS, Azure AKS, Google GKE
Customer rating5 out of 5 from 1 verified AWS Marketplace review

Watch the knowledge base overview

A 33-second overview of how enterprise content becomes a governed, agent-callable retrieval tool — the shortest way to see what the source and citation rows above mean in practice.

Jarvis Knowledge base overview · 33 sec

How to stand up a governed knowledge domain

Knowledge projects stall when they start by ingesting everything. The 5 steps below scope a first domain narrowly enough that answer quality can actually be judged.

  1. Pick one domain. Choose a single body of content with a clear owner — HR policy, a product manual, a runbook set.
  2. Connect the source. Attach the system that already holds it rather than copying documents into a new repository.
  3. Scope access to match. Bind the domain to the identities allowed to read the source, so retrieval cannot widen existing permissions.
  4. Check the citations. Run real questions and verify each answer cites the document a subject-matter expert would have pointed to.
  5. Expose it to agents. Publish the domain as an MCP tool so agent workflows can call it as a step rather than a separate lookup.

Retrieval and discovery

Two properties make a knowledge base usable by agents rather than only by people: automatic discoverability, and answers that carry citations back to the underlying record.

Enterprise knowledge base answering questions from internal company documents with citations
Answers are grounded in your own documents and cite the records they came from.
Auto-discovery exposing the Jarvis Knowledge Base to agents as a governed MCP tool
The knowledge base is discoverable by any governed agent, without manual wiring.

Frequently asked questions

Retrieval projects raise a consistent set of questions about permissions, accuracy, and how the content stays current.

Can retrieval expose documents a user should not see?

No. Every query is identity-bound and domain-scoped, so retrieval operates within the permissions the user already has on the underlying source.

That is the property that makes it safe to point a knowledge base at a system like SharePoint, where access varies significantly between teams.

How do we check an answer is right?

Answers carry document citations, so a reviewer can open the source record and confirm the claim rather than trusting the phrasing.

Citations are also what make retrieval useful in an agent pipeline: a later step can act on the source document, not just on the summary.

What happens when the source content changes?

Connected sources sync automatically, so the retrieval index tracks the system of record instead of drifting away from it.

This is the main practical argument against copying documents into a separate store: the copy is stale the moment someone edits the original.

How do agents find the knowledge base?

It is published as an MCP tool, so a governed agent discovers it through the registry and calls it as a workflow step with no bespoke integration.

Retrieval therefore becomes part of a pipeline rather than a sidebar a human has to consult separately.

How does an AI agent use a knowledge base?

It registers as an MCP tool in the Jarvis Registry. When an agent receives a task, it auto-discovers the KB through the Agent Gateway and invokes it at runtime — issuing targeted queries, evaluating retrieved evidence, and citing sources before generating a governed response.

That sequence matters: the agent is not handed a block of context up front, it decides what to look up while working, which is what keeps long workflows from drowning in irrelevant retrieved text.

How do I expose a knowledge base as an MCP tool?

Automatically. Once configured in Jarvis, it registers itself as an MCP server endpoint. Any connected copilot or agent — Claude, GitHub Copilot, VS Code, Cursor — can then discover and invoke it without additional configuration.

There is no per-client integration step, which is the difference between 1 configuration and 1 configuration per team.

What data sources does Jarvis Knowledge Base support?

Amazon S3, Google Drive, SharePoint, OneDrive, Confluence, Notion, Web Crawler, Amazon OpenSearch, PostgreSQL, MySQL, Microsoft SQL Server, and direct file upload of PDF, DOCX, and image files — 12 source types in total.

Structured and unstructured sources sit behind the same retrieval interface, so an agent asking a question does not need to know whether the answer came from a wiki page or a database table.

Related Jarvis resources

Retrieval is one capability inside a governed platform. These pages cover what it plugs into.

Standards and references

ASCENDING is an AWS Advanced Tier Services Partner with the AWS Generative AI Competency, and builds Jarvis as licensed software that runs inside your own cloud account. The sources below cover the protocol and storage services referenced above.