From Document Store to Agentic AI Platform
Agentic AI platforms for document management are systems that turn static repositories of files into governed, context-rich environments where autonomous AI agents can safely reason over, summarize, and act on enterprise knowledge at scale. That is the shift iManage set out at its ConnectLive conference, recasting its document and knowledge-management stack from a place where work product is stored to a platform that actively surfaces, connects, and brokers that knowledge for AI. CEO Neil Araujo has compared the scale of this overhaul to the move to cloud, underlining how central it is to the company’s future. With adoption across 79% of the Am Law 100 and 83% of a defined Top Global 100, any change to iManage’s core platform quickly becomes a de facto template for document management modernization across knowledge-intensive enterprises.
Inside the iManage ‘Context Fabric’
At the heart of the redesign is an AI context fabric, an inference layer that sits above governed firm data and connects documents, relationships, and ongoing activity. iManage describes this fabric as transforming accumulated content into a “living, governed foundation” that both people and AI agents can query and build on, while staying permission-aware. Rather than bulk-exporting data into external tools, the fabric is meant to give models controlled, transparent access to matter context, work product, and institutional knowledge. Governance and security are built into the platform, not bolted on, so that policies, ethical walls, and client-specific constraints follow any agent that touches the corpus. In effect, the context fabric aims to become the connective tissue of enterprise knowledge management, where AI agents receive the right context at the right time without breaching confidentiality or compliance rules.
The DMS as Governance Plane for AI Agents
ConnectLive’s roadmap positioned the document management system as the primary governance plane for agentic AI. New controls span ethical-wall-aware agents, client- and matter-level AI restrictions through Security Policy Manager, and expanded monitoring of agent activity via Threat Manager. According to Legal IT Insider, these capabilities “collectively describe a posture in which the DMS becomes the governance plane for whatever AI systems a firm chooses to plug into it.” This re-centers the DMS not as a back-office utility but as the front line where AI access is authorized, audited, and constrained. iManage’s Model Context Protocol (MCP) Server reinforces that role by letting AI agents and large language models search and surface permission-aware knowledge without bypassing existing security models, which is essential as firms move from AI experiments to operational deployments.
Claude Integration and the Agentic Stack
The platform overhaul is tightly linked to the wider shift toward agentic AI platforms across the legal and enterprise ecosystem. iManage’s integration with Anthropic’s Claude, including its placement in Anthropic’s partner ecosystem and Claude store, gives organizations a governed path to use a leading AI assistant on their internal corpus without custom connectors or uncontrolled exports. The MCP Server for Insight+ extends this by letting Claude and other agents discover and apply context while staying within existing governance boundaries. At the same time, iManage’s multi-region search, native OCR, and reworked user experience are designed to reduce friction so human users and agents operate against the same, clean knowledge base. This aligns with a broader move from point tools to connected stacks where orchestration, governance, and context fabric layers decide how autonomous AI agents interact with enterprise content.
An Inflection Point Comparable to Cloud Migration
The scale of iManage’s pivot has strategic implications beyond any single product release. With 83% of the top Global 100 firms and 40% of the Fortune 100 on its platform, the decision to re-architect around an AI context fabric effectively nudges the market toward an agent-centric model of document management modernization. Araujo’s comparison to the shift to cloud is not only rhetorical: as AI agents begin to automate complex workflows, the question becomes where control sits. Recent moves from NetDocuments, Aderant, Harvey, and Anthropic suggest a contest to own the governed surface through which enterprises operationalize AI. Firms must now decide whether the governance layer belongs in the DMS, an AI orchestration platform, or elsewhere. Whichever option they choose will shape how safely and effectively they can scale agentic AI across their knowledge work.