From Storing Documents to Powering Agentic AI Platforms
At ConnectLive, iManage used its flagship conference to reposition its core platform around agentic AI workflows, in a move its CEO has compared in scale to the industry’s shift to cloud. Rather than simply adding AI features on top of a traditional document management system, the company is reframing the DMS as a governed knowledge backbone for autonomous agents. This shift reflects a broader document management evolution: enterprises are moving from AI experimentation to AI operationalisation and need foundations that can safely fuel AI, not just repositories that store files. With a footprint across a large share of leading law firms and major corporations, iManage’s re-architecture is more than a product refresh; it is a signal of where the wider enterprise knowledge management stack is heading. For AI leaders, the message is clear: document systems must now be designed as agentic AI platforms from the ground up.
Inside the Context Fabric Architecture
At the heart of iManage’s overhaul is its “context fabric” architecture, an inference layer that sits above governed firm data and activity. Instead of treating each document as an isolated object, the fabric models content, relationships and real-time work patterns across matters, clients and teams. This allows AI tools and agents to draw on a rich, permission-aware context fabric architecture that understands how knowledge is actually used, not just where it is stored. The fabric is continuously enriched by what people and agents are doing right now, turning static repositories into a living, governed knowledge mesh. Governance and security policies are native to this layer rather than bolted on, which means agentic AI platforms can reason over institutional knowledge while still respecting granular permissions. For enterprises, this architectural shift is what enables AI agents to operate safely inside sensitive knowledge domains.
From Document-Centric to Context-Centric Enterprise Knowledge Management
The context fabric marks a deeper transition from document-centric systems to context-centric platforms. Traditional DMS tools were optimised for filing, searching and versioning documents. In an agentic era, however, the unit of value is no longer just the file but the surrounding context: which matter it belongs to, who worked on it, what precedents it relates to, and how outcomes were achieved. By elevating context to a first-class concept, platforms like iManage give enterprises a way to govern not just content, but the way AI agents interpret and act on that content. This is critical for enterprise knowledge management in regulated, knowledge-intensive environments, where misapplied context can be as risky as misused data. The repositioned platform aims to actively surface, broker and connect relevant work product to AI agents, making governance an enabler of intelligent automation rather than a brake on innovation.
New Controls That Make Agentic Workflows Operational
Beyond architecture, iManage is rolling out features aimed squarely at operationalising agentic workflows at scale. New AI-specific controls let firms define how AI is applied across clients and matters, aligning automated behaviour with existing ethical walls and confidentiality rules. Enhanced monitoring and reporting on AI agent activity provide auditability, so teams can see what agents accessed, generated or changed within the platform. The expansion of the iManage Model Context Protocol (MCP) Server gives organisations a more robust way to connect external AI tools and agents to governed knowledge without bulk data exports, including through formal placement in Anthropic’s partner ecosystem and Claude store. Additional platform enhancements—such as multi-region search and native OCR—reinforce the foundation needed to make AI outputs both comprehensive and compliant. Together, these changes are less about flashy features and more about the practical plumbing required to run agentic workflows safely across the enterprise.