From Document Repositories to Agentic AI Foundations
Enterprise document and knowledge management is undergoing a structural reset as organisations move from AI experimentation to AI operationalisation. At the centre of this shift is a new expectation: document systems must no longer simply store files, they must actively fuel autonomous AI agents with governed knowledge. iManage’s platform overhaul, unveiled at its ConnectLive conference, encapsulates this change. With 83% of the Top Global 100 firms, 79% of the Am Law 100 and 40% of the Fortune 100 relying on its platform, the company’s move signals a broader knowledge management platform evolution. Document management agentic AI is becoming a core operational model, not an add-on feature. The strategic question for CIOs and knowledge leaders is no longer which large language model to choose, but how to architect enterprise AI so that permission-aware knowledge, context and expertise are reliably available to autonomous agents at scale.
Inside the ‘Context Fabric’: A New Enterprise AI Architecture
iManage’s answer to this challenge is its “context fabric” — a new enterprise AI architecture that sits above governed firm data. Rather than treating documents as static artefacts, the context fabric models content, relationships and real-time user and agent activity as a living knowledge graph. This layer understands and reasons over matters, work product and institutional knowledge, continuously enriched by what professionals and agents are doing right now. Crucially, governance and security are native to this fabric, not bolted on, enabling context fabric systems to give agentic AI workflows permission-aware access to precisely the information they are entitled to use. This design reflects a deliberate repositioning of the document management system: from a passive repository to an active broker of context, decisions and workflows for autonomous agents operating across legal, financial and professional services environments.
Governance, MCP and the Safe Activation of Knowledge
The move to document management agentic AI raises immediate concerns about risk, ethics and control. iManage’s platform response is to embed governance deep into its context fabric. New AI-specific controls govern how AI is applied across clients and matters, while enhanced monitoring and reporting track AI agent activity across the knowledge estate. A key component is the iManage Model Context Protocol (MCP) Server, which provides a secure bridge between external AI tools and the governed corpus, allowing models and agents to query firm knowledge without bulk exports. Formal integration into Anthropic’s ecosystem gives Claude a governed route into this context layer. Together, these capabilities aim to let organisations safely “activate” their institutional knowledge — making it contextual, auditable and policy-aware by design — while retaining the fine-grained security that knowledge-intensive industries require.
Rewiring Workflows for Agentic Knowledge Work
Beyond architecture, the platform overhaul is reshaping everyday workflows. iManage positions this as a shift from systems that merely store knowledge to ones that actively surface, connect and orchestrate it for human and AI collaborators. The first phase includes redesigned user experiences, multi-region search, native OCR and enhancements to tools like Ask iManage, such as playbook analysis that helps teams consistently apply precedent guidance. These features are not just productivity tweaks; they are scaffolding for agentic AI workflows in which agents draft, summarise, route and classify work under human supervision. For knowledge workers, the document management system becomes a co-pilot environment where context-aware suggestions, risk checks and next-best actions flow from the context fabric. For organisations, it is a step toward treating enterprise AI architecture as a core operational layer rather than an experimental sidecar.