Lack of AI Context

Organisations deploying multiple AI tools across their workflows faced a fundamental coherence problem: each AI system operated in isolation, with no shared context about what other systems knew, had decided, or were currently doing. This produced contradictory outputs, redundant work, and AI assistants that felt frustratingly amnesiac — unable to build on prior interactions or connect information across the tools in a user's daily workflow.

We implement Model Context Protocol (MCP) architectures that create a shared context layer connecting AI systems across an organisation's tooling stack. Rather than each AI operating as an isolated agent, MCP enables a connected network of models that share relevant context, coordinate actions, and maintain coherent state across sessions and systems. Implementation is designed to be secure and privacy-respecting, with granular controls over what context is shared between which systems.

Our MCP implementation includes a centralised context management layer that brokers information sharing between AI systems, integration connectors for major enterprise tools including CRM, project management, email, and document platforms, session persistence that maintains meaningful context across conversations and tool switches, privacy-aware context controls with role-based access, audit logging of context sharing events, and a developer toolkit for extending MCP connectivity to custom internal systems.

Clients who implemented MCP reported a qualitative shift in the usefulness of their AI tooling — assistants that previously felt limited and repetitive became genuinely helpful because they could reference context from across the user's work environment. Duplicate work and contradictory AI outputs dropped significantly. Knowledge workers reported spending less time providing context to AI tools and more time acting on the outputs they received.

We are developing multi-agent orchestration capabilities on top of MCP infrastructure, enabling complex multi-step workflows to be autonomously executed by coordinated AI agents with shared context and clear task handoff protocols. Cross-organisation context sharing standards are also being explored, which would allow context-aware AI collaboration across company boundaries while maintaining appropriate data sovereignty controls.