WORK-SELF announces the Maya Human Context MCP Server – the governed human context layer for enterprise AI agents.

LONDON, UNITED KINGDOM, June 9, 2026 /EINPresswire.com/ -- WORK-SELF today announced the next evolution of Maya Enterprise: the Maya Human Context MCP Server, a new architecture designed to give enterprise AI agents the one form of context they still lack - governed, permissioned, employee-specific human context.

The enterprise AI market is rapidly standardising around agents that can retrieve data, use tools, and coordinate work across systems. But most agents still understand only the task, the document, the workflow, or the enterprise knowledge base. They do not understand the human counterpart: how that person works, what they should review, when not to interrupt, what decisions must remain human-led, or how their role is changing under AI transformation.

Maya Enterprise addresses that gap. The Human Context MCP Server allows approved enterprise agents query Maya before initiating, escalating, or handing off work with an employee. Maya returns a permissioned Context Capsule and Work Contract: the minimum necessary task, role, organisational, culture, transition-readiness, and work-preference context required for that agent to collaborate effectively without oversharing sensitive personal data.

Built on WORK-SELF's proprietary identity graph, 80,000+ identity profiles, 2.2 billion scenario permutations, and a founder-held 17-claim US AI patent, Maya converts workforce identity and transition intelligence into governed runtime context for enterprise agent stacks. WORK-SELF believes Maya Enterprise is the first enterprise human-context layer purpose-built for agent-human orchestration.

"MCP gives AI agents a standard way to connect to enterprise tools and data. Maya gives those agents a governed way to understand the humans they work with," said Wolf Magdelinic, CEO and Co-Founder of WORK-SELF. "The next wave of enterprise AI will not be won by the company with the most agents. It will be won by the company that knows how to pair agents and people safely, intelligently, and at scale."

The category problem: agents know systems, not humans

AI agents are beginning to operate like digital coworkers: retrieving information, drafting deliverables, opening tickets, preparing customer responses, analysing data, and handing work to other agents. But as agents enter core workflows, the human friction becomes visible. Employees are forced to decide what to trust, what to reread, what to rewrite, when to prompt again, and when to stop the agent. Managers become the manual escalation layer. Organisations gain automation, but lose consistency, trust, and cognitive bandwidth.

Maya Enterprise is designed to resolve that failure point by turning individual workforce identity, work style, transition readiness, company context, and governance rules into operational context that approved agents can use at runtime.

What the Maya Human Context MCP Server provides

• Work Contract: Defines how a specific human should work with AI in a specific workflow: human-owned decisions, agent-owned tasks, escalation rules, review style, autonomy threshold, interruption cadence, and completion rule.
• Context Capsule: A permissioned packet of task, role, company, policy, culture, transition-readiness, and work-preference context shared with an approved agent for a specific purpose and duration.
• Review Map: A human-facing overlay that tells employees what to read, skim, ignore, decide, or delegate so they do not become the manual quality-control layer for AI.
• Autonomy Dial: A visible operating mode for each workflow: Manual, Copilot, Delegated, Autopilot, Training, or Focus.
• Digital Andon: A stop-or-escalate signal that humans or agents can trigger when risk, missing context, overload, or unclear decision rights appear.
• Learning Loop: Every accepted, edited, escalated, or rejected AI output improves the Work Contract and future orchestration quality.

The WORK-SELF differentiators

Maya Enterprise is not another enterprise search tool, chatbot, HR analytics layer, or generic agent builder. It combines human identity intelligence, enterprise transformation context, and MCP-native human-context infrastructure into one governed layer.

• 80,000+ identity profiles: Maya is grounded in a proprietary identity graph built from real professional transition interactions across industries, career stages, and transition types.
• 1,000+ Maya AI Agent consultations and 151 full Career Audits: Structured conversations and Career Audit methodology capture goals, values, energy patterns, work style, skill gaps, and reinvention intentions at depth.
• 2.2 billion scenario permutations: The Simulation Lab models permutations across industry, company type, department, AI-augmented workflow, work archetype, and self archetype, enabling Maya to evaluate context-specific transition and orchestration cases rather than generic prompts.
• Company Second Brain integration: Through Maya Staging, HRIS/API integrations, and MCP connectors, enterprises can attach target operating model documents, org charts, policies, role architectures, knowledge bases, CRM records, project data, learning materials, and workflow documentation.
• Cohort of Influence methodology: A structured knowledge layer incorporates reference organisations, methodologies, and bodies of work across workforce transformation, change management, skills intelligence, executive coaching, behavioural science, career identity, and AI governance.
• Governed human context: Employee context is designed to be visible, consented, correctable, purpose-limited, auditable, and shareable with agents only when relevant to a task.

Designed for sensitive workforce environments

Because Maya operates at the boundary between workforce identity and enterprise AI, the governance model is central to the product. In enterprise deployments, Maya is designed around employee-visible profiles, granular consent, purpose limitation, data-loss prevention, immutable audit logs, context expiry, and separation between private employee context and manager-facing support signals.

Where biometric-aware inputs or deeper transition-state signals are used, WORK-SELF recommends explicit consent, legal review, and a strict enterprise posture: no medical or diagnostic labels exposed to managers, no hidden surveillance layer, and no repurposing of private employee context for performance management without a separate lawful basis and human review.

Initial enterprise applications

• Financial services: AI-human orchestration across investment research, compliance monitoring, client advisory, model governance, and internal mobility into AI oversight roles.
• Professional services: Client proposal workflows, research synthesis, deliverable review, account planning, and quality-control handoffs between associates, managers, partners, and AI agents.
• Customer service and operations: AI triage, human escalation pods, policy-aware handoffs, and manager intervention when automation changes role identity and workload.
• Enterprise transformation programmes: Pre-announcement readiness diagnostics, redeployment planning, workforce transition coaching, and CHRO visibility across affected cohorts.

Ian Thompson
WORK-SELF | Public Relations Director
7766092604 ext.
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