WorkingAgents + Contextual AI: Governance for Expert-Level AI Agents

Contextual AI turns expert knowledge into actionable AI agents. WorkingAgents governs what those agents are allowed to do. When an AI agent is performing patent research, analyzing device logs, or planning production schedules, the stakes are too high for ungoverned execution. The partnership: Contextual AI provides the expert intelligence, WorkingAgents provides the control plane.

The Fit

Contextual AI — founded by Douwe Kiela, who pioneered RAG at Meta — builds specialized RAG agents for expert-level technical work. Agent Composer lets engineers in semiconductors, aerospace, and manufacturing build agents that turn hours of complex analysis into minutes. Customers include Qualcomm, NVIDIA, Advantest, ShipBob, and HSBC. The platform includes a Metadata Search Tool for multi-hop reasoning, custom RBAC, and a unified stack from development through production.

WorkingAgents is the access control and orchestration layer between AI agents and enterprise systems — three gateways, capability-based permissions, guardrails at every checkpoint, audit trails on every action.

Both platforms care about the same thing: making AI agents trustworthy enough for production. Contextual AI solves trustworthiness through RAG grounding and evaluation. WorkingAgents solves trustworthiness through governance and access control.

Synergy Areas

1. Permission-Scoped Expert Agents

Contextual AI’s agents access deep technical knowledge — device logs, patent databases, production systems, engineering documentation. Different roles need different access:

Senior Engineer Agent
  ✓ Full device log analysis
  ✓ Patent database search
  ✓ Production planning recommendations
  ✓ Write findings to engineering systems

Junior Engineer Agent
  ✓ Device log analysis (read-only)
  ✓ Patent search (own product line only)
  × Production planning
  × Write access to engineering systems

Contextual AI provides RBAC for its own platform. WorkingAgents extends that governance to every external system the agent touches — ERP, PLM, ticketing, communication tools — through the MCP Gateway. One permission model across the RAG knowledge layer and the action layer.

2. Governed Multi-Hop Agent Workflows

Contextual AI’s Metadata Search Tool enables multi-hop reasoning — an agent traverses references across documents to answer complex questions. When those reasoning chains lead to actions (filing a patent response, escalating a device failure, adjusting a production schedule), WorkingAgents governs the execution:

Contextual AI agent reasons across technical documentation (multi-hop RAG)
Agent determines an action is needed (e.g., “device failure pattern matches recall criteria”)
WorkingAgents validates the agent’s permissions for that action
Guardrails check: is the recommendation within safety bounds? Does it require human approval?
Action executes through governed MCP tools
Full audit trail: RAG reasoning chain + permission check + guardrail evaluation + action outcome

The reasoning is Contextual AI’s. The governance is WorkingAgents’. The audit trail covers both.

3. Cross-Platform Tool Access for RAG Agents

Contextual AI agents excel at knowledge retrieval and reasoning. But expert work doesn’t end at finding the answer — it requires acting on it across enterprise systems. WorkingAgents’ MCP Gateway gives Contextual AI agents governed access to those systems:

A technical support agent retrieves the solution from Contextual AI’s RAG, then creates a ticket, updates the CRM, and notifies the customer — each action governed by the user’s permissions
A production planning agent reasons across inventory and scheduling data, then adjusts orders in the ERP — within approved bounds, with human approval for changes above threshold
A patent research agent identifies prior art, then drafts a response and routes it for legal review — guardrails prevent unauthorized filing

Contextual AI provides the intelligence. WorkingAgents provides governed access to the tools where that intelligence becomes action.

4. Audit-Ready Expert AI for Regulated Industries

Contextual AI serves semiconductors, financial services (HSBC), and manufacturing — all regulated. Expert-level AI decisions in these domains face scrutiny: what knowledge did the agent use, what did it recommend, who approved it, and what happened?

Contextual AI’s RAG grounding answers the first question — traceable retrieval back to source documents. WorkingAgents answers the rest — permission checks, guardrail evaluations, approval workflows, and action logs. Together: a complete chain from knowledge source to governed action to auditable outcome.

Starting Point

Contextual AI agents need to act on their findings through enterprise systems. WorkingAgents exposes those systems as governed MCP tools. The integration: Contextual AI’s Agent Composer builds the expert reasoning agent, WorkingAgents’ MCP Gateway provides the governed action layer. One demo showing a RAG agent that reasons, acts, and is fully auditable end-to-end opens the conversation.

WorkingAgents is an AI governance platform specializing in agent access control, orchestration, and security for enterprises deploying AI at scale.