MCP as the Agentic Bridge — AI-Enabled Process Collaboration
How Model Context Protocol transforms process engines into AI-native orchestration infrastructure
Inside the AI-Enabled Process Agent
LLM Reasoning Core
• Natural language understanding • Multi-step planning • Context-aware decisions • Domain-specific inference
Domain Ontology
• Shared vocabulary contracts • SALT / MUCC / ICS semantics • Context-dependent meaning • Cross-process interoperability
MCP Client Interface
• Dynamic tool discovery • Schema-guided invocation • Authorized operations only • JSON-RPC 2.0 over stdio / SSE
reasons about
interprets as
invokes
MCP PROTOCOL BRIDGE — CAMUNDA 8.9 GATEWAY
DISCOVER
searchProcessInstances
Process Definition Tools
GOVERN
Schema-as-Governance
Role-based Authorization
ACT
writeVariable
createProcessInstance
OBSERVE
Conditional Events
Cluster Variables
Camunda 8.9 Process Engine — Cluster State
Running Instances
All active workflows across the cluster
BPMN Definitions
Deployed process models available for discovery
Cluster Variables
Shared state readable by any process instance
Authorization
Role-based access — same rules as human ops
The Agentic Shift
Before MCP: Human coordinator fills the gap between processes Phone calls, spreadsheets, undocumented decisions With MCP + AI Agents: Agents discover, reason about, and operate across process boundaries through a governed protocol
Research Ecosystem (2026)
Srinivasan — MCP production design patterns Zhu & Wang — Trust-as-a-Service via MCP agentic AI Schlapbach (SBB) — Schema-guided dialogue + MCP Dobriy et al. — SPARQL-MCP agents on knowledge graphs Stein — 177K MCP tools empirical study Stratis — AI Agents with MCP (O’Reilly, Aug 2026)
Agent → Protocol → Engine
State → Schema → Reasoning
Three layers: AI reasoning (top) flows through MCP (middle) to reach process engine state (bottom) — and back
Figure 2 — MCP as the Agentic Bridge: AI-Enabled Process Collaboration | S7-MCI | April 2026