According to a recent Redwood Software customer survey, 68% of RunMyJobs by Redwood users work with AI tools multiple times per week. They ask ChatGPT to troubleshoot errors, use Copilot to draft scripts and paste job logs into Claude and ask what went wrong.
None of those AI models can reach into RunMyJobs and take action. They answer questions about your workflows but can’t run them, check on them or build new ones. Your AI assistants and your automation platform operate in separate worlds, and that gap is exactly what makes it hard to get real value out of either.
Model Context Protocol (MCP) support in RunMyJobs changes that.
Why every major AI platform adopted the same protocol
MCP is an open standard that gives AI systems a shared way to connect with external tools. Think of it as the USB port of agentic AI. MCP lets you plug any MCP-compatible AI agent into any MCP server without custom integrations. Drop an MCP server in front of a product, and AI systems can immediately interact with it, reading context, calling functions and taking action.
Anthropic released MCP in late 2024 and donated it to the Agentic AI Foundation under the Linux Foundation in December 2025. Since then, OpenAI, Google DeepMind, Microsoft, Salesforce and ServiceNow have adopted it. The protocol has moved past experimentation, as its interoperability across AI platforms is proven in production today.
For RunMyJobs users, MCP means something specific: the business logic, connectors and workflows you’ve spent years building are now accessible to AI agents through a standardized protocol that every major AI platform already supports. Any MCP-compatible AI tool or large language model can now trigger your workflows, check job status and build new workflows through the RunMyJobs MCP server with no custom API work and no rearchitecting. The workflows and connectors you’ve built over years of production use become tools that AI agents call on demand.
This is how your backend processes become agentic.
What can AI tools do through RunMyJobs’ MCP?
- Trigger workflows and jobs: Any MCP-compatible agent can kick off your existing RunMyJobs workflows to make your current processes agent-ready without migration.
- Check job status: AI tools can query whether critical jobs are running, finished or failed and surface that information inside whatever platform your team uses.
- Manage workflows: Coding agents can validate and deploy RunMyJobs workflows through MCP, cutting development time.
These capabilities work with Claude, Microsoft Copilot Studio, ServiceNow Agent Builder, n8n, ChatGPT and Salesforce Agentforce. Authentication, access controls and permissions all flow through RunMyJobs — agents can only do what their associated role is allowed to do.
Here’s what that looks like in three real-world use cases.
SAP’s Joule: Submitting and monitoring jobs from your ERP
Your SAP basis administrator needs to trigger a nightly data extraction early because a report deadline moved up. Instead of switching to the RunMyJobs console, logging in, finding the workflow and submitting it by hand, they stay in SAP and tell Joule:
“Submit the GL account extraction workflow for company code 1000.”
Joule calls RunMyJobs through MCP. The workflow starts. Joule confirms it.
An hour later, the same admin asks Joule about the financial data load. Joule checks RunMyJobs and finds that the extraction finished, but the transformation step is still running. Estimated completion: 45 minutes.
No dedicated SAP integration project made this possible. MCP standardized the connection. RunMyJobs partitions and roles still control who can trigger what, so your governance model is intact, but your admin gets a faster, context-aware path to the same workflow they’ve run hundreds of times before.
This is what it means to agentify your existing SAP processes: The workflows don’t move, and the business logic stays where it is. Joule just gets a direct line to it.
ServiceNow: Remediating failed batch jobs without the 2 AM phone call
Your nightly accounts receivable batch job fails at 2:14 AM. Today, that triggers a ServiceNow incident. An on-call operator picks it up, logs into RunMyJobs, reads the error log, figures out the cause, restarts the job with corrected parameters and closes the ticket. That process takes 30 to 90 minutes, depending on who’s on call and how fast they diagnose the issue.
With ServiceNow Agent Builder and MCP, the ServiceNow agent handles most of that loop. It detects the failed job alert, queries RunMyJobs through MCP for real-time error details and job history and matches the failure pattern against known remediation steps. If the fix is a known restart with corrected parameters — wrong file path, stale credentials, a transient connection timeout — the agent resubmits the job in RunMyJobs and updates the ServiceNow incident with what it found and what it did.
If the failure falls outside the agent’s confidence threshold, it escalates to the on-call operator with a pre-built diagnostic summary: the error, job chain context and last three successful runs for comparison. Your operator starts the investigation 15 minutes ahead of where they’d be without it.
RunMyJobs still controls execution. Partitions and roles still govern who — or what — can restart which jobs. ServiceNow still owns the incident lifecycle. MCP connects the two external systems without custom middleware in between.
Microsoft Copilot Studio: Finance teams running month-end close
Month-end close involves dozens of batch processes across ERP, consolidation and reporting systems. They run in strict sequence, often at night, and someone watches a console to catch failures.
Your finance controller builds a Copilot agent in Microsoft Copilot Studio. The agent submits the intercompany elimination workflow through MCP to RunMyJobs. When that job finishes, the agent triggers the consolidation jobs. If reconciliation fails, the Copilot agent sends the controller a Microsoft Teams message — a plain-language summary of the failure, plus the remediation workflow they can approve with one click.
The controller doesn’t need RunMyJobs training. They tell the Copilot agent what outcome they need, and RunMyJobs handles execution. Your finance team stays in Teams and focuses on the close, not the tooling.
What this means for your RunMyJobs investment
The good news for teams feeling pressure to adopt agentic AI: you don’t have to rewrite your enterprise workflows. You don’t have to move your batch processing into a new tool. MCP exposes what you’ve already built to the agents your developers want to build, through a standardized protocol they already know.
The automation fabric you’ve built in RunMyJobs is your real AI asset; MCP is how you unlock it.
The RunMyJobs governance model — partitions, roles, access controls — still applies. Scalable agentic orchestration doesn’t require trading away the enterprise-grade controls you rely on. Your AI-powered workflows run under the same oversight as everything else in your automation environment. But your teams get a new way to interact with automation that fits inside the AI tools they’ve already adopted.
Redwood is building toward a model where AI agents and workload automation run side by side, supporting open standards such as agent-to-agent (A2A) and MCP to unlock existing business logic and make it accessible in a governed and observable platform operating at enterprise scale. MCP support in RunMyJobs is where that starts, and the foundation is the automation you’ve already built.
See how RunMyJobs works with MCP to unlock your investment in enterprise applications and expose them to agents. Get a demo today.