Enterprise AI is running into a less glamorous problem: getting intelligent systems to actually execute work across the applications and infrastructure businesses already depend on. Redwood Software is addressing that gap with the latest release of RunMyJobs, adding native AWS connectivity, a generally available Model Context Protocol (MCP) server and new AI capabilities designed to connect agentic reasoning with enterprise workflows.
Redwood Wants AI Agents to Do More Than Recommend the Next Step
The enterprise AI market has spent the past few years focused on models, copilots and increasingly sophisticated agents. But a capable AI system still has limited value if it cannot safely trigger the systems that execute the work.
That is where enterprise orchestration is becoming a more important layer.
Redwood Software has released RunMyJobs 2026.3, positioning the latest version as a bridge between AI agents and the underlying workflows, applications and infrastructure that keep large organizations operating.
The release comes alongside Redwood’s recognition as a Leader in Gartner’s 2026 Magic Quadrant for Service Orchestration and Automation Platforms (SOAP). Gartner’s report evaluates vendors according to ability to execute and completeness of vision and describes SOAP platforms as technology that unifies workload automation, workflow orchestration and data pipelines across on-premises and cloud-native environments.
Redwood says it also ranked first in four of the five use cases in Gartner’s 2026 Critical Capabilities research, with a tie for first in the remaining use case. Those rankings are Redwood’s characterization of the Gartner research, rather than an endorsement by Gartner.
The more strategically important development, however, may be the product release itself.
MCP Connects AI Reasoning to Enterprise Execution
RunMyJobs 2026.3 adds Redwood’s Model Context Protocol server to general availability.
MCP has emerged as a standardized way for AI applications to connect models with external tools and data. In Redwood’s implementation, the MCP server gives AI systems controlled access to more than 50 tools across nine AWS regions, with auditability built into the interaction.
Redwood says the server has been validated with Microsoft Copilot, SAP Joule and Anthropic’s Claude Code.
The practical implication is straightforward: an AI agent can move beyond analyzing a business problem and begin interacting with the workflows that resolve it.
For example, an AI system might identify a failed business process, inspect its associated workflow, retrieve operational information and initiate an approved remediation process. The agent does not need to replace the existing enterprise automation layer. Instead, the orchestration platform becomes the execution mechanism.
That distinction matters because large enterprises rarely have the luxury of rebuilding their technology stacks around every new AI capability.
They have decades of scheduled workloads, ERP integrations, data pipelines, cloud services and business rules already running.
AWS Integration Targets Hybrid Environments
The release also adds native connectivity to Amazon Web Services, including Amazon S3, EventBridge and Glue.
The significance is less about adding another cloud connector and more about making cloud and legacy workloads part of the same orchestration environment.
Modern enterprises frequently operate hybrid estates in which applications run across public clouds, private infrastructure and on-premises data centers. A workload may begin in an ERP platform, trigger a cloud data pipeline and eventually feed an analytics or AI system.
Without orchestration across those environments, AI agents can become isolated from the processes they are supposed to improve.
Redwood’s approach is to put AI interaction on top of the existing workflow layer rather than treating every agent as an independent automation system.
This is increasingly aligned with where enterprise AI architecture is heading.
IDC’s 2026 FutureScape research identifies enterprise-wide orchestration as a key step in moving AI beyond isolated pilots, arguing that organizations need coordination across agents, applications, data and workflows.
From Workflows to Agentic Operations
RunMyJobs 2026.3 also introduces three AI capabilities: Agent Studio, Workflow Builder and Operations Agent.
Agent Studio allows organizations to incorporate large language model reasoning into existing workflows, while support for MCP and the Agent2Agent (A2A) protocol is intended to let AI agents trigger and participate in orchestrated processes.
Workflow Builder takes a more direct route into automation. Redwood says users can describe a workflow in plain English or provide documentation, with the system generating an auditable workflow.
Operations Agent focuses on the other side of the lifecycle: monitoring running processes, identifying failures and detecting risks to service-level agreements.
That creates a potentially important feedback loop.
An agent can help create or initiate a workflow. The orchestration platform executes it. Another AI capability monitors the result and provides context when something goes wrong.
The goal is not simply to automate one task. It is to create a controlled operating layer around autonomous software.
Why Enterprise Orchestration Is Becoming Critical
The market is approaching a point where the number of AI agents inside organizations could become difficult to manage through application-by-application controls.
IDC reported in June that 50% of organizations were already deploying AI agents in production across multiple business areas, while another 27% had agents operating in at least one business area.
IDC has also projected that the number of active AI agents could reach 1.15 billion by 2029, executing hundreds of billions of actions per day.
Those numbers help explain why orchestration is becoming an architectural issue rather than simply an automation feature.
More agents mean more decisions, more system calls and more dependencies. Enterprises will need mechanisms to determine which agents can perform which actions, how those actions are audited and how failures are handled.
That puts traditional workload automation platforms in an interesting position.
Companies such as IBM, Broadcom, BMC, HCLSoftware, Stonebranch and Rocket Software already compete in service orchestration and automation. Meanwhile, cloud providers such as AWS, Microsoft and Google are building their own AI-agent ecosystems and enterprise automation capabilities.
Redwood’s strategy is to position RunMyJobs as the execution layer connecting those worlds.
The Shift From AI Pilots to AI Infrastructure
The timing reflects a broader enterprise AI problem.
McKinsey’s 2025 global AI survey found that 88% of respondents said their organizations regularly use AI in at least one business function, but most organizations were still in experimentation or pilot phases rather than full enterprise-scale deployment.
The next challenge is therefore not simply adoption. It is integration.
For CIOs and enterprise architecture teams, the question is becoming how AI agents can operate without creating another collection of disconnected automation silos.
That makes governance and execution as important as model intelligence.
Redwood’s product direction suggests one possible answer: keep the enterprise’s existing orchestration infrastructure at the center and give AI agents controlled access to it.
If that architecture works at scale, the AI agent becomes less like an isolated chatbot and more like a participant in an enterprise operating system.
That may ultimately be the more important battleground for agentic AI.
Market Landscape
The enterprise automation market is moving toward a convergence of workload automation, workflow orchestration, AI agents and cloud infrastructure.
Gartner’s 2026 research describes service orchestration and automation platforms as systems that unify workload automation, workflow orchestration and data pipelines across on-premises and cloud-native environments.
AI is adding another requirement: those platforms increasingly need to coordinate autonomous software as well as conventional workloads.
IDC’s 2026 research identifies fragmentation as a major obstacle to scaling AI, pointing to disconnected agents, data and workflows as barriers to enterprise-wide orchestration.
This creates an increasingly competitive field.
Traditional automation vendors such as Redwood, BMC, Broadcom and IBM are adding AI capabilities to established orchestration environments. Meanwhile, hyperscalers including AWS, Microsoft and Google are developing agent platforms around their cloud ecosystems.
The strategic question for enterprises is whether AI agents should become another layer of application-specific automation or operate through a centralized orchestration and governance layer.
Redwood is clearly betting on the latter.
Top Insights
- Redwood’s RunMyJobs 2026.3 connects AI agents with enterprise workflows, helping organizations move from AI recommendations toward governed execution across hybrid infrastructure.
- The new MCP server provides controlled access to more than 50 tools across AWS regions, giving AI systems a structured path into existing business automation.
- Native AWS connectivity links cloud services with established enterprise workflows, reducing the need to redesign legacy systems around emerging AI architectures.
- Agent Studio, Workflow Builder and Operations Agent extend orchestration from scheduled automation toward agentic workflow creation, execution and operational monitoring.
- As enterprises deploy more AI agents, centralized orchestration could become critical for governance, auditing, system coordination and reliable execution at scale.
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