Agentic orchestration in RunMyJobs: What it takes to make enterprise AI operational

Article

Plenty of vendors are bolting a chatbot onto an automation platform and calling it agentic. At Redwood Software, we think that’s backwards. Governance, reliability and scale aren’t qualifiers you add to a product. They are the product.

Every agentic action in RunMyJobs by Redwood, from a single job step to a complex multi-agent workflow, is traceable, auditable and compliant. Deterministic guardrails apply hard-coded logic constraints to probabilistic AI, preventing rogue agent behavior before it reaches your systems of record. Your existing automation doesn’t get replaced — it becomes AI-ready, so you build on what already works.

Start building autonomous workloads now

We built RunMyJobs’ AI capabilities the way we did because the pressure on enterprise automation teams is real and coming from multiple directions at once.

1. Shadow AI is already inside your business. Departmental agents are spreading without central oversight, exposing sensitive data and credentials to third-party AI while critical business logic gets trapped inside individual chat histories. 82% of organizations discovered shadow AI agents in the past year.
2. Agentic conflict is coming. Agents optimized for different outcomes will collide over goals, shared resources and policies, the same way human departments do, except faster and at machine scale. Gartner projects at least 15% of day-to-day work decisions will be made autonomously by 2028. Somebody has to referee that, and it won’t be a spreadsheet.
3. Your automation was built for absolute certainty. AI agents reason probabilistically. Those are incompatible worlds, and rebuilding decades of critical business logic to force them together is neither feasible nor safe. One agentic hallucination can poison a data pipeline or exploit permissions that were designed for rigid scripts, not goal-seeking software.
4. The environment is shifting. Gartner also predicts that 33% of enterprise software applications will include agentic AI by 2028. When a third of the software landscape assumes agents are present, platforms that can’t govern them become a liability.

The path forward is incremental

You don’t have to reach full autonomy overnight. Like any automation maturity journey, the path to agentic orchestration is progressive. You start by augmenting your people with AI-powered productivity tools. Then, you make your existing business processes AI-ready so agents can reach the systems that matter. Eventually, you orchestrate autonomous agents natively inside the workflows your enterprise already depends on.

That progression only works if the platform underneath is designed to support each stage without requiring you to rip out what you did in the last one. The AI capabilities in RunMyJobs map to three building blocks: agentic productivity, agentic business processes and agentic enterprise, each solving distinct problems and delivering value independently. Every step forward is governed and reversible. Here’s what each one looks like in practice.

Building block 1: Agentic productivity

Start with making people more productive, building and managing automation at scale.

The Redwood RangerAI Product Assistant and Automation Co-pilot embeds an intelligent expert inside the platform, right next to every operator. Instead of digging through documentation or waiting on the one senior person who knows everything, your team asks a question and gets a grounded, plain-language answer. The Co-pilot:

• Generates job scripts from natural language
• Operates the platform through conversational commands
• Produces documentation in a click

Junior engineers start resolving issues that previously sat in a senior architect’s queue.

Workflow Builder tackles a bottleneck most automation teams have simply accepted as life: the specialist intake queue. Every new workflow goes through a developer, usually via a ticket, and some enterprises process 30 to 50 of those a week. With Workflow Builder, a process owner describes a new data reconciliation workflow in plain English or uploads an SOP, and an intent agent:

• Matches the request to the actual jobs, connectors and schedulers already in the environment
• Builds the chain and resolves parameters
• Flags anything it can’t resolve for human input
• Creates nothing until a human explicitly approves

The specialist’s role shifts from building to reviewing.

Building block 2: Agentic business processes

This is where you make your existing business logic AI-ready without rewrites, custom APIs or architectural overhaul.

The RunMyJobs Model Context Protocol (MCP) server gives your AI reasoning tools governed access to the systems that actually run the business. Over 50 tools across nine global AWS regions, with OAuth 2.0 authentication and per-request credential isolation.

Your workflows become the agent’s toolbox.

Instead of giving an agent raw credentials to your ERP, you give it a workflow that already encodes the right steps, permissions and error handling. The agent gets a trusted tool. Your systems get a hard boundary and full audit traceability. For SAP-centric customers, the MCP server is validated with SAP’s Joule, so your SAP AI assistant can invoke RunMyJobs operations natively.

In practice, this opens up entirely new operating models:

• An operations engineer uses Microsoft Copilot or Slack to submit jobs, restart failed steps and raise events through natural language without opening the RunMyJobs UI
• A finance team’s AI assistant, scoped to their partition, triggers a reforecast workflow when demand signals change, monitors status and notifies the team on completion — without IT involvement
• A developer uses Claude Code to trigger a data refresh in a production environment, then RunMyJobs verifies all dependencies and prerequisites are met, giving Claude a trusted way to execute in critical environments. 

The Operations Agent tackles one of the most persistent costs in enterprise automation: overnight incident triage. It changes what an alert means:

• Proactively detects failures, SLA deviations, silent completions and cascade outages
• Delivers enriched alerts with job history, blast radius, SLA countdown and suggested next actions
• Groups dozens of alerts from a single root cause into one incident

When a critical batch job fails at 3 AM, the agent identifies the twelve downstream jobs at risk, calculates the SLA countdown and delivers a single contextualized notification — before the engineer touches the keyboard. The destination is fully autonomous tier-one and tier-two remediation.

Building block 3: Agentic enterprise

Agent Studio gives you the lowest-friction path to embedding agents inside the workflows you already run. 

Write agent skills, connect MCP servers as tools, choose your preferred LLM and drop the agent in as a step under the same governance and observability model as everything else. Your deterministic processes stay unchanged. The agent adds a judgment layer for exception handling, risk scoring or compliance narrative drafting, and a human stays in the loop on every consequential call.

• A reconciliation agent investigates mismatches, resolves known patterns and escalates only genuinely novel cases
• An approval agent risk-scores incoming requests, auto-clears low-risk items with an audit trail and routes the rest to a human
• A compliance agent drafts the narrative auditors need, summarizing what changed each period for human review

Consider a data validation job that encounters an anomalous record set matching no existing exception rule. An Agent Studio step invokes an LLM to classify the anomaly, decides whether to escalate or auto-resolve and writes the outcome to an output parameter, all within the same job chain and under the same partition governance.

As you scale beyond individual agents, agentic workflows coordinate fleets of specialized agents toward shared goals with managed state, shared context and centralized conflict resolution. And the agentic library eliminates the cold-start problem by transforming your existing jobs, workflows and enterprise connectors into trusted tools and pre-built skills. Your agents show up already understanding your business.

Start where you are

Redwood brings 30 years of enterprise experience and the trust of more than 50% of the Fortune 50. RunMyJobs is the only SAP Endorsed App for workload automation and orchestration and the only Service Orchestration and Automation Platform (SOAP) in the RISE with SAP reference architecture. That trusted foundation is what makes governed autonomy possible at enterprise scale.

You don’t need to overhaul everything at once. Augment your operators. Make your existing logic AI-ready. Embed one agent inside one workflow you already run. Each move builds trust, extends autonomy and delivers something measurable, and you can pause or reverse at any point.

Explore the AI and agentic capabilities in RunMyJobs and find your starting point.

SAP Landscape Management reaches end of support: Keeping hybrid SAP operations automated

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SAP has confirmed that mainstream maintenance for SAP Landscape Management 3.0 will conclude on December 31, 2027, with no extended maintenance currently planned. As an add-on to SAP NetWeaver AS for Java, SAP Landscape Management (LaMa) is directly affected by the end of SAP Business Suite 7 mainstream maintenance. SAP Landscape Management Cloud, which was originally positioned as the cloud-based successor, has since been discontinued as SAP refocuses its strategy on RISE with SAP with SAP Cloud ERP Private and GROW with SAP with SAP Cloud ERP.

That timeline aligns with the end of support for SAP Business Suite 7 (ECC 6.0) and SAP Solution Manager 7.2, making 2027 a convergence point that will reshape how SAP Basis teams approach landscape operations, system monitoring and infrastructure automation.

SAP’s direction makes it clear that the future of SAP operations is cloud-first, standardized and increasingly managed by SAP itself. That strategy is well-founded, and it’s already delivering real value to customers adopting RISE with SAP and GROW with SAP.

For many organizations, though, the transition timeline means hybrid operations will remain a reality for years to come. And SAP LaMa handled a set of operational workflows that don’t retire just because the tool does.

What still needs to be orchestrated

In managed environments like RISE with SAP and GROW with SAP, many traditional SAP Basis tasks are handled by SAP. SAP Cloud ALM replaces parts of the operational capabilities previously associated with SAP Solution Manager, while SAP manages more of the underlying infrastructure.

These don’t eliminate the need for orchestration. Organizations still need to automate system refreshes and copies, SAP Post-Copy Automation (PCA), cross-platform disaster recovery (DR) and cross-system operational workflows that span self-hosted SAP systems, cloud environments and non-SAP applications. These workflows remain essential in hybrid landscapes, even as more infrastructure moves under SAP management. 

Customers who relied on SAP LaMa for this coordination need a new approach before support ends in 2027.

The 10-year reality of RISE with SAP transformations

SAP’s investment in managed cloud services gives customers a strong foundation for cloud operations. Virtual machines, high availability configurations and patching are increasingly handled within the RISE with SAP operating model.

But the reality is that most large-scale RISE with SAP transformations are multi-year programs. For numerous enterprises, this is a decade-long journey where, even after completion, certain technical systems will remain outside SAP’s managed perimeter. Manufacturing systems, regional SAP HANA instances, legacy landscapes, reporting environments, industry-specific solutions and non-SAP satellite applications all continue running alongside SAP-managed cloud ERP.

The picture is more grey than black-and-white. SAP’s managed cloud addresses a significant portion of infrastructure operations, and most large enterprise customers with diverse landscapes also need orchestration that extends across what they continue to operate themselves.

Total cost of ownership (TCO) and operational efficiency don’t improve by adding more disconnected tools to cover what SAP LaMa used to handle. They improve through consolidation into a modern orchestration layer that works across every environment in the landscape, managed and self-hosted alike.

Extending SAP operations with RunMyJobs

RunMyJobs by Redwood helps you preserve the automation, orchestration and governance your hybrid SAP landscape still requires. As the only SAP Endorsed App for workload automation and the only orchestration platform included in the RISE with SAP reference architecture, RunMyJobs automates across the full SAP ecosystem while supporting clean core principles through standard, SAP-approved integrations rather than ERP customizations.

RunMyJobs complements SAP’s managed cloud operations by extending orchestration to the systems, workflows and dependencies that customers continue to operate themselves — and covering the operational scenarios where SAP isn’t providing a direct successor.

SAP operations after SAP LaMa

Here’s how that translates to the specific challenges SAP LaMa’s retirement raises.

• Orchestrating across both sides of the managed boundary: RunMyJobs coordinates workflows across self-hosted SAP systems, RISE with SAP, SAP Business Technology Platform (BTP), hyperscalers, databases and non-SAP enterprise applications from a single platform. Operational runbooks, DR procedures and IT service management workflows continue running seamlessly across hybrid environments, instead of fragmenting into disconnected scripts and manual handoffs. 
• Keeping system refreshes zero-touch: RunMyJobs orchestrates complete system refreshes end to end, coordinating SAP Post-Copy Automation (PCA) task lists, massively parallelized BDLS processing, database restores, storage operations and validation activities without manual intervention. Instead of reverting to manual scripts, disconnected runbooks and spreadsheets, you maintain standardized, repeatable workflows with centralized visibility and automated error handling.  
• Preserving a clean core strategy across the full transformation timeline: As organizations progress through multi-year RISE with SAP journeys, RunMyJobs connects through SAP-approved APIs and integrations free of additional local software and code installation, rather than introducing ERP customizations. SAP Basis teams can automate operational runbooks and cross-system dependencies while keeping custom logic outside the ERP core, making future upgrades and innovation easier to adopt. 
• Delivering reliability without adding infrastructure: While SAP manages more of the underlying infrastructure, organizations still need orchestration for hybrid operations. A self-managed replacement for SAP LaMa simply recreates the infrastructure your team is trying to leave behind. RunMyJobs is a cloud-native SaaS platform with 99.95% guaranteed uptime, predictive SLA monitoring and automatic upgrades, allowing SAP Basis teams to focus on modernization instead of maintaining automation infrastructure. 

Plan your transition now

The retirement of SAP LaMa, with both the on-premises version and its planned cloud successor now approaching end of support, is more than a product lifecycle event. It signals that the operational architecture around SAP needs to evolve alongside the ERP itself.

More infrastructure will become managed. More ERP services will move to the cloud. More organizations will embrace clean core principles and SAP Cloud ALM as their operational command center.

At the same time, hybrid landscapes will remain a reality for years. System refreshes, DR orchestration, cross-platform dependencies and operational runbooks don’t disappear because the underlying deployment model changes. Coordinating these environments across managed and self-hosted boundaries becomes more important as the architecture distributes.

Treat SAP LaMa’s retirement as an opportunity to modernize your orchestration strategy, and your organization will be better positioned to support both today’s hybrid operations and tomorrow’s cloud-first SAP landscape.

To learn how RunMyJobs automates SAP and non-SAP operations across hybrid environments.

Governed for quality, ungoverned for delivery: Why trusted data still shows up late  

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Every data investment your organization has made rests on an assumption nobody wrote down: that the data will arrive complete, current and on time when the business reaches for it. The business carefully approved important data programs on their merits: a Snowflake migration, a Databricks rollout, the AI roadmap. All of them assume accurate data will show up when it’s needed. And the layer responsible for making that happen almost never gets tested against it.

I regularly sit across the table from data organizations in architecture reviews and discovery sessions, and I’ve watched the same pattern play out at company after company. A transformation feeds the nightly load that finance and analytics both depend on, but it runs on customer data that went stale two days earlier because an upstream sync failed, and nothing tied that failure to the deadline downstream. No alert fires, because someone built the alerting to catch job run failures instead of deadlines that slip. The pipeline run reports green, the executive dashboard reports green and the number that drives a decision is wrong. Nobody finds out until after someone has already acted on it.

The pattern repeats no matter how different the organizations are otherwise. Data infrastructure investment and data delivery reliability get treated as the same thing, and they aren’t. Enterprises pour money into the first and leave the second as an afterthought.

A green pipeline isn’t a business guarantee

Ask most data teams whether a pipeline succeeded, and they’ll check three things: did the job complete, did it throw errors and is the status green. All that tells you is that the process ran. It says nothing about the question the business is actually asking: did the right data show up, complete and current, at the moment a mission-critical application outcome, a strategic report or an agentic AI workflow needed it?

Those two definitions of “done” drift apart constantly. A Snowflake load can finish exactly on schedule, then hand off to an ERP process that opened its window before the load was ready. A dbt model can complete without a single error and still deliver its output an hour after the executive pack was already pulled. Both register as passing jobs, and both are deliveries that failed the business. That’s how you end up with a wall of green and no idea whether the business got what it needed.

Where accountability lands

Nobody measures you on pipeline uptime. They measure you on whether your analytics produce decisions people can trust, whether your AI initiatives survive scrutiny and whether the business believes the numbers you put in front of it. Those outcomes depend on an orchestration layer that executes the entire business process that’s reliable, traceable and measurable against real deadlines, not just functionally in a narrow, technical sense.

When that business process execution breaks behind the scenes, the fallout doesn’t stay inside engineering. It shows up as a figure that moved after leadership already acted on it. Once the business loses confidence in the data, somebody has to account for why. In the reviews I sit in, that reckoning lands on whoever owns the orchestration layer, working backward through four systems to piece together what happened and when. Trust doesn’t come back quickly, either: a long stretch of dependable delivery earns it, and one discrepancy nobody can explain spends it.

AI has made this exposure worse. When a model produces a bad outcome, the reflex is to interrogate the model. But the real culprit is more often a data refresh that ran late or died upstream. Most programs aren’t ready for this: Gartner found that 63% of organizations either lack the right data management practices for AI or aren’t sure they have them. The same research predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. Without audit trails that show what arrived and when, there’s no way to prove the data was sound. You end up defending a result you can’t fully reconstruct, which is a hard position to be in when the questions come from the board.

The line item that wasn’t on the invoice

The organizations I work with have spent seriously on data platforms, data quality tooling, observability inside those platforms and, lately, on AI and machine learning infrastructure. The money isn’t the issue. What all that spending doesn’t produce is an orchestrated, governed execution layer: something that confirms the pipelines feeding your business services are delivering against real deadlines and required outcomes, not just running without errors.

The gap shows up first in the industries that went farthest down the automation road earliest. In financial services, 61.4% of organizations report that siloed automation environments constrain their AI readiness, according to original research from Redwood Software. These are well-funded organizations whose sophisticated data architecture never accounted for production coordination across the full business process. And where financial services goes, the rest of the data-intensive economy usually follows.

Here’s where I’d push back on how governance gets scoped. Most data governance frameworks orient around data quality dimensions like accuracy, completeness and consistency, and control concerns such as data security, access controls and regulatory compliance under GDPR or HIPAA. All of that matters, yet none of it answers the delivery question: did this data arrive for the process that needed it, at the time it needed it, with a traceable record of the path it took?

Trusted data isn’t only clean, compliant data. It’s data that arrived when the business needed it, and most governance programs have no way to prove that part.

Monitoring pipelines vs. governing delivery 

Closing this gap calls for a change in how data delivery is governed, not another tool bolted onto your stack. Treating data workflows as production business services means a few specific things:

• Monitoring gets tied to business outcomes rather than job completion, so the question becomes whether the financial close data is ready by the window the ERP depends on, instead of whether the pipeline ran
• Dependency visibility runs the length of the chain, so a late or failed step upstream surfaces before the service deadline instead of during the postmortem
• Evidence becomes something you can produce on demand: what ran, when, what it depended on and whether it met its SLA, without a two-day expedition across half a dozen disconnected tools and multiple teams
• Alerting gets calibrated to business impact rather than technical status, so the signals that reach you are the ones that matter

The distance between “we monitor pipelines” and “we govern data delivery” is an operating chasm most data strategies have left open. It’s the difference between catching a problem in your own systems and hearing about it from the business.

Solve the problem that lives between teams

Delivery governance sits precisely where no single team’s mandate reaches. Between the team that runs the pipelines, the one that builds the applications and the one that schedules, runs and monitors the production process end to end, every technical task has a clear owner. What none of them answers for is whether the business got what it needed, on time and with proof it arrived. That question goes unanswered simply because nobody was assigned to answer it.

Closing it takes an orchestration layer that sits above the individual tools without replacing them. That layer connects the tools you already run (Snowflake, Databricks, Airflow or a managed Airflow service, native schedulers inside ERP and cloud services) and holds the full flow to the deadline the business actually cares about, recording what happened at every handoff. RunMyJobs by Redwood ties these siloed tools together into one governed flow. Airflow keeps orchestrating its pipelines; Snowflake keeps running its loads. What changes is that the delivery across all of these disparate tools finally has an owner and a documented, auditable trail. 

When someone asks whether the data was complete and on time, the answer is already on record rather than reconstructed after the fact. Consolidating orchestration this way also means accelerating transformation across the enterprise at the lowest possible total cost of ownership (TCO) instead of adding one more scheduling tool for your team to manage and maintain.

Your board and your AI sponsors weren’t asking whether the pipeline ran. They want to know the data was there, complete, on time, ready when the business-critical decision needed it. That’s not something a data platform can tell you. The answer comes from the orchestration layer that governs how data reaches the business. Most companies haven’t built that layer yet. Until they do, all the governance in the world doesn’t answer the one question the business asked: did the data show up when we needed it?

See how RunMyJobs connects hybrid data pipelines.