A business unit wants to put an AI agent on its fulfillment exception process. They’ve found a process with enough variance to justify AI, enough repetitive manual work to make automation attractive and enough business value to get leadership’s attention.
Now, they want sign-off from the architecture team, and not just for this agent. For any new agentic application.
You know that if you approve it, the next team will point to this decision. Then the next. Before long, one narrowly approved use case becomes the precedent for how agents act across your hybrid estate.
As you look through the proposal, you search for strong external controls on the agents, because you know that whether the agent is smart enough isn’t the issue. In a complex business process, an agent can reason perfectly and still become the riskiest participant in the chain if the architecture can’t prove what it touched, what it could’ve touched and what happened after it acted.
The precedent is bigger than the use case
Most AI governance conversations still start too close to the agent: with the model, the prompt, the runtime, the framework, the evaluation scores.
Those do matter, but they’re not the part you’ll be asked to defend when the agent triggers something downstream.
What you’re really approving is an execution model. Can an AI-powered decision leave the tool, cloud service or application where it originated and act safely across the systems that run the business?
For example, a fulfillment process may cross an ERP system, a warehouse platform, a data pipeline, a service management workflow and an on-premises dependency that’s been stable for years. Each domain has its own controls; each one can prove its own piece. But the business process doesn’t care where the boundaries are.
You already know how these environments accumulate, in waves. These cloud migration waves move important workloads forward, but the execution paths underneath them don’t always modernize at the same pace. A polling job becomes the bridge, a script becomes the handoff and a temporary trigger becomes production infrastructure because the business keeps depending on it.
Take an order-to-cash path as an example: a bank file, an older scheduler, an ERP update, shell scripts, a cloud landing zone, an event rule, a transformation job and a warehouse load. None of those pieces is inherently wrong. The issue is that the process now spans multiple systems, teams and control planes.
When someone asks you to “agentify” that kind of process, you can’t avoid the architectural question: what can the agent decide, and what can your estate prove it’s safe to do?
Gartner predicts that more than 40% of agentic AI projects will be scrapped by the end of 2027. The surviving projects will not only have better agents, but also a governed path to production.
Proof of execution
Most teams aren’t training foundation models from scratch. They’re assembling agents from managed models, cloud AI services, copilots, agent frameworks, internal APIs and existing workflow logic. These agents may be able to reason through decisions, but the architecture still has to answer the questions that determine whether it can act:
Who owns the workflow after the agent triggers it?
Who is watching the process end to end?
What exactly can the agent touch?
Which steps require approval or validation?
What dependencies have to resolve before execution continues?
Can an in-flight action be stopped before it completes?
Can security, legal and compliance prove what happened afterward?
Real trust in agentic AI comes from what the execution path can enforce: scoped identity, executable guardrails, downstream visibility, deterministic controls and an audit trail native to the workflow. The agent can evaluate options freely. What it can do in production shouldn’t expand because its reasoning sounded convincing.
Open protocols such as Model Context Protocol (MCP) and Agent2Agent (A2A) reduce the need to solve connectivity per agent, per vendor and per model update. But connectivity doesn’t settle the architecture question. Without a governed access point into the hybrid estate, each new agent arrives with its own identity decisions, logging assumptions, rollback plan and exception path. Pilot momentum can, therefore, turn into technical debt.
Orchestration carries the action from connection to completion, under the controls the business requires.
Give the agent a governed route
You don’t need a longer AI policy as much as you need a controlled execution path. AI agent actions should route through the same kind of layer that already governs critical workflows across cloud, enterprise applications, data platforms and on-premises systems.
That changes your review:
The agent gets a governed interface, not a direct line to production systems
Workflows exposed through MCP can be scoped to exactly what the agent is allowed to do
The same access model used for human-triggered jobs can apply when an agent initiates the action
If an agent-triggered step needs to stop midstream, it should use the same control path as any other failed job, stuck execution or blocked dependency. Audit should work the same way: a byproduct of execution, not a separate reconstruction from scattered logs.
Before you sign off
So what do you tell the business unit this week?
Approving as-is is hard if the process underneath has no single place tracking what ran, what depended on what or how to stop the action midstream. Sending it back with no path forward only guarantees the same request will come from another team.
The approvable answer is conditional: route the agent’s actions through a governed orchestration layer before it touches the business process.
Then, human-triggered and agent-triggered steps can report through the same layer. The distance between “the agent decided” and “the work safely happened” closes because something sits between reasoning and execution, not because the agent got better at reasoning.
RunMyJobs by Redwood helps you build that governed layer, so you can orchestrate hybrid workflows across cloud, enterprise applications, data platforms and on-premises systems with the visibility, control and auditability agentic AI requires.
I’ve sat in enough SAP Integrated Business Planning (IBP) rollout conversations to notice a pattern: almost everyone talks about the planning model, and almost no one talks about what feeds it. That may be fine, until a forecast comes back looking wrong and nobody knows why.
SAP IBP has earned a strong position with SAP customers for good reason. It brings demand planning, inventory optimization, response and supply planning and sales and operations planning into a cloud platform built for modern supply chain volatility. Customer feedback reflects that momentum. SAP recently highlighted SAP IBP’s top customer choice status across major software review platforms. For many SAP-centric enterprises, SAP IBP is becoming the supply chain planning system where teams make decisions they can’t afford to get wrong.
But SAP IBP doesn’t create reliable plans from the platform alone. It depends on accurate, timely data arriving in the right sequence.
Only as strong as your weakest handoff
A demand planning run is only useful if the master data behind it is current. A replenishment recommendation is only reliable if the latest inventory levels, sales activity and supplier performance data made it into the planning model. And a demand forecasting simulation only helps if the upstream data movement completed before the simulation started.
When a planning result in SAP IBP looks off, the problem may have started somewhere else entirely — in SAP S/4HANA, SAP Cloud Integration for data services, a warehouse system, a supplier feed or a non-SAP application that missed its window. Modern supply chain planning depends less on whether individual systems can execute and more on whether their dependencies stay aligned.
That sounds obvious, but it’s exactly where many supply chain planning processes become fragile. Teams tend to focus on the planning application because that’s where the business outcome appears. But the data movement, dependency handling and monitoring supporting it often receive less attention.
In production planning, every delay matters, and the underlying cause is the same whether the trigger is SAP IBP or the shop-floor data pipelines feeding SAP production planning. A late data load can translate directly into excess inventory, the wrong replenishment signal or a planning cycle that finishes after operations needed to act. For supply chain planners, the cost is a missed window to respond to something that would’ve been fixable an hour earlier.
The gold-standard engine behind SAP IBP forecasts
SAP IBP is designed to support sales and operations planning (S&OP), demand forecasting, inventory optimization, response planning and supply planning in a single cloud-based environment. It helps supply chain planners sense demand variability, run scenario planning and align operational plans with financial planning expectations. SAP IBP uses machine learning and predictive analytics to power this, running what-if simulations and flagging disruptions before they compound. To do any of that well, SAP IBP needs high-quality time-series and master data from across the enterprise.
SAP Cloud Integration for data services (SAP CI-DS) is the required engine for that data movement. It connects on-premises systems, cloud applications and SAP IBP across a hybrid IT landscape, so planning processes work from current data rather than yesterday’s snapshot. In many cases, SAP CI-DS is the operational bridge between where supply chain data lives and where planning decisions are made.
When SAP CI-DS tasks run cleanly, the process looks uneventful. Data arrives, planning templates execute, reports refresh and business users get what they expect.
When something goes wrong, the symptoms usually surface later and somewhere else, as a forecast that looks inconsistent because a data load only partially completed or a planning simulation that starts before the required master data is available. The individual task failure often isn’t the whole story.
This isn’t unique to SAP IBP. Recent Redwood Software research found that 8 in 10 manufacturers have automated less than half of their critical data transfers, and more than a quarter still move sensitive documents by hand. Whatever the planning system, the pattern holds: missed or incomplete data movement affects inventory management, procurement, distribution planning and production schedules downstream. If the plan overstates demand, then safety stock, carrying costs and holding costs rise. If it understates demand, stockouts, lead times and customer satisfaction become the problem. Either way, supply chain performance suffers because the system acted on incomplete or late data.
Success in each system ≠ success across the chain
SAP IBP and SAP CI-DS both execute their own work well within their own boundaries. For isolated tasks, native scheduling may be enough. The limit shows up when those tasks become links in a larger supply chain planning process:
SAP S/4HANA updates inventory and order data → SAP CI-DS moves time-series and master data into SAP IBP → SAP IBP runs a planning template → Replenishment reporting refreshes → Operations teams review exceptions
A green status in each tool doesn’t necessarily add up to a trustworthy plan. If one step runs late, partially completes or advances before the required data is ready, the result can still look successful while reflecting the wrong business state. That’s a hard failure mode to catch.
Orchestration is what turns a set of completed tasks into a reliable process. Instead of trusting the clock or a local success message, a modern orchestration platform waits for proof that the process is ready to move forward.
How RunMyJobs supports SAP supply chain planning
SAP IBP and SAP CI-DS are immensely powerful on their own. RunMyJobs by Redwood extracts their full potential by connecting them to the wider enterprise workflows they support. With out-of-the-box connectors for both, you can coordinate planning templates, data movement tasks, monitoring, retries and downstream processes from a central control plane. RunMyJobs’ role as a control plane isn’t limited to SAP; it extends the same coordination to cloud applications, legacy tools and everything else in your landscape, giving your team one place to watch the whole chain instead of piecing it together across systems.
That visibility works best when it’s continuous rather than periodic. RunMyJobs monitors execution in real time and flags SLA risk before a deadline is missed, so a delayed data load or a bottleneck surfaces while there’s still time to act on it, not after a planning cycle has already run on incomplete data.
For SAP IBP, RunMyJobs can retrieve available job scheduling templates, import templates as single jobs or step-by-step chains and submit SAP IBP jobs as part of larger workflows
For SAP CI-DS, RunMyJobs can import existing tasks, execute them, track status through completion or termination and retrieve execution status and logs for centralized monitoring
The planning chain moves because the previous step finished, not because the schedule assumes it should have. That’s especially useful when supply chain planning has to respond to real-time data during demand spikes, supplier delays, inventory exceptions and production capacity constraints. RunMyJobs can trigger the right planning workflow when the business event occurs, stop the process if a dependency fails and alert teams before a late-running cycle affects replenishment or production schedules.
Inside Energizer’s SAP IBP production planning process
Energizer’s planning process is a practical example of this approach working at scale.
Darrin Ward, Business Systems Analyst at Energizer Holdings, explained in a webinar how Energizer optimized and orchestrated its SAP IBP production planning process with RunMyJobs. The team automated many manual steps required to support on-time delivery and coordinated execution across SAP IBP, SAP S/4HANA, SAP CI-DS and Azure Synapse/Data Factory.
RunMyJobs coordinated complex calendaring, proactive alerting and an SAP ECC-to-S/4HANA transition without breaking on-time delivery. That’s the practical test for any orchestration layer: not whether it works when everything is stable, but whether it holds up while the underlying systems are mid-migration.
Treat the pipeline as part of the plan
SAP IBP gives supply chain teams a strong foundation for modern planning. SAP CI-DS provides the required data movement into SAP IBP. Together, they’re central to many SAP supply chain architectures. But the process around them still needs orchestration.
If SAP CI-DS data loads, SAP IBP planning templates, ERP jobs, analytics refreshes and downstream exception workflows are scheduled separately, the planning chain depends on assumptions. If they’re orchestrated together, each step runs based on verified completion and business context.
That’s the difference between a supply chain planning process that usually works and one you can depend on when volatility increases.
I’ve spent nearly three decades working with Redwood Software customers on the jobs, schedulers, scripts and workflows that run large enterprises. What used to be scheduler environments are now hybrid automation estates spanning cloud services, enterprise applications, data platforms and on-premises systems. No two look the same, but the pattern is familiar.
If you already trust RunMyJobs by Redwood with your mission-critical work, you have a foundation many teams are still trying to build. Maybe RunMyJobs runs your SAP processes. Maybe it coordinates your finance workflows, overnight dependencies or reporting chains. Whatever the mix, your most important work already runs through a reliable, governed orchestration layer. The opportunity before you now is to extend that model to the workflows still operating outside it.
In many estates I see, AWS workloads arrive through a different path. For example, a Lambda function solves a specific problem. An AWS Glue job becomes part of a data pipeline. An Amazon S3 bucket turns into a handoff point. A Step Functions workflow grows from a team-level solution into part of a larger business process. Nobody plans these parallel automation models on a whiteboard. They accumulate as practical answers to real needs.
Those AWS services belong in your estate, but the question is whether they should still be operating outside the orchestration model you already trust.
The edges of your coverage
When I talk with customers about AWS, I usually start with a simple exercise: draw the edge of your current RunMyJobs coverage. Not the whole architecture, nor every service inventory — and not the ideal future state. Just the practical boundary between the workflows RunMyJobs already governs and the ones that still operate elsewhere.
That boundary is revealing.
On one side, you have the processes that already inherit the RunMyJobs operating model: dependency logic, calendars, role-based access, approvals, audit history and service-level visibility. On the other side are the workflows that have grown up around AWS, data platforms or SaaS applications on their own timelines. They’re usually perfectly reasonable workflows, but they’re running on a fixed schedule, polling script, local trigger or point integration. Hence, orchestration sprawl happens unintentionally.
I see this most often around processes that cross boundaries: AWS into SAP, AWS into an on-premises application, AWS into a data platform, etc. Each environment has its own logs and controls, but the business process doesn’t stop to respect those boundaries. Hidden risk lives right there.
The next best candidate for moving to RunMyJobs is the workflow that works most days, until it doesn’t.
What’s running on trust?
Look first for workflows that still depend on:
Fixed schedules that assume upstream work finished on time
Polling scripts that check whether a file, job or message is ready
Manual handoffs between AWS and non-AWS systems
Data pipelines where every technical step can succeed, while the business result is late or stale
Those are examples of the AWS workloads RunMyJobs may not know about yet. Some may look small, but if they connect to a business process, they carry operational weight. If they still rely on informal coordination, they carry costs, too.
A bill nobody can quite explain
The cost conversation often starts in AWS, with an instance running longer than expected, a development environment staying up or a batch host waiting most of the day for a few hours of work. FinOps teams can usually identify those patterns. The harder question is what depends on them. No one wants to stop the instance that might still be holding up month-end close or retire the polling script no one fully owns.
That’s why cost optimization is often blocked by uncertainty, not lack of visibility into the bill. When more AWS and hybrid workflows are managed through RunMyJobs, you can see how work starts, waits, runs and completes across the full process. That turns cost from something your team can explain into something your team can act on.
2 versions of the same workflow
AWS-native services are strong building blocks. Step Functions, EventBridge, Lambda, Glue, Managed Workflows for Apache Airflow (MWAA) and other AWS services help teams build scalable, event-driven processes inside AWS. They should keep doing that work.
AWS, SAP, data platforms and on-premises systems each show their own status
The full process has one governed view
A handoff depends on a script, local trigger or manual check
The handoff becomes part of the orchestrated workflow
“Done” means each tool completed its own step
“Done” means the business process completed
Troubleshooting starts with multiple consoles and teams
Troubleshooting starts with the dependency chain
Governance is recreated per tool or team
Governance follows the workflows end to end
Curious whether a specific AWS service is already covered? See the full list ofAWS connectorsRunMyJobs supports today.
The part AI can’t do for you
I get asked about AI in this context more often now: “We’re already putting AI agents on this, so does it matter that some of it still runs outside RunMyJobs?” My answer goes something like: an agent deciding what should happen is not the same as the work safely getting done.
An agent may recommend restarting a failed step, refreshing a data pipeline, triggering a reforecast or escalating an approval. But that action still has to move through the same governed, auditable business processes you already operate. If a workflow sits outside RunMyJobs today, AI doesn’t make it safer by touching it. The handoff still needs ownership, access control, dependency context and an audit trail.
So, coverage matters here too. With capabilities like Model Context Protocol (MCP) support, RunMyJobs can expose existing workflows to AI agents through governed access, rather than giving agents raw access to production systems. Your production workflows remain controlled, governed and scoped to the same permissions and process logic you already trust.
Somewhere in your IT estate tonight, a workload will run that is older than some of the engineers maintaining it. It may close the books, move inventory, trigger replenishment, process settlements or feed a dashboard before the business day starts. It’s survived multiple technology waves because it works. It does what the business needs it to do.
Somewhere else, maybe in the same chain, an AWS Lambda function will fire, or an Azure Data Factory job will transform data. A container task will run. A data platform will load the result into a report that someone depends on tomorrow morning.
Nothing about that combination is unusual anymore. The systems that have carried your business for years now run alongside cloud services built for a different era of speed and scale. Neither is inherently fragile, but the connections between them often become costly points of failure as integration and workflow complexity grow.
No one gets to modernize in a vacuum
Your modernization program is already changing the shape of the business. Applications and workloads are moving to the cloud. SaaS platforms are expanding across functions. Data pipelines are being rebuilt for speed, scale and AI readiness. But modernization doesn’t arrive as a clean cutover. You prioritize the areas with the clearest business value first, while the systems that already run the business keep running.
That is what makes hybrid cloud environments difficult. You’re not replacing one world with another. You’re running decades of technology at the same time across cloud services, enterprise applications, data platforms, on-premises infrastructure and private cloud environments. Every new wave has to connect to what came before it.
Ask anyone who has run a postmortem on a cross-environment failure, and they’ll recognize the typical way this shows up: a legacy batch process completed, an AWS service did what it was supposed to do and both teams have logs that prove their side behaved correctly. Yet, the business process still broke. All the evidence stops at the edge of each system.
That also means the failure lives in a place that doesn’t cleanly belong to anyone. The script holding together the on-prem side and the cloud side may have been written three years ago and never put under version control. It’s both load-bearing and invisible.
Over time, taking the shortest path available on any given day allows your hybrid estate to grow both systems and handoffs simultaneously. You already know what this costs because you’ve probably lived the 2 AM version, when someone is trying to remember who knows what a particular script was supposed to do.
“The job ran” and “the business got what it needed” have never been the same claim, but tooling fragmentation forced you to treat them as if they were. Cloud migration may change where the work runs, but it doesn’t automatically change how it’s coordinated.
The economics of old habits
Cloud changed the cost model faster than most operating models could follow.
In the data center, a lot of habits were rational. Fixed schedules gave you predictability, and extra capacity reduced risk. A server left running overnight didn’t create a new hourly charge, and polling every five minutes was cheap enough to ignore. Move those same patterns into a metered cloud environment, and you’re paying for 20 hours of polling rather than the four hours of true work. For readiness rather than action.
That isn’t getting better on its own. Flexera found that wasted cloud spend rose to 29% in 2026, the first increase in five years. That tracks with the arrival of AI workloads, according to the study.
But cloud cost conversations often get stuck. FinOps can point to all the candidates for waste, and the list is rarely a surprise:
Instances that run long after the workload ends
Environments sized for old peak assumptions
File watchers and polling jobs that could be event-driven
Scripts that exist only to bridge one system to another
Batch hosts no one wants to shut down
The hardest part is proving what happens if you change any of them. You can’t safely stop an instance, retire a script or replace a polling loop if you don’t know which business processes depend on it.
In one recent conversation I had with a senior engineer at a large enterprise assembled through several acquisitions, they described it like this: “Jenkins here, Cron there, an Azure Automation instance nobody fully owns, SAP schedulers running in parallel, Step Functions and Airflow layered in as our data pipelines grew. Each tool has its own team, its own visibility and its own blind spot at the edge. Rather than creating a new version of this problem, AI is raising the cost of the one that’s already there.”
Run a simple version of the math against your own environment.
If you have 500 Amazon EC2 instances averaging $400 per month, and even 20% exist mainly to host, wait for or watch batch work, that’s roughly $480,000 per year before storage, transfer, snapshots or operational overhead. Idle time isn’t theoretical when the meter is always running.
This isn’t just theoretical. We see Redwood Software customers cutting these costs every day.
A global CPG company consolidating decades of manual document processing onto a single orchestration layer and cut document-processing costs by 98%
A large, regulated enterprise bridging legacy batch infrastructure with its cloud environment, removing 59% of peak runtime and 25% of average runtime from its estate without missing a single billing window
Both have hybrid environments where the fix was to govern the handoffs between what already existed, not to engage in another migration.
The way out is not simply to squeeze every workload harder. It’s to change how work starts, waits and stops, and that means solving orchestration tooling sprawl instead of compressing individual runtimes.
Not just another migration
The pain usually shows up when you realize you have many places to schedule work, but no single place to understand the full process. Your instinct could be to just migrate again. Move to a new tool, consolidate, rehost, standardize. And those steps could be necessary, but another migration won’t automatically rebuild the connective layer between systems. If your measure of success is whether every job runs the same way somewhere else, you may have improved the platform while preserving the operating model that created the problem.
Better questions are outcome-based:
Which polling loops can you retire?
Which always-on hosts can start and stop around real workload demand?
Which manual handoffs can become governed workflows?
Which scripts can be replaced instead of recreated?
Which failures can now surface before the business notices?
Which business processes can be monitored end to end, not system by system?
A modern orchestration layer doesn’t ask cloud services, enterprise applications, data platforms and on-premises systems to behave as if they were one technology stack. It gives you one place to coordinate the work between them. The older process can stay. The newer cloud service can stay. But the handoff becomes visible, governed and auditable, with role-based access for who can touch it, an immutable trail of what ran and why and an SLA that predicts a chain trending late while there’s still time to act on it.
That matters now for cost and reliability. And it will matter even more as AI continues to move from pilots to everyday use.
Agents decide, but something else has to execute
The line between cloud modernization and AI readiness is getting thinner and thinner. Most enterprises aren’t building models from scratch. They’re layering agents onto cloud services, managed models and AI-enabled applications and asking those agents to do more than answer questions. Increasingly, they need to trigger workflows, remediate failures and escalate approvals.
But an agent that decides isn’t the same thing as work that gets done. A recommendation to release an order, correct a billing run or kick off a fulfillment path still has to travel through the same deterministic, auditable execution layer as everything else in your hybrid estate.
That’s the part almost nobody has built well, and it’s the same problem as the cost story above, not a separate one. If a handoff is invisible to IT Ops today, it doesn’t become safe or governed just because an AI agent is the one initiating it. The value doesn’t come from the AI alone, but from the blend of judgment and execution and the space between them is where the real work is.
Make decades of tech governable
As you know, forcing every new service into one architectural pattern simply won’t work. The systems running your business have earned their place, and the newer services you’re adopting are there for good reasons: speed, scale, elasticity, AI readiness. You can’t operate in such complexity on trust alone.
Cost is often the first place this becomes visible. Handoffs that were once absorbed as operational noise now show up as workloads you can’t safely optimize, instances you can’t confidently stop, scripts you can’t retire and business processes whose true cost you can’t explain. The more your hybrid estate grows, the more expensive those unknowns become.
More running in the cloud doesn’t mean more modern. What truly supports modernization in the long run is having a modern application and data workflow orchestration platform. It must deliver a governed operating model for each new service, pipeline or AI-enabled workflow to inherit — the same role-based access, audit trail and SLA visibility, applied consistently instead of rebuilt per tool.
Redwood brings old and new together under one orchestration layer. See how you could bemanaging hybrid cloud workflowsacross cloud services, enterprise applications, data platforms and on-prem systems with end-to-end visibility and control.
Ask any automation team where their time goes, and you’re unlikely to get a simple answer. Waiting on a specialist to turn a business process into a working workflow, investigating stopped jobs, trying to figure out why it broke in the first place, answering the same how-to question they answered last week and writing documentation that was meant to be done three sprints ago. Those are just a few you might hear.
We’re talking the automation lifecycle: design, build, document, run, monitor. At every stage, a person has to translate, investigate, explain or configure something before the work can move again. Multiply that by 30 to 50 requests a week, and the backlog can easily grow beyond a manageable level.
AI changes this. Not in a vague “AI will transform everything” way, but in a specific, measurable, this-quarter way.
The AI capabilities inside RunMyJobs by Redwood are designed to enhance the complex automation lifecycle and eliminate productivity leaks: building workflows, handling exceptions, finding answers, writing documentation and more.
Workflow Builder: Describe it, upload it, done
Until now, turning a business process into an automated workflow meant translation. Someone who understands the process had to explain it to someone who understands the platform, usually across several meetings, a ticket and delays. RunMyJobs’ Workflow Builder removes the translation steps entirely.
You have two ways in, and neither requires you to write a line of code.
Prompt it. Type what you want in plain English. “Every night at 11, pull the sales extracts from SAP, validate the file counts, load them into the warehouse and alert the data team if anything fails.” That’s a workflow description, and it’s enough to get started.
It’s not a one-shot prompt machine, either. It’s more of a conversation. Look at what comes back and refine it, add a step, change a schedule, ask it to handle a failure case differently. You shape the workflow the way you’d brief a colleague, iterating until it matches what’s in your head.
Upload a document. This is the one that changes daily life for most teams. Drop in whatever already describes the process, such as a standard operating procedure (SOP), a process description or a runbook. The AI reads it and works out the steps involved in the automation.
Think about what that means. Your organization has years of process knowledge sitting in documents that describe exactly how work should flow. That knowledge used to be inert, but now it can serve as an input. The policy document becomes the workflow.
What happens under the hood matters more than the demo
Plenty of tools can turn a sentence into a flowchart. What makes Workflow Builder useful in a real enterprise is that it builds from your actual environment.
An intent agent parses your description or document and matches it against the real objects you already run: your jobs, your connectors, your partitions, your schedulers. It constructs the job chain and resolves the parameters from what genuinely exists. If it needs something that isn’t there, it doesn’t invent it. Instead, it flags the gap and shows you the closest match. Anything it can’t resolve gets marked as requiring your input.
And here’s the part your IT leadership will care about most: nothing gets created until a human explicitly approves it. The AI drafts, but you decide. Business users get the power to build, and IT keeps the oversight it needs. Best of all, nobody deploys a hallucination to production.
You’re going from policy to production in minutes. The intake queue doesn’t disappear, but it stops being the constraint. And the metric this moves is one your leadership already tracks: time-to-value. How long between someone deciding a process should exist and that process running in production? Today, that’s weeks of tickets and meetings. This makes it a same-day conversation.
Agent Studio: Human-in-the-loop to agent-in-the-loop
Building workflows faster clears the build queue. But the exception queue is still there, and it’s where Agent Studio comes in. Workflow Builder speeds up how workflows are built, and Agent Studio changes what runs inside them.
Even a well-automated workflow has moments that need judgment. A mismatch, an exception, an anomaly. Today, those moments all resolve the same way: the workflow stops, a human gets notified and the work sits in a queue until someone finds time to investigate. The automation itself is fast, but the exception handling is exactly as slow as your busiest person.
That’s the human-in-the-loop model, and for consequential decisions, it’s still the right one. But most of what the human does in that loop isn’t deciding; it’s the pre-work. Pulling the data, tracing where things diverged, figuring out whether this is the usual thing or something new. It’s skilled detective work, and it gets repeated from scratch, every time.
With Agent Studio in RunMyJobs, you build your own AI agents and put them into that loop instead. A custom agent runs as a step inside the workflow, doing the work a human would otherwise have to do before anyone even looks at the problem:
At a minimum, the agent does the analysis. When an exception fires, the agent investigates immediately, in the flow, and hands the human a worked-up case instead of a bare alert. The human still decides, but the hour of digging is already done.
At best, the agent decides, too. For patterns it recognizes, it resolves the exception itself, with a full audit trail, and only escalates the genuinely novel cases.
Judgment doesn’t disappear from the process, but it stops being the bottleneck.
Because these agents run as workflow steps, they inherit everything the workflow already has: the same schedule, the same governance and the same observability, with timeout and cost controls built in. You define the agent’s skills, connect its tools and run it on your preferred AI platform. Your deterministic process keeps running at full speed. The agent handles the moments in between.
The math is simple.
Exceptions get a first pass in seconds instead of hours
Fewer of them ever reach a human
The ones that do arrive with the investigation attached
Watch two numbers in particular: 1) mean time-to-resolution (MTTR), because the investigation now starts the second the exception fires, and 2) SLA attainment, because workflows stop sitting overnight waiting for a human’s first pass.
Product Assistant and Automation Co-pilot: Closing the last two leaks
So, workflows are being built faster, and exceptions are being handled in the flow. That leaves the question queue and the documentation debt, which is where Redwood RangerAI, the AI assistant layer inside RunMyJobs, steps in.
Redwood RangerAI ProductAssistant kills the search-and-escalate cycle. Every automation team has one or two senior people who hold the platform knowledge, and everyone else queues for their attention or digs through documentation that’s a version or two out of date.
Product Assistant effectively puts a senior platform expert in the UI at all times. Junior operators ask questions in plain English and get instant, context-aware guidance for troubleshooting and how-to questions.
The KPI? Time-to-productivity. When every operator has an expert on tap, new hires ramp in days instead of months, and escalation volume drops with them.
Automation Co-pilot takes on the two biggest killers of development velocity: scripting overhead and documentation debt. Developers describe what they need in plain language, and the Co-pilot generates the job scripts. It operates the platform through conversational commands and produces comprehensive technical documentation for any job or workflow in a single click, which means the documentation actually gets written. Anyone who has inherited an undocumented automation estate knows precisely what that’s worth.
Two numbers move here: delivery throughput, because weeks of scripting and documentation compress into hours, and key-person risk, because the knowledge stays with the organization, whether or not the person who built it does.
What you get back
Look at where a typical automation team’s week typically goes: waiting for a specialist to translate a request, answering the same how-to questions, writing scripts that follow well-worn patterns, investigating exceptions that turn out to be the usual thing. And documentation — or, more honestly, feeling guilty not having written it.
The AI capabilities in RunMyJobs take a bite out of every one of those:
Business analysts turn a process description or an SOP into a reviewable workflow draft in minutes, no ticket required
Custom agents do the exception pre-work in the flow, so humans decide instead of dig
Operators get answers in the moment, without escalating or searching
Developers build with prompts and clear the backlog instead of feeding it
Documentation happens in one click instead of never
The compound effect is the real story, and the number that matters most is capacity. Real operational efficiency looks like the same headcount, more delivered, because hours that once went into waiting, digging and re-explaining convert into work that ships. Junior staff start operating like senior staff. Senior staff stop being a bottleneck and start doing the work you hired them to do. The automation backlog — the one every enterprise has and nobody talks about — finally starts shrinking.
None of this removes people from the picture. It removes the queue between people and outcomes. Your team still decides what gets automated and approves what goes live. They just stop paying a translation tax on every single request.
Where you could be headed: Agentic orchestration
This is really about what happens once AI agents stop being a side project and start running inside your production workflows, governed the same way everything else is. If that’s the direction you’re already heading, take a look at how Redwood Software approaches agentic orchestration, making everything above safe to run at scale.
Somewhere in your organization, there is a script. It runs on a cron schedule on an EC2 instance nobody remembers provisioning. It checks an SAP system every five minutes to see whether a job has finished, and when it has, it calls an AWS API to kick off whatever comes next. A contractor wrote it two years ago. It has never been in version control, nobody has reviewed it since and it breaks every time an upstream format changes.
That script exists because of a problem almost every enterprise running AWS has. It sits between the AWS boundary and everything on the other side of it. Multiply that script by every system boundary in your enterprise, and the scope of the problem comes into focus.
Hybrid is the architecture, not a phase
For a decade, the industry narrative said cloud consolidation was the destination and that on-premises systems, ERP estates and private infrastructure were transitional states waiting to be resolved. Reality settled somewhere else.
Gartner forecasts that 90% of organizations will adopt a hybrid cloud approach through 2027. SAP contracts run for decades. Regulated workloads stay on-premises. AWS grows alongside those systems instead of replacing them.
Hybrid is simply the standard operating model of the modern enterprise. Few enterprises seriously considered going all-in on AWS. The real work has always been getting AWS to work alongside the SAP systems, on-premises infrastructure and other platforms already running the business.
The building blocks aren’t the problem
AWS services are excellent at what they do.
Amazon EventBridge is an elegant way to route events between applications
Amazon SQS has been reliable for nearly two decades
Amazon ECS and AWS Fargate made container operations accessible to teams that had no interest in running Kubernetes
AWS Step Functions, AWS Lambda, Amazon Managed Workflows for Apache Airflow (MWAA) and Amazon Aurora are best-in-class building blocks for serverless orchestration and distributed applications, and enterprises are right to build on them
The problem starts the moment a workflow crosses the AWS boundary.
Think of an AWS Batch job that can’t start until an SAP financial close completes, or a goods receipt in SAP that should trigger downstream AWS processing in real time, or a data pipeline that begins with an on-premises extraction and ends in Snowflake. AWS-native orchestration tools like Step Functions and EventBridge Scheduler work well inside the boundary, using a state machine and the Amazon States Language to manage task state, wait states and retry logic across a workflow. But they can’t see an SAP job chain, enforce a dependency on an on-premises process or govern an end-to-end SLA that spans both worlds.
So enterprises bridge that boundary the only way they can: with custom scripts, scheduled polling, Lambda wrappers and manual coordination. A team writes one script to solve a specific handoff. Another team writes a different script for a different handoff six months later. None of it appears on an architecture diagram, and after a few years, the integration layer holding SAP and AWS together is really just a pile of undocumented cron jobs.
The disconnect has a price tag
Deloitte’s 2026 Global Technology Leadership Study estimates that technical debt absorbs 21% to 40% of total IT spending, and the custom integration code accumulating at every system boundary adds to that figure with every new program. Flexera adds another dimension: 29% of cloud spend is wasted on idle or underutilized resources, often because nothing is orchestrating when infrastructure should be running.
The more visceral cost shows up at 9 AM on a Tuesday. A dashboard shows stale data. Was the failure in SAP? In the AWS pipeline? In the handoff between them? SAP logs live in one world, CloudWatch in another and the script connecting them logs to neither. Multiple teams are pulled in, and what should have been a five-minute diagnosis becomes an entire afternoon. Because the trigger was a polling schedule, the failure happened hours before anyone noticed.
Every new AWS service an enterprise adopts adds another boundary, another handoff, another script. The estate keeps growing. The distance between AWS and everything it depends on never closes.
A different model: An orchestration layer above the blocks
With orchestration, it’s a simple division of labor: AWS runs the cloud, and the orchestration layer runs the enterprise.
RunMyJobs by Redwood sits above the AWS execution tier. It doesn’t compete with EventBridge or Step Functions; it extends them to the systems they can’t reach. In this model, an SAP business event doesn’t wait for a polling window. RunMyJobs orchestrates the wider business service and publishes natively to EventBridge or populates application queues in SQS the moment the business event occurs, and the customer’s event-driven and serverless architecture takes it from there.
A container task on ECS, a Batch job and an Airflow DAG on MWAA can also each become governed steps in a chain that started inside SAP and might end with Snowflake, Microsoft PowerBI, Box or ServiceNow, with one dependency map, one SLA policy and one audit trail across the whole thing.
This works because the integration is native at every layer of the AWS stack.
The application layer:Connectors for EventBridge, SQS and SNS make orchestrated workflows first-class participants in event-driven architectures. They publish events, manage queues and fan out notifications using the same primitives AWS applications use to talk to each other.
The compute layer: EC2, ECS and Fargate, Batch and Lambda run as governed execution steps, with IAM roles controlling exactly what each one is allowed to touch. Instances start and stop around workloads, containers run and are monitored to completion and exit codes propagate into the dependency chain.
The data layer: S3, DataSync, Glue, AppFlow, MWAA and Amazon RDS govern the pipeline end to end. They ingest from on-premises and SaaS sources into a data lake or warehouse, trigger transformations, execute SQL and monitor Airflow DAGs as steps in a larger, governed whole.
Walking the chain: One retailer’s overnight run
Here’s what that looks like end to end.
Picture a retailer whose stores send the day’s transactions to SAP Customer Activity Repository overnight, where every cash register and every store is validated and audited. Merchandising needs those numbers on dashboards before the morning trade meeting.
The moment SAP confirms the data is complete, RunMyJobs publishes a “POS data ready” event to EventBridge, and every AWS application subscribed to it, from replenishment to store operations, knows instantly. There is no fixed 2 AM schedule and no guessing. In the same governed chain, RunMyJobs places a message on an SQS queue for each store’s dataset, and processing workers consume them at a pace downstream systems can handle.
A containerized ECS task enriches the audited data. The operational reporting database on RDS is refreshed, started for the load and stopped when idle, because nobody should pay for a database running at 4 AM for no reason. An MWAA pipeline aggregates by store, region and product line. The final datasets land in Snowflake, and the dashboards are live before the meeting starts.
Seven services. One SAP system of record. One dependency chain. If a store’s data never arrives, the team knows right away, rather than when the merchandising director is staring at incomplete numbers.
Nothing in that chain required a polling script or custom middleware at the SAP-to-AWS boundary.
Every migration wave, the same problem
One group experiences this disconnect as an active, funded problem rather than chronic background pain: teams in the middle of a migration to AWS.
Migrations move in waves, sequenced by risk and dependency. Compute and databases typically lift and shift first, and workloads modernize onto containers, serverless and managed pipelines as each one is ready. Every wave that introduces a new AWS service also introduces a new dependency that the old on-premises scheduler can’t reach, which leaves teams with the same choice each time: stop the wave and rebuild the connection back to SAP and on-premises, or move on and leave it broken. Either way, the orchestration layer becomes the migration’s bottleneck rather than the workload move itself, and every custom script written to patch the boundary is more technical debt.
When every AWS service in the migration path already has a native connector, that choice disappears. Each wave goes live with its dependencies already governed. The migration stays on the timeline the business case promised, and the orchestration layer is the one thing that never needs to be re-evaluated at the next wave.
Gut check: What to ask before your next AWS migration wave
1. The last time a workload moved to a new AWS service, what happened to the SAP or on-premises dependencies feeding it? If the answer involved rework, custom scripting or a delay, then every new AWS tier you adopt means re-solving the same orchestration problem.
2. If a critical cross-system process failed overnight, how quickly would you know, and how many tools would you need to open to find out why? If the honest answer is “when someone notices downstream” and “at least three,” the space between your systems has no owner.
3. How much of your engineering capacity goes to maintaining integration glue rather than building anything new? Deloitte’s estimate that technical debt absorbs 21-40% of IT spending holds true for most enterprises, and a meaningful share of it lives in exactly these scripts.
RunMyJobs will change your answer to all three. It’s the SaaS-native orchestration platform with native connectors across the AWS stack, deep SAP integration as the only SAP Endorsed App in workload automation and orchestration and a 99.95% uptime SLA. Every connector works without deploying infrastructure. Get a demo of RunMyJobs for AWS.
