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What’s the main reason for adopting agentic automation?
At its core, it’s to eliminate manual effort in routine tasks and free senior engineers to focus on higher-value work.
Agentic automation promises to elevate the productivity gains that have made automation a core part of enterprise IT. By enabling workflows that can interpret and respond dynamically to requirements, it aims to remove the remaining human involvement from repetitive, time-consuming tasks that still consume a significant portion of IT teams’ workloads.
Yet in many organizations adopting agentic automation for the first time, the opposite happens.
As AI-driven automation expands, senior engineers often find themselves even more involved, not less. This is because, in poorly designed automation environments, AI-driven systems can introduce a level of unpredictability that forces senior engineers to review automation changes, troubleshoot failures, validate outputs, and act as the final line of defense when issues arise. Rather than scaling productivity, agentic automation becomes another system that only experts can safely manage.
When agentic automation increases dependency on senior engineers instead of enabling effective delegation, something in the model is fundamentally broken.
The Productivity Paradox of Agentic Automation
On the surface, agentic automation promises a big boost to productivity. More workflows are running. More tasks are executed automatically. More systems are connected.
But productivity isn’t measured by activity; it is measured by outcomes.
Teams often discover that while agentic automation can be applied to many of their routine tasks, it can also increase remediation efforts when things go wrong.
Without the right guardrails, governance, and logging in place to keep AI-driven automation in check, errors become difficult to trace and even harder to resolve without deep expertise. Issues that might once have been isolated now propagate rapidly across systems.
The problem is not only the errors and security risks this creates, but also the additional demand it places on senior engineers. Instead of delegating routine work to AI-assisted staff or fully autonomous, zero-touch workflows, senior engineers have to be on hand to monitor, approve, and troubleshoot things at every step.
How Agentic Automation Quietly Increases Dependency on Experts
The core issue is not agentic automation itself, but the governance strategy around it.
In many large organizations, agentic automation is not introduced into a clean environment. Instead, it lands on top of years of fragmented automation practices that were never designed to support the autonomy of agentic automation at scale.
Scripts live in different tools, owned by different teams, written to different standards. Execution is spread across schedulers, RPA platforms, CI/CD pipelines, cloud services, and ad-hoc scripts run manually when needed. Guardrails, where they exist, are locally applied and inconsistent. Logging varies wildly, if it exists at all. Access controls are often embedded directly into automation workflows rather than enforced universally.
This kind of automation environment can function tolerably well when humans remain in the loop. However, it also promotes an outsized reliance on senior engineers who know how scripts work, which work well, and which need close supervision. They compensate for weak governance with experience and caution, but this creates a serious bottleneck that limits how far automation can scale.
Rather than resolving this issue through zero-touch autonomy, poorly governed agentic automation exacerbates it. AI agents don’t understand organizational history, tribal knowledge, or “the way things are usually done.” They lack the “this might be a bad idea” instinct that even a junior engineer has.
Instead, they operate at high speed across systems, treating every available pathway as a valid option, without the contextual risk awareness that senior engineers apply intuitively.
As a result, differences in how automation is created, executed, and controlled suddenly matter a great deal. Without standardized execution parameters, an agent may behave flawlessly in one run and then fail in another, triggering a cascade of unintended side effects. Without robust, built-in guardrails, a misconfigured agent can operate across systems and databases far beyond its intended scope.
When something goes wrong, the lack of comprehensive oversight becomes painfully clear. Execution paths are difficult to reconstruct. Logs are incomplete or fragmented across platforms. It’s unclear whether an issue stems from the agent’s configuration, the data or tools it interacts with, or gaps in the environment’s guardrails. Troubleshooting becomes slow, reactive, and heavily dependent on deep expertise.
Faced with this uncertainty, organizations respond in predictable ways:
- They restrict who can interact with agentic automation.
- They require senior engineers to approve changes, monitor executions, and intervene when outcomes look wrong.
- They block integration with core infrastructure, citing elevated risks and unclear value.
Adoption and delegation of agentic automation capabilities across teams stalls, not because teams don’t want it (of course they do), but because the automation environment doesn’t support it safely.
This is the point where a fundamental change is needed.
From Fragmentation to Centralization: Securing Agentic Automation ROI
Agentic automation can’t succeed as an overlay onto fragmented practices. To deliver sustainable value, it requires a holistic, centralized automation strategy that treats automation as a consistently governed infrastructural capability rather than a loose collection of tools.
At the core of this shift is standardization:
- Automation must be created and executed within consistent parameters, regardless of the underlying system or platform.
- Guardrails and access controls should be enforced by design, not retrofitted or left to individual scripts and workflows.
- Oversight and logging need to be comprehensive and centralized, so execution can be clearly understood, audited, and trusted.
This lays the foundation for controlled delegation. Teams need an efficient, governed interface through which pre-approved automation actions can be safely executed, not only by senior engineers, but also by junior staff and autonomous AI agents handling routine work.
A centralized automation approach, built on this level of standardization, creates an environment where approved automation workflows can be shared across the organization to boost productivity without introducing misconfiguration, insecure design, or unnecessary duplication. Delegation expands capability without expanding risk, while materially reducing the oversight and remediation burden placed on senior engineers.
Without this shift, agentic automation will continue to concentrate responsibility at the top, increasing workload for senior engineers and limiting productivity gains. With it, it can finally scale in a way that reduces effort, improves quality, and delivers on its original promise.
Enabling Safe Delegation with ScriptRunner
ScriptRunner enables organizations to govern agentic and scripted automation in a way that restores trust and unlocks real productivity gains. By centralizing control over execution, permissions, and operational guardrails, teams can clearly define what automation is allowed to do, and who is authorized to run it.
ScriptRunner’s centralized agentic automation and orchestration platform allows senior engineers to delegate automation capabilities with confidence, knowing that outcomes will stay predictable, auditable, and effective as operations scale.
The result is what agentic automation was always meant to deliver: higher-quality work, faster-moving teams, and experts who spend their time scaling the organization rather than supervising automation.
If agentic automation is creating more work for your senior engineers, it’s time to change how it’s governed. Book a meeting with ScriptRunner to discover how we enable safe delegation that turns automation into a true productivity multiplier.