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As organizations seek to take the next leap in automation productivity, agentic automation has moved quickly from theory into practice. Across enterprise IT, AI agents are already being tested to triage incidents, query databases, orchestrate remediation workflows, and provide feedback in real time.
In controlled test environments, the results can be impressive. Agents act quickly and operate with a level of autonomy that exceeds traditional automation, reducing the routine manual workload that has long burdened IT teams.
Yet despite successful pilots, many organizations struggle to take the step into full-scale integration with live infrastructure.
According to Gartner, more than 40% of agentic AI projects are expected to be canceled by the end of 2027, largely because existing infrastructure and governance models are not ready to support agentic automation.
Another Gartner survey found that while 75% of organizations have piloted or deployed some form of AI agent, only 15% are seriously considering or deploying fully autonomous agents. Governance gaps, organizational maturity, and risk concerns are cited as the primary barriers.
These figures underscore a persistent pilot-to-production gap that isn’t necessarily about a lack of ability in agentic automation to handle routine IT tasks. Rather, in many cases, the operational ecosystems that large-scale organizations have fostered are simply not designed to support the speed, autonomy, and flexibility that agentic automation introduces. This lack of readiness leads to inconsistent performance and heightened security risks, making integration with core systems untenable.
Moving from experimentation to integrations that deliver sustainable long-term value therefore requires a fundamental shift in how automation is set up, governed, and overseen.
Why Agentic Automation Can Feel Safe in Labs, but Dangerous in Production
In the experiment phase, AI agents operate in environments that are intentionally forgiving. Their scope is limited to well-defined tasks, systems are isolated from wider production data, and engineers are watching closely at every step.
Success here proves that AI agents are fundamentally capable of the reasoning and execution required for operational work.
However, the same speed, flexibility, and autonomy that shows value in testing can quickly create instability in production, where systems are more complexly interconnected, data is sensitive, and mistakes carry real consequences.
The primary weakness that this exposes within many organizations’ pre-existing approach to automation is fragmentation.
Most enterprise automation environments have evolved organically rather than through deliberate design. Scripts and workflows are built to solve immediate problems, shaped by team-specific tools, priorities, and policies. Moreover, access controls are often overly broad or inconsistently applied, with broad privileges tied to administrative accounts rather than limited to those required for each task. Over time, this results in environments where:
- Execution logic varies across teams and systems.
- Ownership and accountability are implicit rather than clearly defined.
- Human judgment compensates for architectural gaps.
- Governance relies on tribal knowledge and manual oversight.
Human operators can work effectively under these conditions because they understand what changes are safe, which credentials are sensitive, and which databases should be accessed to resolve a particular task.
AI agents do not possess this implicit understanding.
Instead, they act freely within the permissions and execution paths they’re given, regardless of how broad these may be. Given the complexity of enterprise IT environments, this often produces long-term problems that weren’t apparent in testing and definitively hamper the ROI that agentic automation can deliver:
- Agents trigger actions based on logic a human would question.
- Execution outcomes become harder to interpret or trust.
- Manual supervision requirements increase rather than decrease.
- Organizational confidence in automation declines, slowing optimization and adoption.
Ultimately, the core challenge is not with the maturity of the AI agents themselves, but instead with the operational debt embedded in the existing automation practices of many large-scale organizations.
Without an automation strategy built around clear governance, well-defined boundaries, and consistent execution logic, agentic automation cannot operate safely or reliably in production.
What Production-Ready Agentic Automation Looks Like
To move agentic automation into core IT operations safely, organizations must rethink how automation is designed and managed.
The objective is no longer simply to automate individual tasks more quickly, but to ensure that any future autonomous execution is predictable, consistent, and secure enough at scale to function as part of critical infrastructure.
Reports consistently show that agentic automation will only become a dependable part of enterprise IT when it is anchored around a centralized execution and governance model.
Crucially, this is designed not to limit autonomy, but to enable it. When clear guardrails are built into automation, both human operators and AI agents can act quickly and decisively to solve real problems, without introducing risk. Trust in automation is restored not through constant oversight, but through intentional design that ensures safety, consistency, and efficiency by default.
When automation reaches this level of maturity, it can support true zero-touch, end-to-end workflows that IT teams have long sought for.
Getting there requires robust standardization of the logic, guardrails, and policies that govern automation, whether triggered by a human, a schedule, or an AI agent. Several foundational capabilities make this possible:
1. A Single, Unified Execution Model
Production-ready agentic automation depends on consistency. A unified execution model removes ambiguity around how scripts run, which identities are used, and how outcomes are produced. Rather than forcing AI agents to navigate a patchwork of tools and execution environments, a unified model provides a stable, well-defined, and standardized execution pathways that they can thrive in.
This consistency significantly reduces operational risk while increasing productivity. Engineers no longer need to account for errors introduced by divergent execution paths across teams or platforms, and agents can operate autonomously with confidence.
2. Centralized Governance and Policy Enforcement
Agentic automation can only scale safely when governance is enforced by default, rather than delegated to individual teams or tools. Centralized policy enforcement ensures that every automated action adheres to enterprise-wide least-privilege principles, compliance requirements, and operational standards automatically.
By embedding governance into the automation environment itself, the burden is removed from engineers to manually interpret or enforce rules. This also prevents AI agents from exploiting access gaps created by misconfigured tools or workflows.
3. Built-In Traceability and End-to-End Visibility
For autonomous execution to be trusted, every action must be fully logged and observable. Production-ready environments establish end-to-end traceability that captures who triggered an action, how it was executed, which permissions were used, and what outcome was produced.
This level of visibility enables faster troubleshooting, simplifies audits, and strengthens confidence in automation. Over time, it also supports continuous improvement by making inefficiencies and failure patterns easier to identify.
4. Reusable Automation Building Blocks
Scaling agentic automation is greatly aided by moving beyond one-off scripts toward reusable, standardized automation components. When workflows are composed from governed building blocks rather than custom logic each time, duplication and maintenance effort are significantly reduced.
This approach benefits both humans and AI agents. Engineers can build and deploy automation more efficiently, while agents can safely orchestrate proven components to handle emerging requirements. The result is automation that compounds value over time instead of creating additional operational debt.
5. Safe Delegation Without Direct System Access
The greatest productivity gains occur when automation can be safely shared beyond a small group of administrators. A centralized approach can enable self-service access to pre-approved scripts and workflows through a controlled interface.
This preserves security while unlocking new avenues of scalability. Safe automation becomes accessible across larger swathes of teams and roles without requiring direct system access, allowing both humans and agents to leverage its benefits.
From Experimentation to Infrastructure with ScriptRunner
When agentic automation becomes well-governed, fully traceable, and safely accessible through a centralized framework, the conditions are set up for successful integration with core operational processes, and delivery of the value that it promises.
ScriptRunner is the leading agentic automation and orchestration platform for Microsoft ecosystems, empowering enterprises to address vital infrastructure challenges with centralized, secure, and policy-driven automation.
If you’re ready to move agentic automation beyond experimentation and into real IT operations, book a meeting with ScriptRunner today.