The evolution of agentic AI in enterprise automation
Contents
Contents
1. What's happening in automation?
Artificial intelligence (AI) is no longer confined to predicting outcomes or generating content — the generative AI (genAI) and machine learning (ML) models you’re probably familiar with. A new path is emerging, one defined by agentic AI systems that reason, plan and act with intent.
Unlike traditional AI agents that execute fixed instructions, agentic AI works as an adaptable and autonomous layer. With it, you can achieve fully AI-powered orchestration for enterprise workloads. That’s when automation doesn’t just follow a script but understands goals and adjusts to meet them. This shift also invites stronger human oversight to keep autonomy aligned with policy.
Just as workload automation evolved from simple job scheduling to Service Orchestration and Automation Platforms (SOAPs), agentic AI represents the next stage of a technology that is revolutionizing business operations and changing the meaning of digital transformation.
2. Early foundations: Task scheduling and rule-based automation
If you want to find the roots of enterprise automation, you have to go back to something surprisingly simple: the cron job. These early scripts and platform schedulers were built to automate repetitive tasks, such as database backups, log rotations and batch processing. They worked reliably, but only if everything went according to plan.
Like early symbolic AI, these systems were rule-based and deterministic. They didn’t adapt to change or handle complexity, but followed explicit instructions and produced predictable, one-dimensional results.
Their limitations became obvious as organizations adopted more cloud-based and data-driven architectures. Without cross-system orchestration, IT teams had to manually intervene whenever a dependency failed or a data source lagged behind. These systems could automate, but they couldn’t think. They lacked the adaptability and context-awareness that define intelligent automation today, especially when tasks break into subtasks across multiple platforms. In software development, for example, a missed artifact or late dependency could stall an entire release plan.
3. When automation learned to think in systems
The next evolution came with workflow orchestration, which connected multiple jobs into coordinated workflows across applications and platforms. Instead of isolated scripts, organizations began designing end-to-end workflows that shared real-time data and triggered actions based on business events.
Workload automation tools started spanning hybrid ecosystems, from legacy systems to cloud services, ERP platforms like SAP and modern APIs. This development paralleled progress in AI algorithms, which were becoming more context-aware and iterative.
SOAPs, as defined by Gartner®, formalized this stage by introducing scalability, observability and interoperability as core design principles. These systems could handle complex workflows, coordinate across departments and reduce manual oversight. Automation had matured — not just running tasks, but managing interdependencies, resolving bottlenecks and supporting business continuity.
The leap mirrored the transition in AI from symbolic logic to neural networks, deep learning and reinforcement learning, where systems began learning from feedback instead of relying solely on prewritten rules.
4. The rise of agentic AI
Enter agentic AI: a new generation of autonomous agents that use reasoning, planning and continuous learning to handle complex challenges. These AI agents extend the capabilities of traditional AI by maintaining memory, analyzing context and coordinating across multiple domains.
They do this by combining the power of large language models (LLMs), ML frameworks and natural language processing (NLP) to understand and react to dynamic environments. In practice, that means an agent can analyze datasets from multiple sources, make real-time decisions and adjust actions based on outcomes — a far cry from static automation scripts.
Agentic AI’s evolution can be seen in three overlapping phases:
- Reactive agents: Responding to specific inputs or triggers
- Proactive agents: Predicting needs and taking initiative
- Goal-oriented systems: Reasoning, learning and optimizing toward desired outcomes
In real-world use cases, agentic AI is already transforming operations. In healthcare, it can analyze patient data to schedule care pathways automatically. In supply chain management, it balances logistics flows when real-time data shows delays. In cybersecurity, it coordinates AI-driven incident response with minimal human intervention. In customer support, AI assistants triage cases and trigger runbooks across ITSM tools. These are not static bots but intelligent partners embedded in enterprise ecosystems.
5. Workload automation meets agentic AI
Pairing workload automation with agentic AI, you get autonomous orchestration — systems that stay a step ahead rather than waiting for instructions.
With SOAPs, AI-powered orchestration adds a layer of intelligence that keeps business processes aligned with enterprise goals. So, instead of just reacting to job failures, these agents use sophisticated problem-solving skills to reason through issues and take proactive measures to keep systems running efficiently.
For example:
- Real-time workload balancing ensures continuous uptime across distributed systems
- Intelligent error handling identifies anomalies, isolates faulty dependencies and triggers corrective actions
- Predictive resource allocation uses historical patterns and reinforcement learning to scale infrastructure before demand peaks
These capabilities bring agility and scalability to enterprise operations. It’s the difference between reacting to problems and preventing them — automation that doesn’t just execute tasks but orchestrates outcomes intelligently.
6. An emerging intelligence layer
The newest generation of automation fabrics weaves together APIs, data pipelines and workflows across cloud, on-premises and hybrid environments. When combined with agentic AI, these fabrics become the nervous system of the enterprise, continuously sensing, reasoning and adapting to the business environment.
You can think of this emerging intelligence layer as the central control plane for this new way of working. Agentic systems operating within these automation frameworks enable:
- Contextual awareness across complex, interconnected environments
- Cross-domain orchestration between critical systems like SAP, Oracle and multi-cloud services
- Continuous optimization based on performance metrics and predictive analytics
These functions are the building blocks of true, real-time orchestration and AI-driven decision-making.
7. Insights from Gartner on automation and AI convergence
Gartner has tracked the evolution of automation platforms for SOAPs. These platforms are a significant leap beyond traditional job schedulers, acting as a central hub to provide a “single pane of glass” for managing complex workflows across hybrid IT environments, from on-premises systems to multi-cloud services. By orchestrating data pipelines, business processes and infrastructure, SOAPs establish the stable, governed foundation necessary for innovation.
The core principles of a SOAP — scalability, observability and interoperability — create the ideal groundwork for integrating next-generation AI. As Gartner research suggests, the convergence of AI with these robust orchestration platforms is critical for scaling automation responsibly.
By leveraging a SOAP, organizations can ensure that as they introduce agentic AI, they do so within a transparent framework that provides the necessary guardrails and capabilities for human oversight. Preparing for this shift means building on the capabilities that SOAPs provide to create the feedback mechanisms and high-quality data pipelines that intelligent systems require to learn safely over time. Treat this as a staged initiative rather than a single deployment.
8. The next phase of intelligent automation
We are entering the age of agentic orchestration, where automation doesn’t just execute but evolves. As enterprises begin to integrate agentic AI into their operations, they will shift from managing workloads to managing intelligence itself.
For IT and operations leaders, the benefits of agentic AI are clear:
- Agility: Reacting to change instantly instead of waiting for a scheduled process to run
- Efficiency: Optimizing compute, storage and data transfer without manual tuning
- Resilience: Ensuring end-to-end reliability even when conditions shift unexpectedly
Agentic AI does not replace traditional automation; it enhances it by transforming workflow management into a continuous cycle of learning and improvement. The practical result is fewer interrupts for humans, fewer brittle handoffs and more consistent delivery against service-level agreements (SLAs).
This aligns with Redwood Software’s vision of automation fabrics, where AI capabilities can be seamlessly integrated into orchestration to create adaptive, autonomous ecosystems that can reason, act and optimize with purpose. It also sets the stage for AI solutions that span departments without raising integration risk.
Find out more about agentic AI and the role of orchestration in future-proofing your enterprise: Visit the AI hub.
