Agentic AI vs. generative AI: Defining their roles in workload orchestration

Enterprises use many types of artificial intelligence to support automation, from predictive models to copilots. Two approaches dominate current conversations: generative AI (genAI) and agentic AI. Both are reshaping how leaders think about automation and data-driven operations. While generative AI systems excel at creating new content from existing data, agentic AI systems take it further — they’re built to pursue specific goals on their own, reasoning through plans and actions as they go.

Learn what each AI approach does best, how they differ and where they complement each other. We’ll explore why the distinction matters for workload automation and how you can prepare your orchestration platform for a future of real-time autonomous decision-making — with examples from IT operations, supply chain management and SAP environments.

Generative AI refers to AI models that produce new content, including text, images, code or other structured outputs. These systems typically use large language models (LLMs) built with deep learning algorithms to recognize patterns in massive datasets. When given user input, they generate relevant new content. You can see it in action any time a developer asks a chatbot to draft a script or a finance analyst uses AI to summarize reports.

Prominent examples of genAI tools include OpenAI’s ChatGPT and chatbots embedded in enterprise platforms.

Core strengths

The primary strength of generative AI is accelerating content creation and code generation. These AI models can draft technical documentation, write job scripts, summarize incident reports and explain complex logs in natural language. In simple terms, they make it faster to turn ideas into usable artifacts.

For instance, a genAI tool could generate a script for an SAP batch job or provide a concise explanation of its function and dependencies. This capability is invaluable for streamlining repetitive authoring work and capturing institutional knowledge as part of broader digital transformation efforts.

Practical limitations for automation

By design, generative models are reactive and lack autonomy. They respond to prompts but don’t independently manage outcomes or orchestrate multi-step workflows. A generative model doesn’t know how to sequence actions or manage dependencies across tools and APIs, at least not without extra layers of logic around it.

Its outputs are based on patterns in its training data, which can lead to misinformation or outdated guidance without diligent human oversight. It’s powerful at creating things, but doesn’t engage in problem-solving on its own.

Agentic AI describes AI-powered systems that convert high-level objectives into concrete actions. Think of an agent like a highly efficient project manager. An AI agent perceives its environment, plans a course of action, executes steps using various automation tools and APIs, observes results and adapts its plan accordingly.

These systems are goal-oriented, moving beyond simple task execution toward autonomous process optimization, the same way the onboard computers in autonomous vehicles adjust to changing road conditions. They combine machine learning (ML) with procedural control to pursue objectives with minimal human input.

Characteristics

Agentic AI systems apply reasoning, planning and decision-making to choose actions that align with business objectives. Instead of following rigid scripts, they can:

• Dynamically adjust schedules
• Re-prioritize tasks
• Handle exceptions
• Coordinate processes across disconnected systems

Imagine an SAP agent tasked with system maintenance. It can pause job queues, perform updates and restore operations without direct supervision.

Why it matters for IT operations

Agentic AI improves operational resilience and adaptability by handling complex tasks in dynamic environments where traditional scripts fail. These systems adapt to real-time conditions, so they reduce errors and keep critical processes aligned with business priorities. The result is a more autonomous, self-correcting layer of enterprise AI operations.

The fundamental difference between these two AI technologies lies in their purpose. Generative AI creates content, while agentic AI takes action. This distinction has significant implications for enterprise automation.

In automation, generative AI helps with drafting and explanation. It can generate job definitions, create alert notifications and document sample API calls. These capabilities shorten development cycles and help keep documentation aligned with evolving technology. For example, a generative model can produce a high-quality guide for a materials planning process, including pre-checks and rollback procedures.

Agentic AI, on the other hand, supervises and adjusts the actual workloads. It can intelligently throttle resource-intensive processes, shift start times to avoid contention and switch to a secondary data center if a deployment fails. In a supply chain, an agent can detect a shipment delay and automatically re-sequence dependent manufacturing jobs to minimize disruption. This is the operational layer where autonomous decision-making provides the most value.

Agentic AI drives value in scenarios where quick decision-making and cross-system coordination are crucial.

IT operations and incident response

Instead of simply alerting an operator about an unhealthy database, an agent can be tasked with ensuring the database remains healthy. It can autonomously monitor, diagnose logs, kill problematic processes and verify that performance has returned to normal without human intervention. As you can imagine, that breadth of capability can transform IT teams from reactive firefighters into proactive enablers.

Supply chain orchestration

When a critical shipment is delayed, an agent can assess the impact on production schedules, check inventory in alternate warehouses and autonomously initiate stock transfers to prevent a factory shutdown. It adapts the process in real time based on new information, ensuring business continuity.

Finance and business process automation

In an order-to-cash process, if a credit check returns an “undetermined” status, a traditional workflow would halt. An agentic system understands that the goal is to approve the order. It can query the customer’s payment history, see that they’re in good standing and proceed to approve the order while logging its reasoning for auditability.

Rather than competing, the two types of AI form a partnership — one focused on creation, the other on coordination. Generative AI is like the strategist sketching out ideas, while agentic AI is the operations lead who makes sure those ideas happen on time and within guardrails.

GenAI acts as a creative engine to produce scripts, plans and recommendations. Agentic AI then serves as the execution engine that takes that plan, makes decisions, orchestrates the necessary tools and closes the loop by verifying the outcome. A generative model could propose a new SAP batch process, and an agentic system could schedule, monitor and adjust that workflow automatically when conditions shift.

The collaboration doesn’t stop at execution. When generative systems observe results from autonomous agents, they can refine future content to improve accuracy and relevance. This feedback loop is central to enterprise AI adoption: agents keep automation running smoothly, and generators learn from those operations to improve future automation logic. In other words, generative AI gives voice to intent, and agentic AI gives that intent momentum. Together, they enable systems that learn from every outcome.

Beyond technology, generative and agentic AI applications are reshaping how teams work. Generative models reduce manual authoring, while agents free talent from constant monitoring. As these systems mature, organizations will evolve toward AI ecosystems that blend human and machine intelligence. The result is intelligent digital transformation, where every automated action connects back to business intent and measurable value.

Adopting agentic AI safely requires more than just deploying new models. It demands rethinking governance, accountability and observability. The most effective organizations are adopting human-in-the-loop structures to combine autonomous decision-making with human judgment.

Agentic systems can now operate across thousands of workflows simultaneously, so defining when and how humans intervene is key.

To prepare, leaders should:

• Evaluate readiness: Identify current workflows that can benefit from agentic AI uses, such as exception management, incident response and end-to-end process optimization.
• Modernize governance: Implement transparent audit trails and explainability frameworks for all AI-driven orchestration decisions.
• Integrate AI assistants: Empower teams to collaborate with AI directly through conversational interfaces for faster troubleshooting and decision-making.
• Establish continuous learning: Feed operational data back into models to refine both generative and agentic AI performance over time.
• Prioritize adaptability: Build architectures that support evolving models, tools and security standards without disrupting ongoing operations.

The goal is not to replace humans but to amplify their impact through AI capabilities that make workflows more adaptive and transparent.

• Use generative AI when you need new content — scripts, templates, or explanations that help people move faster
• Use agentic AI when you need coordinated action — end-to-end execution that adapts across systems in real time
• Use both for a closed loop where authored knowledge improves operations and operational signals improve authored knowledge

The power of this pairing reflects the future of AI in enterprise automation.

Redwood Software views agentic AI as the engine of the next transformation in enterprise automation: the autonomous era. Redwood’s automation fabric solutions support interoperability, future-proof technology strategies and empower enterprises to move from simply automating tasks to orchestrating intelligent business outcomes.

Find out more about agentic AI and the role of orchestration in future-proofing your enterprise: Visit the AI hub.