Agentic AI: Powering the Next Evolution in Data and AI Lifecycle
Imagine an analytics world where Agentic AI autonomously navigates the entire data and AI lifecycle. It accesses raw data, cleans and transforms it, identifies the best algorithms, builds and validates models, deploy them seamlessly into production, and monitor model performance over time—often at a scale and speed beyond human capabilities. All that’s left for humans is to interpret insights in real time, orchestrate AI systems, and make strategic decisions while AI Agents handle the rest. Goodness!
The Evolution of AI Systems
The evolution from chatbots to Agentic AI represents a significant leap in AI capabilities, moving from basic task automation to autonomous, goal-oriented systems. Chatbots were the initial foray into conversational AI that provide single-turn responses, followed by AI assistants and copilots that streamline workflows and offer contextual and more personalized support. Building further, AI agents can autonomously plan, make decisions and execute tasks toward goals, while Agentic AI represents the next leap—combining memory, tool use, and multi-step reasoning to handle complex, evolving objectives with minimal guidance.
Agents are specialized and can be used with a copilot to perform specific tasks, often requiring little input from users. If agents are like apps on an AI-powered interface, then a copilot is the interface that allows you to interact with these agents. An AI agent is an advanced AI assistant designed to augment human capabilities across a wide range of tasks. AI assistants are reactive whereas AI agents are proactive.
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The core idea of Agentic AI refers to AI systems that autonomously plan and execute workflows. By combining AI models, data, tools, and logic, these systems work toward achieving a goal, with or without human supervision. An Agentic AI system comprises one or more AI agents equipped with reasoning and planning capabilities, while generative AI (Gen AI) serves as a subcomponent, often used for tasks like text generation or summarization within the broader agentic framework. Even non-LLM agents—such as rule-based systems or those using text analytics—can qualify as AI agents if they can interpret requests, make decisions, and execute actions using tools. The shift from LLMs to AI agents marks a move from passive text generation to active, goal-oriented behavior. Learn more about how AI agents work in practice and how they differ from Agentic AI by visiting this page on sas.com: AI Agents – What They Are and Why They Matter.
To better understand where today’s AI systems stand, we can broadly group them into three categories: RAG-Based AI Systems, Tool-Augmented AI Systems, and Agentic AI Systems. Let’s explore each through a scenario to see how they differ in capabilities and autonomy.
Scenario 1: RAG-Based AI System - A SAS user who needs help with specific tasks like running a logistic regression or plotting graphs and she might ask questions such as: “Which procedure do I use for logistic regression in SAS?” or “What are the options available in the PROC SGPLOT?” or “How do I merge datasets in SAS?” Behind the scenes, the AI assistant uses a Retrieval-Augmented Generation (RAG) approach, pulling accurate and relevant content from trusted sources like SAS documentation in PDFs or web pages. It doesn't just dump raw content—it extracts the right syntax, surfaces helpful examples, and gives the user clear, targeted guidance. However, this system does not act on behalf of the user. There's no execution, no interaction with live SAS sessions, and no autonomous decision-making. It’s a powerful information assistant, but not an agent. Think of it as a smart guide—one that enhances your workflow but doesn’t run it.
Scenario 2: Tool-Augmented AI System - A data engineer working within a live SAS Viya environment, needing to run forecasting models or generate visualizations. Instead of just looking up how-tos, the AI assistant is now directly integrated with SAS Viya APIs, job execution services, and CAS actions. The engineer can issue direct commands like: “How many rows are there in my CAS table sales_2024?” or “Run the forecast_model.sas program on my latest dataset.” or “Create a bar chart of profit by region.” This AI assistant connects to the live system, executes the necessary actions, retrieves metadata, runs scripts, and creates visuals — all in real time. It’s no longer just an advisor; it’s a hands-on assistant that gets things done through tool integration. That said, there are still boundaries. While the system can perform tasks, it doesn’t make decisions on its own. It relies entirely on user instructions, lacks autonomy, and doesn’t engage in multi-step reasoning or planning. It’s powerful and interactive — but still not fully agentic.
Scenario 3: Agentic AI System - A marketing head wants to improve customer retention and simply asks the Agentic AI-powered agent: “Why are we losing repeat customers, and what can we do about it?” The agent takes over, accessing and cleaning data from CRM systems, support tickets, and transaction logs. It then builds predictive models to identify churn patterns — discovering that high return rates and delayed deliveries are key drivers. Based on these insights, it recommends and deploys actions: setting up a loyalty program and streamlining logistics for affected regions. It configures dashboards to monitor KPIs and runs A/B tests to measure impact — all autonomously, without step-by-step user instructions. This showcases Agentic AI managing data, developing models, and deploying insights — end-to-end across the analytics lifecycle.
AI agents are currently one of the most talked-about trends in technology. To explore the key components that make up an AI agent, look at this SAS blog: Understanding the Components of an AI Agent: A Five-Step Life Cycle. To learn more about Agentic AI, check out the video 'What is Agentic AI?' on the SAS YouTube channel.
Empowering the Data and AI Lifecycle with Agentic AI
AI is essentially the story of starting with real questions and then using your data to make decisions based on understandable, actionable insights. The process takes us through three primary phases: Manage Data, Develop Models, and Deploy Insights. We begin with the data management, where data from multiple sources is accessed, cleaned, and prepared. This ensures a strong foundation for any analysis. Next comes analytics and modeling, where data scientists use statistical techniques, machine learning, and AI to uncover patterns and build predictive models. The goal is to generate insights that explain trends or forecast future outcomes. Finally, in the deployment and decisioning phase, these insights are put to work. Models are integrated into business processes, dashboards are created, and decisions are automated or guided by analytics.
The lifecycle is iterative—insights lead to new questions, driving continuous improvement. With SAS, each stage is supported by powerful tools, enabling organizations to make smarter, data-driven decisions end to end.
Motivated by the Agentic AI framework, imagine an AI assistant that can handle all the tasks you typically perform — from generating ETL jobs and checking data quality to writing SAS code, creating analytic process flows, preparing and analyzing data, building and evaluating model pipelines, generating dashboards, deploying models, managing business rules, optimizing decisions, and producing reports. While we're not fully there yet, we're well on our way toward realizing this vision.
SAS’ Generative AI product strategy is closely aligned with this goal. For example:
- SAS Viya Copilot for SAS Code Assistance generates, explains, and comments on SAS code, while the SAS Viya Copilot for Modeling Pipelines guides you through tools like Model Studio. For more details, check out this SAS blog: SAS® Viya® Copilot – Your AI Assistant for New Levels of Productivity with Data and AI.
- Specialized industry copilots are designed to accelerate domain-specific workflows — such as SAS Viya Copilot for Clinical Data Discovery in SAS Health, or SAS Viya Copilot for Asset and Liability Management in financial services.
- Flexible SAS Viya GenAI building blocks support the development and orchestration of LLM-based applications, including the ability to orchestrate agents using SAS Intelligent Decisioning. Watch the video Agentic AI in Action: How SAS Intelligent Decisioning Powers Smarter, Fairer Decisions on the SAS YouTube channel for a quick overview.
- Standalone GenAI services, like SAS Data Maker, integrate seamlessly into your existing toolchains to enable scalable, AI-powered workflows.
Together, these innovations represent SAS’s commitment to building toward a truly Agentic AI future. SAS offers massive value proposition in combining generative AI with everything we have been doing for 50+ years. With Enterprise Agentic AI on SAS Viya, you can build and design your own AI agents. Explore SAS' Agentic AI page on sas.com to learn about the key differentiators and discover what SAS Agentic AI can do for you.
If you would like to learn to build, deploy, and monitor Agentic AI LLM-based applications using SAS Viya and the SAS Agentic AI Accelerator check this SAS Course: Course: Agentic AI - How to with SAS® Viya®
Transforming Data Roles with Agentic AI
AI agents are reshaping responsibilities across the data and AI lifecycle—not by replacing people, but by amplifying their impact.
Here's how Agentic AI is transforming data roles:
- For data engineers, routine tasks like building ingestion pipelines, refactoring, and incident resolution become largely automated, freeing them to focus on architecture design, governance, and strategic enablement.
- Business analysts see accelerated insight generation as AI handles preparation and insight discovery, making data quality and structure even more essential. This evolve from reporting and querying to guiding AI agents that proactively uncover insights and generate narratives.
- Data scientists shift from model-building to orchestrating agentic systems that automate feature engineering, model development, and evaluation—requiring stronger business acumen, expertise in reinforcement learning and multimodal AI, and oversight of transparency and ethics.
- Meanwhile, business users gain greater self-service capabilities: natural-language querying and AI-powered visualization tools enable exploration and decision-making without intermediary teams.
- Machine Learning Engineers move from model deployment and infrastructure tuning to integrating and maintaining autonomous AI agents in production.
- For model Risk Officers, the focus shifts from periodic model reviews to continuous monitoring and governance of autonomous decision-making agents.
Across the data and AI lifecycle—from preparation and analysis to governance and action—agentic AI integrates with semantic layers to elevate both automation and trust.
Concluding Remarks
Agentic AI is about autonomous goal-driven orchestration, where models are tools, agents are decision-makers, and the system evolves beyond fixed flows into adaptable, intelligent workflows — LLM-powered or otherwise. A model like an LLM is just a tool; it predicts or generates stuff. But an agent is more than that. It’s a system that decides which tools to use and how to use them to get things done.
Right now, most agents use decision flows with LLMs baked in. They’re helpful, but still structured. In the future, we’ll see more advanced agent platforms—think of them like central control systems—with more autonomy and flexible toolkits. What sets Agentic AI apart is its ability to adapt. It doesn’t just follow rules—it understands goals and builds or adjusts workflows on its own. Also, it’s not all about LLMs. Agents existed before them, and even rule-based or older AI systems can be considered agents—if they understand tasks, make choices, and use tools. When you start connecting multiple such smart agents together, each capable of making decisions and calling tools, that’s when you get a truly agentic AI system.
SAS is well poised to excel in Agentic AI due to its robust foundation in data management, analytics, and model governance. With decades of expertise in automating analytical workflows, built-in support for data quality, explainability, and compliance, SAS can seamlessly integrate AI agents into enterprise ecosystems. Its evolving platform capabilities—like intelligent process automation, decisioning engines, and support for code and low-code experiences—position SAS to orchestrate Agentic AI systems that are trustworthy, scalable, and enterprise-ready.
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