In the fast-paced world of decision automation, Agentic AI is transforming how organizations design, manage, and scale intelligent business processes. By combining automation with generative reasoning, Agentic AI helps teams accelerate insight generation, reduce manual effort, and enhance decision quality. I thought where is the cheat sheet to get started fast. I didn’t see one, so I decided to create one.
SAS Intelligent Decisioning, a powerful solution for building, governing, and deploying automated decisions now integrates seamlessly with Agentic AI, delivering a new level of agility and intelligence across every step of your decision workflow.
This quick-start guide walks you through the key SAS components, essential terminology, and step-by-step instructions to start using Agentic AI within SAS Intelligent Decisioning.
Why Combine Agentic AI with SAS Intelligent Decisioning?
Agentic AI is a concept, coordinated system that manages and integrates multiple AI agents, enabling them to collaborate, execute complex multi-step processes and operate with autonomy, adaptability and decision-making capability. An AI agent is an individual tool built to perform specific tasks and executing tools.
SAS Intelligent Decisioning empowers organizations to automate, manage, and monitor business decisions at scale. It provides a centralized environment to integrate rules, analytics, and AI models into consistent, governed decision flows.
When combined with Agentic AI, this capability extends beyond traditional rule automation. It allows decision flows to dynamically reason, interpret, and adapt—enabling:
- Faster innovation: Rapidly prototype and deploy AI-driven decisions using natural language interfaces and automated prompt building.
- Enhanced interpretability: Generate explanations or summaries to improve decision transparency and auditability.
- Trust and governance: Leverage the robust model management of SAS and monitoring tools to maintain compliance and control while scaling AI adoption.
The result is an intelligent decision ecosystem where automation meets reasoning that is secure and responsible.
Key SAS Components:
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Component
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Purpose
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Documentation Link
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SAS Viya CLI
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Publishes to SAS Container Runtime for model deployment.
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Documentation
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SAS Environment Manager
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Manages permissions, roles, and access control.
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Documentation
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SAS Studio / SAS Workbench
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Build datasets, scripts, and Python integrations.
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Documentation (SAS Studio)/Documentation (SAS Workbench)
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SAS Model Manager
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Register, govern, and version LLMs or custom AI models.
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Documentation
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SAS Portal Framework
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Design and host prompt interfaces (Prompt Builder).
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Documentation
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SAS Intelligent Decisioning
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Design, test, deploy and publish automated business decisions.
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Documentation
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SAS Visual Analytics
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Monitor LLM performance with dashboard
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Documentation
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Key Terminology:
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Term
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Definition
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LLM (Large Language Model)
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A generative model that produces text or code outputs from natural language prompts
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SAS Container Runtime
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Destination for deploying decision flows or AI components
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Prompt Builder User Interface
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SAS interface (requires web server) to design, test, and tune prompts
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The SAS Agentic AI Accelerator is a collection of resources, code and components designed to help companies build, govern and deploy AI agents within their existing SAS Viya environment. Here is a great blog that goes in detail about the tool.
Getting Started
- Set Up Your Environment
- Confirm that you have appropriate roles and permissions in SAS Environment Manager.
- Verify that SAS Viya CLI and SAS Container Runtime are correctly configured for model deployment.
- Register an LLM
- Open SAS Model Manager to register your chosen LLM or custom AI model.
- Define connection details such as API endpoints, authentication tokens, and model configuration parameters.
- Version your models to ensure traceability and governance across environments.
- Build and Test Prompts
- Launch the Prompt Builder UI within the SAS Portal Framework.
- Experiment with prompt templates to fine-tune accuracy and output relevance.
- Adjust parameters such as temperature, response length, and output format to achieve consistent results.
- Save successful prompt patterns as reusable templates for decision flows.
- Integrate Agentic AI into Decision Flows
- In SAS Intelligent Decisioning, create or modify a decision flow.
- Add a node to invoke your LLM or AI agent for specific tasks such as:
- Rule generation automatically producing or updating business rules.
- Use code to call API or SQL external
- Explanation generation like providing justifications for AI-driven recommendations.
- Summarization or prediction interpretation (translating analytical outputs into actionable insights).
- Use Agentic AI nodes to make your decision flows more adaptive and context-aware.
- Deploy and Monitor
- Publish your decision flow to SAS Container Runtime for operational use.
- Use SAS Visual Analytics dashboards to track performance metrics—such as latency, response accuracy, and user engagement.
- Continuously monitor model drift and prompt behavior to ensure consistency and fairness.
Best Practices for Success
- Iterate often: Treat Agentic AI prompts and decision logic as living components. Small refinements can yield large accuracy gains.
- Govern responsibly: Maintain documentation, version history, and approval workflows in SAS Model Manager to support explainability.
- Collaborate across teams: Encourage collaboration between data scientists, business analysts, and domain experts to co-design decision flows.
- Measure business impact: Use analytics dashboards to quantify improvements in speed, accuracy, and decision quality.
Conclusion
Agentic AI represents the next evolution in intelligent automation—bringing adaptability, creativity, and reasoning to business decisioning.
By integrating it with SAS Intelligent Decisioning, organizations can move beyond static rules toward self-improving decision systems that continuously learn from outcomes.
Use this cheat sheet as your starting block: test, learn, iterate, and scale. The more you embed Agentic AI into your workflows, the faster you’ll move from manual processes to truly intelligent, data-driven automation.
