Introduction   In today’s rapidly evolving world, companies are under pressure to make faster, smarter, and more transparent decisions. Whether it’s approving a loan, detecting fraud, or recommending a personalized offer, every decision must balance automation, governance, and human judgment. This is where SAS Intelligent Decisioning shines — empowering businesses to design, deploy, and manage data-driven decisions at scale.   At the heart of this capability lies one of the simplest yet most powerful logic structures in computing and my personal favorite, the IF/THEN/ELSE statement. In SAS Intelligent Decisioning, this structure becomes a foundation for building governed and explainable decision flows. Business users can easily create and manage rules through visual interfaces, while data scientists can integrate analytics and Agentic AI components such as Large Language Models (LLMs) for deeper reasoning and adaptability. By combining traditional rule-based logic with the flexibility of Agentic AI, companies can create hybrid decision systems.   This blog explores how IF/THEN/ELSE logic can be applied through rule sets and conditional branching in SAS Intelligent Decisioning. It shares best practices for designing and building responsible, autonomous decision-making systems using SAS Viya.   Two Ways to Use IF/THEN/ELSE Logic in SAS Intelligent Decisioning   In SAS Intelligent Decisioning, you can use IF/THEN/ELSE logic in two primary ways: Within rule sets, where business users can define conditions and actions in a user-friendly interface. Through conditional branching in decision flows, to orchestrate multiple components including Agentic AI elements such as LLMs.   Figure1: Default Rule Sets with IF/THEN/ELSE/THEN logic     Using IF/THEN/ELSE in a Rule Set Rule sets are a core component of SAS Intelligent Decisioning. They allow business analysts to build and manage logic through a visual interface which requires advanced coding.   Here’s how to get started:   Step 1: Create a New Rule Set In the SAS Intelligent Decisioning interface: Create a new rule set and define your input and output variables. Step 2: Add Your First IF Rule Click Add Rule. Define your condition: Example: IF loan_amount > 100000 Specify the action: THEN approval_status = 'Pending Review' Step 3: Add an ELSE Rule Select the rule you just created. Click Add → Else Rule. Example: ELSE approval_status = 'Auto Approved' Step 4: Add an ELSE IF Rule Insert additional conditions by selecting Add Rule again and changing the operator to ELSE IF. This creates an extended logical sequence, like: IF loan_amount > 100000 THEN approval_status = 'Pending Review'; ELSE IF credit_score < 600 THEN approval_status = 'Rejected'; ELSE approval_status = 'Auto Approved'; This structure allows you to capture business policies in an auditable, version-controlled format that can evolve as strategies change.     Figure 2: Example of Intelligent Decisioning with Rules Sets and LLM     Using Conditional Branching in a Decision Flow For more complex processes, you can use conditional branching within a decision flow. Decision flows let you visually orchestrate multiple components with rule sets, analytical models, custom code, and now, Agentic AI capabilities.   Step 1: Build a Decision Flow On the canvas, drag and drop nodes such as: Rule Sets Analytical Models Custom Code Call LLM (Agentic AI) nodes Step 2: Add a Conditional Node Click a connection arrow between two nodes and choose Insert Conditional Branch. This allows your flow to split based on a condition, for example: IF approval_status = 'Pending Review' Step 3: Create Branches True Path: Connect to a new sequence of actions for example, Call LLM node. False Path: Connect to another path for example, finalize approval or rejection. Step 4: Integrate Agentic AI Components Agentic AI brings a new level of reasoning and adaptability to decisioning. Call LLM Node: Sends data and context to a Large Language Model for advanced analysis or recommendations. Example prompt: “Analyze this applicant’s financial situation and suggest whether they should be approved with conditions.” Memory and Guardrails: Add context persistence (memory) for consistent decisioning and define guardrails to ensure AI output aligns with corporate and ethical policies. Processing LLM Output: After receiving an LLM response, use a new rule set or code node to interpret it and finalize the decision.   Example: Hybrid Loan Approval Workflow Below is an example of combining rule sets and Agentic AI in a single decision flow illustrating a governed, explainable, and adaptive decision process. Step-by-Step Flow Start Node: A new loan application enters the system. Rule Set Node (Eligibility Check): IF credit_score > 750 THEN approval_status = 'Auto Approved'; ELSE IF debt_to_income_ratio > 40 THEN approval_status = 'Pending Review'; ELSE approval_status = 'Rejected'; Conditional Branch: The flow splits based on approval_status: Path 1 (Auto Approved): Proceeds directly to an End node — loan approved. Path 2 (Rejected): Proceeds to an End node — loan denied. Path 3 (Pending Review): Proceeds to a Call LLM node. Call LLM Node: The LLM reviews the case, analyzing the applicant’s financial situation and providing a recommendation such as: “Applicant has strong collateral; recommend conditional approval.” Rule Set Node (LLM Analysis): IF LLM_recommendation = 'Approve with Conditions'     THEN final_decision = 'Approved with Conditions'; ELSE final_decision = 'Manual Review Required'; End Node: The process concludes with the final decision logged and auditable.   The Power of Combining Rule-Based Logic with Agentic AI This hybrid approach of blending traditional IF/THEN/ELSE logic with the adaptability of Agentic AI offers several key benefits: Transparency: Every rule and decision branch is documented and explainable. Governance: Business users can manage rules directly, ensuring compliance. Adaptability: Agentic AI adds intelligence to handle complex or borderline cases. Efficiency: Routine cases are automated, freeing experts to focus on exceptions Scalability: SAS Viya’s cloud-native architecture supports high-volume, real-time decisioning.   Here are examples of best practices: Autonomy with Human-in-the-Loop (HITL) Controls Autonomy should not eliminate human judgment, but it should enhance it.   Human-in-the-Loop (HITL): Route low-confidence or high-risk claims to adjusters. IF (Claim_Amount > 10000) OR (Fraud_Risk_Score < 0.6) THEN Route_To_Human_Review = Yes   Human-on-the-Loop (HOTL): Supervisors monitor dashboards in SAS Visual Analytics for anomalies or drift, pausing automation if needed.   Out-of-the-Loop (OOTL): Fully automate low-risk, high-confidence cases. IF (Claim_Amount < 500) AND (Fraud_Risk_Score > 0.9) THEN Auto_Approve = Yes     Situational Awareness and Real-Time Context Agents must operate with environmental intelligence understanding when, where and how to act. Two Types of Awareness Operational: Adapt to system load or model health Social: Respect user context and interaction norms Technical Examples Operational Awareness (Decision Flow): IF (Model_Drift_Score > 0.2) OR (System_Load > 0.8) THEN Route_To_Human_Review = "Yes";   Social Awareness (Context Window): IF (User_Status = "InMeeting") THEN Send_Notification = "Later"; Use SAS Visual Analytics to build real-time dashboards showing: Decision latency trends Model drift over time Trigger frequency for context-aware rules Example: A dashboard tile displaying the number of automated actions deferred due to “User in Meeting” context offers transparency into how situational awareness influences outcomes.   Robustness to Failure and Uncertainty In real-world environments, agentic systems can face uncertainty like incomplete data, unreliable APIs, or ambiguous human inputs. To ensure safe autonomy, agents must be designed with graceful degradation, fallback mechanisms, and error-aware decision logic.   Key Challenges Missing data: sensor or customer data unavailable or delayed. Unexpected tool responses: external API or LLM returns invalid, null, or inconsistent results. Ambiguity in instructions: conflicting signals or uncertain natural language prompts.   Technical Implementation in SAS Viya Handling Missing or Incomplete Data Within SAS Intelligent Decisioning, you can define default values, imputation rules, or alternative branches for missing input variables. Example: /* Example decision table snippet */ IF CreditScore IS MISSING THEN     RiskCategory = "Unknown";     Route_To_Human_Review = "Yes"; ELSE IF CreditScore < 600 THEN     RiskCategory = "High"; ELSE     RiskCategory = "Low";   Managing Ambiguity in Natural Language or User Inputs When agentic systems rely on LLM-based reasoning or human interaction, ambiguity can arise from unclear text instructions or mixed signals. Mitigate this using confidence thresholds and structured clarification prompts. Example: /* Decision logic for ambiguous instructions */ IF (LLM_Confidence_Score < 0.7) THEN DO;     Clarification_Request = "Yes";     Route_To_Human_Review = "Yes"; END; ELSE DO;     Clarification_Request = "No";     Proceed_With_Action = "Yes"; END;   Uncertainty-Aware Decisioning Use model confidence scores or prediction intervals within SAS Intelligent Decisioning to route low-confidence cases to humans or backup systems. Example: Integrate model uncertainty directly into a decision flow. IF (Model_Confidence < 0.6) THEN     Decision = "Manual_Review"; ELSE     Decision = "Auto_Approve"; Combine this with SAS Model Manager monitoring to track how many decisions fall below confidence thresholds which is a critical governance metric for safe autonomy.   Graceful Degradation and Self-Monitoring When failures occur, the agent should degrade gracefully but continue to operate in a safe, restricted mode rather than stopping abruptly. Example: If the fraud API is down and the local model is outdated, the system switches to a minimal-risk policy mode: IF (API_Status = "Down") AND (Model_Age > 30) THEN     Decision = "Hold";  /* Pause automation */     Alert_Supervisor = "Yes"; Combine this with SAS Visual Analytics dashboards that show: Frequency of fallback activations Distribution of confidence scores Downtime correlations with external APIs This visualization provides continuous assurance of reliability and accountability. Best Practice: Design fallback policies, graceful degradation, and uncertainty thresholds.   I want to mention if you have complex workflows, you might also use segmentation trees.  In SAS Intelligent Decisioning, segmentation trees function as flowchart-style diagrams in which data variables are evaluated at each node. Based on the value of these variables, the tree splits into branches, with each branch representing a different rule, condition, or business scenario. Following the branches ultimately leads to a specific action or outcome. Maybe I will blog about segmentation trees in the near future.   Conclusion The integration of Agentic AI with SAS Intelligent Decisioning transforms traditional decision flows into dynamic, adaptive systems. By combining IF/THEN/ELSE logic for structured governance with LLM-based reasoning for deeper insights, companies can achieve faster, smarter, and more transparent outcomes.  In the age of intelligent automation, the IF statement just got a whole lot smarter. ... View more

In SAS Intelligent Decisioning, the Large Language Model (LLM) response is evaluated, using contextual checks that interpret data such as user inputs, historical records, or environmental variables within a governed decisioning framework to enrich decision workflows.   Introduction In the era of Generative AI (GenAI) and AI agents, decision governance is not just a compliance necessity, it’s the foundation of responsible innovation. SAS Intelligent Decisioning (ID), part of the SAS Viya platform, helps organizations operationalize analytics, automate decision flows, and maintain governance across business rules, models, and AI components. When paired with agentic AI, SAS ID extends decision-making while preserving transparency and accountability.   Decision Governance: Definition Decision governance is the discipline of defining, managing, and monitoring how automated decisions are made, deployed, and maintained. It ensures that every automated decision is: Traceable – You can see how, why, and by whom it was made. Transparent – Stakeholders understand the logic and data behind it. Compliant – It meets internal policies and external regulations. Consistent – It behaves the same way across channels and over time. Without governance, organizations risk decisions that are made quickly but with little visibility, raising ethical, regulatory, and reputational concerns.   Figure 3: Lifecycle of Agents Figure1: Key Elements of Governance and Trust   SAS Intelligent Decisioning: The Governance Backbone SAS Intelligent Decisioning gives organizations the tools to build, test, deploy, and monitor decision logic at scale. It connects business rules, predictive models, and data inputs into unified decision flows, ensuring every step is logged, validated, and explainable.   Figure 2: Screenshot of SAS Intelligent Decisioning     Example: A Loan Approval Decision Flow Scenario: A financial institution uses SAS ID to automate loan approvals. Workflow: Data Inputs: Applicant credit score, employment history, transaction behavior. Predictive Model: A SAS Model Studio logistic regression model predicts default probability. Business Rules: Reject if credit score < 600. Flag for manual review if predicted default > 0.25. Contextual LLM Check: An LLM interprets the applicant’s unstructured input and generates a context tag   "Contextual Insight": "Recent job change - no income risk indicated"   SAS ID integrates this output as a data node to influence the review rule.   Key governance features include: Business rule and flow management – Create, version, and test decision rules through a low-code interface that bridges business and analytics teams. Lifecycle governance – Built-in workflows for drafting, reviewing, approving, and deploying decisions with full audit trails. Transparency and traceability – Every decision can be decomposed to show which rules fired, which models ran, and which data was used. Integration with models – Deploy SAS or open-source models seamlessly within governed decision flows. Real-time decisioning at scale – Use REST APIs for high-throughput, low-latency deployments. In essence, SAS ID brings structure and discipline to what was once a fragmented, manual process.   Enter Agentic AI: New Power, New Governance Challenges Agentic AI has revolutionized how we approach decisioning. LLMs and AI agents can analyze unstructured text, interpret complex requests, and even generate policy suggestions or customer responses. However, these new capabilities bring new governance questions: How do we validate AI-generated insights? How do we prevent bias or “hallucinated” content from influencing business decisions? How do we document and explain GenAI-driven logic?   Figure 3: Lifecycle of Agents Figure 3 explains how AI agents are more than a LLM.    Example 1: Automated Policy Drafting A financial institution uses an LLM within SAS Intelligent Decisioning (SAS ID) to draft initial versions of lending policy updates based on new regulatory text. Old approach: Rules were manually reviewed and translated into decision logic by analysts. Agentic AI approach: With the AI agents the LLM reads the regulation, identifies key clauses, and generates draft decision rules. For example, loans above $50,000 require additional verification. Challenge: How can we verify that these AI-generated rules correctly reflect the regulation and aren’t hallucinated?   Governance Control in SAS: Prompt & output tracking: SAS logs every LLM prompt (e.g., regulatory text and query) and response. Validation workflow: A human reviewer in SAS Model Manager validates or corrects the AI draft before it becomes part of a production decision flow. Auditability: All versions of the generated logic are stored, time-stamped, and traceable.   Example 2: Customer Response Generation A telecom company uses Agentic AI within its customer service flow to generate personalized responses to billing disputes. LLM task: Summarize the issue and propose a response using customer interaction history. Risk: The model could fabricate account details, for example your discount expired last month, which isn’t true and causes hallucination. Governance Solution: SAS introduces guardrails and post-processing logic: The Agentic AI output passes through a validation layer that cross-references customer data in SAS Viya. Only verified facts are included in the final message. Each decision path—inputs, prompts, model responses, validation steps, and final outputs—is tracked in SAS ID decision logs, ensuring end-to-end transparency.   Example 3: Bias Detection and Model Validation A healthcare insurer integrates Agentic AI to summarize physician notes and suggests care recommendations. Challenge: The model may generate biased summaries that emphasize certain demographics or treatments. Technical Governance Approach in SAS: Bias metrics are automatically applied to the model outputs within SAS Model Studio. Results are visualized in SAS Visual Analytics to show how AI insights feed into downstream decisions.   SAS addresses these challenges through AI governance frameworks that manage the entire model lifecycle—from training and validation to deployment and monitoring. When Agentic AI components are integrated into SAS ID decision flows, organizations can track inputs, prompts, model outputs, and post-processing logic—all under the same governance umbrella.   The “Ins” of Decision Governance To make decision governance successful, organizations should focus on the following best practices: Architect for clarity – Map each decision type, its data inputs, business rules, models, and intended outcomes. SAS ID’s visual decision flows help teams align logic before deployment. Govern the full lifecycle – Apply approval workflows, versioning, and testing from design to decommissioning. Ensure data and model integrity – Use trusted data pipelines, monitor for drift, and maintain bias detection. Embed transparency – Enable “explainability on demand” through rule analysis and decision-path tracing. Maintain human oversight – Determine when human approval is required and when automation can proceed independently. Measure and iterate – Track outcomes and refine logic based on performance and feedback. With these “Ins,” decision automation becomes both scalable and trustworthy.   The “Outs” to Avoid Conversely, organizations often stumble when they: Treat governance as an afterthought—bolting it on after deployment rather than designing it in from the start. Rely exclusively on black-box AI—without explainability, adoption and compliance falter. Ignore model drift—models and GenAI agents change over time; without monitoring, decisions degrade. Exclude human oversight—especially in high-risk areas like finance or healthcare, humans must stay accountable. Separate decision, model, and data governance—each informs the others; silos invite inconsistency. Avoiding these “Outs” keeps automation aligned with business ethics, compliance, and customer trust.   SAS ID + Agentic AI in Practice: The Hybrid Decision Future When SAS Intelligent Decisioning meets Agentic AI, the result is hybrid decisioning—where structured business rules and predictive models collaborate with generative reasoning and AI agents.   Examples include: Customer engagement: Using Agentic AI to personalize offers while SAS ID enforces eligibility and compliance rules. Fraud management: Combining deterministic risk thresholds with Agentic AI’s ability to detect novel fraud patterns in text or behavior data. Operational efficiency: Allowing AI agents to draft decisions, with SAS ID validating and executing approved outcomes automatically. In each case, SAS ID ensures that Agentic AI’s creativity operates within well-defined, auditable guardrails.   Business Impact Organizations adopting SAS ID with AI agents-powered governance gain tangible benefits: Speed with confidence – Automate complex decisions faster without losing control. Transparency and trust – Build auditability directly into decision logic. Regulatory readiness – Maintain documentation for compliance and internal audit. Cross-team collaboration – Bridge the gap between data scientists, business experts, and compliance officers. Scalable innovation – Experiment with Agentic AI safely, knowing that governance protects both process and reputation.   Conclusion The future of enterprise decisioning is intelligent, automated, and increasingly AI-driven—but it must also be governed. SAS Intelligent Decisioning, augmented by Agentic AI, provides the ideal framework to achieve both speed and control. By mastering the “Ins” of governance—clarity, transparency, lifecycle management, and oversight—and avoiding the “Outs” of neglect, and unchecked autonomy, organizations can unlock the full potential of AI-powered decisions responsibly. With SAS Intelligent Decisioning and Agentic AI, you can drive your organization’s decision automation forward, confidently and ethically.       Additional Resources: https://www.sas.com/en_us/news/press-releases/2025/may/innovate-ai-agents-intelligent-decisioning.html https://blogs.sas.com/content/subconsciousmusings/2024/04/05/llm-prompts-with-sas/ ... View more

  Introduction Decision automation is key, but the question remains: Who made the decision? How was it made? Can we audit the logic, trace the outcome, detect bias and drift? Enter the next wave: logging + auditing + agentic AI. In this post, we will explore through how these concepts sharpen decision governance and trust, and how SAS Viya’s Intelligent Decisioning capabilities support them.       Why Logging & Auditing Matter in Decisioning With decisions matter, simply executing a rule for credit approvals, fraud detection, regulatory compliance, or customer treatment isn’t enough. Companies also need to: Trace each decision back to its inputs, logic, rules,  data, and model version. Audit the decision flow, rule executions, model versions, and deployment artifacts to satisfy internal and external governance. Govern ownership of decisions, track decision logic  changes, and control versioning. Monitor performance over time for issues such as drift, fairness, and unintended consequences.      Key Logging & Auditing Capabilities in SAS Intelligent Decisioning Here are some of the features you get with SAS Intelligent Decisioning and SAS Viya that support logging and auditing: Decision flow audit history – The system maintains a history of how decision flows were built, deployed, and changed. Rule-fire analysis and detailed trace – You can perform “explicit and detailed rule-fire analysis” and persist trace information for auditing later. Logging configuration and platform logs – SAS Viya provides a logging facility where administrators can capture log messages from services, view audit-service loggers, monitor variable assignment logs, etc. Governance workflows & approvals – The decision-authoring environment supports governance checkpoints and approval workflows so that changes to logic don’t go unchecked. Support for human oversight & exceptions – The framework supports human in the loop, and escalation logic.   To sum up logging and auditing aren’t an after-thought—they are baked into the decision-platform. This is essential when you move beyond simple automation.     Figure 1: Example of decision traceability — following a decision from inputs to logic to outcome using SAS Intelligent Decisioning.     Enter Agentic AI: What It Is, and Why It Matters Automation traditionally means “if this, then that” rules, or scoring a model and acting on it. But the concept of Agentic AI shifts that paradigm: it’s about agents that can reason, decide, act, and adapt, often combining deterministic logic, analytics, business rules, and large language models (LLMs).  Agentic AI is a 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.   Figure 2: Comparison — a single AI agent performing a task versus an orchestrated Agentic AI system coordinating multiple agents autonomously.         Key characteristics: Hybrid logic: combining rules/analytics + LLM or generative elements. Human-AI balance: decide what tasks the agent does autonomously vs. when humans must intervene. Governance built in transparency, explainability, audit trails, bias detection. Scalability: agents can run at scale, across workflows, decision domains. Why this matters for decisioning: Decisions are no longer static; they can adapt in real time, escalate, or human-hand over when needed. But with greater autonomy comes greater need for logging, traceability, audit-readiness. Which takes us to the next section…     Figure 3: Stages of building, designing, and deploying Agentic AI     Merging Logging/Auditing with Agentic AI in Decisioning: The “Beyond Automation” Moment When you have an agentic AI decision-system in production, you can't rely solely on automation and hope for the best. You need to build reliability, trust and oversight into every part of the process. Here’s how logging, auditing and governance become central: Decision-flow transparency An agent might decide “We’ll approve this loan, but escalate if X happens”. You must log: The inputs - data points, score, rule triggers Which path through the decision flow was taken for instance which rules fired, which branch Which model(s) were used, what version Whether a human review occurred and if so, what the human did The final output and action Using SAS Intelligent Decisioning you get traceability of rule-fires, variables and flows. Agent autonomy boundaries and human-in-the-loop In designing agentic systems, you must define: when does the agent act autonomously; when does it escalate? You also must log those hand-offs. The human and AI balance is key as a pillar of the agentic AI offering: allowing companies to determine appropriate autonomy. Monitoring, performance & drift Agents may evolve, models may decay, rules may no longer apply. Monitoring frameworks must capture performance metrics, track fairness, bias, and detect anomalies. Audit trails for compliance/regulation High-stakes decision making, for example credit, insurance, and fraud often carries regulatory obligations. Every decision must be made. This means the logging must be sufficiently rich and immutable. There are auditing features within SAS Viya.  Audit records for user actions, resource changes, security events are some features to name a few.  Model/regime change tracking When you deploy new versions of models, or allow agents to operate differently, you must log versions, approvals, and validation results. SAS Intelligent Decisioning platform supports version control of flows, rule sets, and linked models.           Figure 4: Example of SAS Intelligent Decisioning         Practical Implementation: How to Get Started with SAS Intelligent Decisioning Here are steps for companies seeking to go “beyond automation” and building agentic decisioning with full logging/audit capability: Define decision-domains and mapping Identify the key decisions you automate like credit, fraud, marketing offers, etc. Map the decision process: data inputs → scoring/model → business rules → action → human review/override. Within SAS Decisioning, build visual flows for these processes using the Decision Builder. Enable logging & audit from day one Configure SAS Viya logging and audit services: use ‘Audit’ module, configure log retention, ensure immutability of logger settings. In decision flows, enable variable-assignment logging or detailed trace for key variables. Define what audit events must be retained for instance flow activation, rule changes, model versioning, decision outcome, etc. Embed governance workflows Use the rule-versioning, decision-flow versioning and approval workflows available in SAS Intelligent Decisioning. Ensure that every change to logic or model is reviewed, approved and traced. Design agentic workflow When building AI agents via SAS Agentic AI Accelerator, you build modular workflows combining LLMs, rules, models.  Let me pause for a second and define what SAS Agentic AI Accelerator.  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. As you do, explicitly define the human and agent autonomy boundary like where does the agent act or when to escalate.   In the agent’s design, ensure that every branch of logic is auditable: which model was called, what prompt was used, what rule-set was invoked, what output was generated, was human review triggered, etc. Operational monitoring & alerting Set up dashboards to monitor decision-volume, rule-fire counts, model performance, fairness-metrics. Use logs and audits to detect anomalies: for example, a rule that suddenly stops firing, a variable that changes unexpectedly or an agent path invoked far more than usual.  Implement LLM guardrails to ensure responsible AI behavior with input/output validation. Perform periodic reviews and “forensic” audit of random outcomes: chase from input → decision → action → outcome. Retention, archive and compliance Configure retention policies for audit logs Secure audit logs so they cannot be tampered with for example protect log configuration, monitor logger level changes)   Benefits & Business Impact When you combine agentic AI with rigorous logging and auditing in your decisioning ecosystem, you unlock several business benefits: Improved decision‐trust: Stakeholders can see how decisions were made and that you have governance. Faster deployment: When you have a decision platform like SAS Intelligent Decisioning, you can iterate more quickly, deploy flows and agents with confidence. Regulatory resilience: With full audit trails, version histories and traceability, you’re better positioned for external audits or regulatory scrutiny. Better risk management: Logging enables you to detect drift, anomalies, unfairness earlier, leading to proactive remediation rather than reactive crisis. Greater agility: The move from automation to agentic means you can respond to changing business contexts faster for example, a new fraud pattern triggers a new agent workflow.   Challenges & Considerations Of course, this isn’t without challenges. Some challenges to watch out for: Volume of logs: When decision flows scale massively, the logging/audit volume can become significant. You’ll need strategies for storage, indexing, searchability, archive / retention. Complexity of agentic logic: Agents that combine LLMs + rules + analytics can become complex to trace and explain. Ensuring transparency remains a challenge. Human/agent boundary design: If the agent scope is too broad or ambiguous you risk uncontrollable autonomy; if too narrow you lose value. A clear design is essential. Data privacy & security: Logging and audit often capture sensitive data (inputs, variables, outcomes). You must properly mask or secure logs. Governance culture: Technology alone won’t suffice. You need ownership like who owns decision flows, change-processes, staffing for monitoring and audit.     Conclusion “Automation” won’t cut it in today’s complex, regulated, fast-moving decision-landscape. To move beyond automation you need logging, auditing, governance — and if you’re ambitious, agentic AI workflows that can take decisions with reason, adaptivity, and oversight built in.  Using SAS Viya’s Intelligent Decisioning platform and its agentic-AI support gives you a commercially mature foundation: decision flows, rule engines, model integration, audit/history capabilities, and now AI agents with embedded governance. If you’re building or upgrading a decision-platform — start with the basics: traceability, logging and audit trails. Then layer in agents. Then monitor, iterate, adapt. The aim is decisions you trust, outcomes you explain with systems you scale.     ... View more

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: Component Purpose Documentation Link SAS Viya CLI Publishes to SAS Container Runtime for model deployment. Documentation SAS Environment Manager Manages permissions, roles, and access control. Documentation SAS Studio / SAS Workbench Build datasets, scripts, and Python integrations. Documentation (SAS Studio)/Documentation (SAS Workbench) SAS Model Manager Register, govern, and version LLMs or custom AI models. Documentation SAS Portal Framework Design and host prompt interfaces (Prompt Builder). Documentation SAS Intelligent Decisioning Design, test, deploy and publish automated business decisions. Documentation SAS Visual Analytics Monitor LLM performance with dashboard Documentation   Key Terminology: Term Definition LLM (Large Language Model) A generative model that produces text or code outputs from natural language prompts SAS Container Runtime Destination for deploying decision flows or AI components Prompt Builder User Interface SAS interface (requires web server) to design, test, and tune prompts     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.   ... View more

BACKGROUND Just like with movie sequels — you don’t have to watch the original to follow the plot, but it definitely helps. Seeing the first film gives you better insight into the characters, background, and little details that make the story richer. The same goes for this blog. It’s totally fine to start here, but if you want the full picture, reading the original paper is a great idea. Still, if you’d rather dive in here first, I won’t hold it against you.   If you are familiar with the original Best Practices for Scheduling in SAS Viya, then you are in for a treat because this document is an updated version of that document.  Many people who read the original have requested an updated version. Five years have passed, bringing several changes along the way. This blog details some changes from the original paper, scheduling types available and helps you take full advantage of scheduling with SAS Viya Environment. It will discuss CAS Table State Management, Command Line Interface, Jobs and Flows Page in SAS Environment Manager, SAS Job Execution Web Application, SAS Studio and an introduction of Air Flow.     INTRODUCTION You can use the following five methods to schedule in SAS Viya: CAS Table State Management, Command Line Interface, The Jobs and Flows Page in SAS Environment Manager, SAS Job Execution Web Application and SAS Studio.  Each scheduling method is unique. However, they use the same microservices to execute and schedule jobs – job execution microservice or scheduling microservice. The scheduling microservice starts the job, which is a synchronous operation to the Job Execution microservice that runs the job.  This paper explores each method’s characteristics and discusses best practices for scheduling. The article follows an alphabetical organization and we will be broken up in two parts.     CAS TABLE STATE MANAGEMENT CAS Table State Management is a great method to load, unload and import tables quickly.  CAS table state management is a feature in SAS® Environment Manager for SAS® Viya®. To access this method, click the Jobs and Flows plug-in on the left navigation pane in SAS Environment Manager. If you are a SAS Environment Manager administrator, you see three sample jobs on the Scheduling tab. You can use these jobs to manage CAS tables.   Updates to the CAS Table State Management from the Original Paper Chaining Jobs Together is a feature developed after the publication of the original paper. The advantage of chaining jobs together feature is the second job and will start as the first job completes. This feature of sequencing jobs is useful for jobs that are dependent on another job. Figure 1 demonstrates that Load Job is chain to the Unload Job.   Figure 1: Chaining Jobs together       The UP:Unload job is schedule to run after 5 minutes.  The UP:Load job is chain to the UP:Unload Job. It takes 45 seconds for UP:Load job to start after the UP:Unload completes. This is quite simple to set up. On the Scheduling Tab, looking at the Job Properties of the UP:Load Job.  Copy the ID value, Figure 2.    Figure 2: Job Properties of the UP: Load Job   On the UP: Unload Job under the Job Properties On the Arguments Tab.  Edit the successJobID setting with the value of the job you want chained in this case UP: Load. Figure 3 demonstrates the Job Properties of UP: Unload   Figure 3: Job Properties of the UP: Unload Job       Figure 4: Scheduling Tab of the Jobs and Flow page in Environment Manager   Figure 4 displays only the UP: Unload job is scheduled however since the UP:Load is chain it is scheduled as well.  Figure 5 shows the Monitoring tab and illustrates this point. This is another advantage of the of Chaining Jobs Together.  It reduces the number of jobs that need to be scheduled.   Figure 5: Monitoring Tab of the Jobs and Flow page in Environment Manager   As far as the disadvantage, be careful with the Job SuccessID on this feature.  If not careful, an endless loop of running jobs can be created.  For example, in this case there are two jobs, UP:Unload and UP:Load respectively.  Putting the successJobID for UP: Load and putting the successJobID for UP:Unload, creating each job kicking off repeatedly with only one job being scheduled will cause an infinite running of jobs.   Command Line Interface This method is great for programmers. The Command Line interface offers a programmatic way into the scheduling world of SAS Viya.  The CLI is an administrative tool for programmers, which can be accessed directly from your machine.  You can install the CLI software on your machine, create the profile and authenticate to your Viya server machine.    Updates to the Command Line Interface from the Original Paper There are several additional commands for flows in Viya 4 for the CLI feature.  The additional commands are cancel, resume, pause, release, show-history-tree, show-status and trigger shown in Figure 6.     Figure 6: Command List for the CLI   The advantage of this feature is there are commands the CLI offers to manage flows that the GUI does not offer and another option to debug job flows. For example, we will use the trigger command. Trigger allows you to run a job flow manually.   Figure 7: Display of a listing of job flows.   Figure 7 is a screenshot which displays the Job ID, Name of the flow, Version and description. Figure 8 is a screenshot of the sas-viya job flows show with the job flow ID, c904f071-c67a-439a-8f76-e91bed0b01dc of the ProcMeans Flow.   Figure 8: Display the command show of job flow, Proc Means Flow   Figure 9: Display of scheduling and trigger the flow   The syntax to schedule a flow is sas-viya job flows trigger --id job_flow_definition_id --sch scheduler_id.  I scheduled the job first by using the Job_flow ID. Once I scheduled the flow, it gave me the job_flow_definition_id.  Then I went to schedule trigger the job flow with the job_flow_definition_id, 226f1ebf-035c-44ae-a865-2f6dde3dceca and added the parameter of –sch scheduler_ID, 9a3db74b-465f-4d36-baf6-b3b8bad1a3b2 found in Figure 8.  There are a couple of ways to check the status of the flow. Figure 10 displays the history of the flow list while Figure 11 displays show history of the flow.   Figure 10 displays the list history of the flow.       Figure 11 displays show history of the flow.   As far as disadvantages, familiarity with JSON is required to use this method. I found the JSON validator to be very helpful.  Also, there are a lot of different IDs associated with the scheduler.  It takes practice to understand which ID will give you the desired outcome.    Job and Flows Page in SAS Environment Manager   You can use the Jobs and Flows page in SAS Environment Manager to schedule or monitor jobs and/or flows. This function is available apart from the left navigation pane of the GUI.   Updates to the Job and Flows Page in SAS Environment Manager from the Original Paper I would like to highlight two additional features. Along with creating jobs, you can now create flows. A Job is defined as a program that contain tasks that you need to run. A flow is defined as a container that can include jobs, flows or a mixture of both. A flow must contain at least one job.  Job-Flow Scheduling Service is a microservice that manages job execution.  SAS Job Flow Scheduler is a type of scheduler that the Job-Flow-Scheduling Service supports and manages for executing job flows. Jobs can be a part of flows and flows, or sub-flows can be a part of a flow.   Figure 12 displays the options of a flow.   Figure 12 displays the options of a flow.  The options as defined: Execute Now, means run now. Run As, means run as a different user Edit Schedule, means to modify the current schedule of the job Unschedule means to unscheduled the job so it does not run its current schedule Execution history means check the history of the running flow Delete means the current flow View Flow means to view the current flow Edit Flow means to modify the flow Properties means to check the flow properties   If this flow was not already scheduled, we will see schedule which means to schedule the flow.   For Event Types, a file event can be a trigger for an event.    Figure 13: New File Event Window   The advantage for users who prefer the point and click over command line have more options within the scheduler.  There are options to control the outcome of a flow.  Here is a blog on Tips for Scheduling Flows with SAS Viya Environment Manager.  Also, you can use a time event and file event combined to start the flow.  As far as disadvantages, when combining the time event and file event ensure the logic is true so the flow can start.  If either the time or file event does not exist, the flow will not start and can cause an impact for scheduling.   SAS Job Execution Web Application SAS Job Execution Web Application is comparable to a SAS®9 stored process. You can access it from a web browser with the SASJobExecution extension.     Updates to the SAS Job Execution Web Application from the Original Paper Jobs can be scheduled within the application.  There is a new menu option on the left-hand side to schedule a job.   Figure 14 displays SAS Job Execution Window   The advantage is to be able to schedule from the SAS Job Execution App.  You can add the parameters jobs within the application like _action=schedule, modify the timeout value and create jobs. These jobs are used for web reporting, performing analytics, building web applications, and delivering content to clients which have prompts. The disadvantage is if you want to monitor the job, then you still need to go to the application of SAS Environment Manager.       SAS Studio SAS Studio is a web-based programming environment for creating SAS code that you can access from SAS Drive.     Updates to the SAS Studio from the Original Paper Jobs can be scheduled within the application.  There is a menu option on the left-hand side to schedule a job.   Figure 15 displays Schedule options with SAS Studio   The advantage of being able to schedule from the SAS Studio is great for programmers. After developing code, the option to schedule the job from the same application is beneficial.  You can schedule the job whether you save the job or not.  If you do not save the program in SAS Studio, the naming convention in the scheduling tab in SAS Environment Manager is SAS Program_Copyn where n is the numeral order of schedule, unsaved programs.     Figure 16 displays Scheduling Tab   The disadvantage is still having to monitor and manage the scheduling from SAS Environment Manager.  Monitoring of a job is performed in SAS Environment Manager or modifying a schedule of a previously scheduled job is performed in SAS Environment Manager.        Introduction to Air Flow Apache airflow is an open-source workflow management orchestration tool for scheduling, monitoring, and managing data pipelines.  Airflow can be deployed in Kubernetes and SAS Viya can take advantage of that same Kubernetes cluster.  One of the benefits of Kubernetes is being able to scale up and down necessary.  DAG, directed acyclic graph, is a data pipeline in airflow that comes with tasks.  There is an application with Airflow that allows you to monitor your DAGS and tasks. Here is a link to a blog titled Scheduling SAS Jobs and SAS Studio Flows with Apache Airflow to learn more about Scheduling with Airflow.     Conclusion I hope you enjoyed exploring the changes and new features since the original scheduling paper. While some aspects of scheduling have evolved, one thing hasn’t changed: SAS Viya for Scheduling continues to offer a wealth of options and provides flexible, concise, and effortless ways to schedule tasks. No matter your approach, SAS Viya for Scheduling truly has a method that fits your needs. ... View more

BACKGROUND Thinking about moving from SAS 9 to SAS Viya, start with the SAS Content Assessment Tool.  It is a free download off support.sas.com.  The SAS 9 Content Assessment Tool is batch tool which contain many applications.  The applications provide a wealth of information about your SAS 9 Environment.  The SAS 9 Content Assessment tool is a part of the planning phase of migrating content from SAS 9 to SAS Viya. With the data provided the SAS Content Assessment Tool, it will give you great success for the deployment and migration to SAS Viya.     DOWNLOAD FILES There are two sets of files to choose from: 1) SAS 9 Content Assessment <Version Number> for Linux PC Users or SAS 9 Content Assessment <Version Number> for Windows PC Users (Figure 1) or 2) SAS 9 Content Assessment <Version Number> for AIX, SAS 9 Content Assessment <Version Number> for Linux or SAS 9 Content Assessment <Version Number> for Windows (Figure 2)  based on the respective operating system.    Figure 1: SAS 9 Content Assessment for PC Users   Figure 2: SAS 9 Content Assessment for Deployed Environment   The set of files in Figure 1 are for the Linux PC Users or Windows PC Users.  These files should be used when metadata is not installed.  As an example of when to use these set of files, if the user has Base SAS and a thin client on their desktop.  Sometimes, you will hear this version referred to as SAS Content Assessment Lite or the PC Version of Content Assessment.    Figure 2 is for users who have Planned deployments of SAS installed at their site.  For example, if you used a sample plan file or custom plan file including a metadata server during the installation of the SAS, you should these set of files. This version is referred to the Full Content Assessment or Content Assessment for Planned Deployments.     Once you choose the correct file type, unpack the zip file or untar the tar file, based on the Operating System on the machine that will run the content assessment.   Additional Information.  If there is a Test server, Production server, QAT server, with three separate metadata servers, then the content assessment tool should run on each environment.    DESCRIPTION OF APPLICATIONS PC User Version Application Description Code Check Displays content from migrating from SAS 9 to SAS Viya Inventory Content Counts the number of artifacts on the SAS 9 Environment Gather SAS Code Scrapes SAS code from catalogs, DI Studio, EG, EM, etc and writes that code to a .sas files     Planned Deployment Version Application Description Application Usage Displays the number of frequent users and applications Code Check Displays content from migrating from SAS 9 to SAS Viya Create Enterprise Miner Batch code Scrapes SAS code from Enterprise Miner projects and writes that code to a .sas files I18nCode Check Identifies information about .sas files compatible with utf-8 encoding in SAS Viya Inventory Content Counts the number of artifacts on the SAS 9 Environment Gather SAS Code Scrapes SAS code from catalogs, DI Studio, EG, EM, etc and writes that code .sas files Published Accessed Content Aggregates the data into a data mart Summarize SAS Logs Displays step procedures and identities program and steps which I can be optimized        CONFIGURATION   PC User Version The setup.txt file is a configuration file where the SAS Content Assessment will look at the file for the paths listed to gather information.   Modify the setup.txt file.  In the setup.txt file, Add the SASHome parameter to include the location of the root level of home directory of SAS. Add the paths that you want Content Assessment to accessed in the Path parameter. Include the Label.  The label helps the specific machine. Figure 3: Screenshot of setup.txt     Figure 3 is an example of setup.txt completed with the information in the SAS_HOME, PATH and LABEL parameters.   Planned Deployment Version The pathslist.txt file is one configuration file used by several applications from the SAS Content Assessment to collect data.   Modify the pathslist.txt. Figure 4 is an example of the how the pathslist.txt needs to be completed. In the pathslist.txt file, Add the paths you want the Content Assessment to accessed Add the path to the gatheredSASCode Add the paths to the Workspace Server, Batch Server and Stored Process Server, if logging is enabled.   Figure 4: SAS 9 Content Assessment for Deployed Environment     Additional Information. The SAS Code Check reports have great details about which .sas files are a good candidate to migrate to SAS Viya for various applications like Enterprise Guide, Data Integration Studio, etc if gatheredSASCode is included in the pathslist.txt file.    Modify the setenv.yaml. In the setenv.yaml, Add the path of the home directory of SAS to the SAS_HOME parameter Add the configuration path of the Lev1 directory to the ASSESSMENT_CONFIGDIR.   Figure 5: Screenshot of setenv.yaml   Figure 5 is an example of setenv.yaml completed with the information in the SAS_HOME and ASSESSMENT_CONFIGDIR parameters.   Modify the metaparms.sas file. Figure 6 is an example of the how the metaparms.sas needs to be modified. In the metaparms.sas file, Update the METADATACUSTOMER parameter with the name of the customer. Update the METADATALABEL parameter with a name. As example, Prod, QAT, etc Update the METADATAHOSTNAME parameter with the fully qualified hostname of the Metadata Server. Update the METAPORT with the port number of the Metadata Server Update the METADATAUSER with the unrestricted administrator account. Update the METADATAUSERPASSWORD using the SAS encrypted password for the METADATAUSER         Figure 6: Screenshot of metaparm.sas   Additional Information. Use the SAS encrypted password for the metadatauserpassword.  For documentation on SAS Encrypted password, following the link.     Execution of the Content Assessment   PC User Version Run the command as an administrator who has access to read the paths listed in the setup.txt file. From the command line, execute Runme or double-click Runme under the Assessment directory of where the Content Assessment Tool lives.     Planned Deployment Version The applications to run are found under the Assessment directory of where you unpacked the Content Assessment Tool. Run the applications as the userid that has access to read the paths listed in the pathslist.txt file. From the command line, execute the following sequence applicationUsage publishedAssessedContent  --create-uploads --encrypt-aes  --datamart-type appusage inventorycontent publishedAssessedContent  --create-uploads --encrypt-aes  --datamart-type inventory profileContent publishedAssessedContent  --create-uploads --encrypt-aes  --datamart-type appusuage gatherSASCode --all codeCheck --scan-tag SASCode --show-progress --sources-file pathslist.txt publishedAssessedContent  --create-uploads --encrypt-aes  --datamart-type codecheck i18nCodeCheck --scan-tag i18n --show-progress --sources-file pathslist.txt publishedAssessedContent  --create-uploads --encrypt-aes  --datamart-type i18n summarizeSASLogs --scan-tag SASLogs --sources-file pathslist.txt publishedAssessedContent --create-uploads --encrypt-aes  --datamart-type summarizelogs createEMBatchCode publishedAssessedContent --create-uploads --encrypt-aes  --datamart-type embatchcode   Additional Information.  You may not need all run all applications.  For example, if you do not use Enterprise Miner Projects you can skip Step 14 and 15.  Make sure you read the documentation in the references section for additional parameters when running the applications.     RESULTS   PC User Version The output generates html files that is found in the assessment directory under Content Assessment Folder. One report called inventory and the other report called Code Check. There are several tabs for the Inventory Application and the Code Check Application.       Planned Deployment Version If you have SAS Viya 3.5 or higher, you can use the Import Assessed Content application of Content Assessment to import the data from SAS 9 to SAS Viya to view Visual Analytics Report. If you do not have an Environment running SAS Viya 3.5 or higher, you can share the results with SAS.  Contact your SAS Account Team and they will assist in providing you the results to view the data in Visual Analytics.  The SAS 9 Preview Assessed Content application create a CSV file to allow you to view the data in a text format.      CONCLUSION The SAS Content Assessment is a great tool to help you understand what exists on your SAS 9.4 Environment.  Based on the installation of SAS (planned deployment or non-planned deployment), SAS 9 Content Assessment Tool offers flexibility to collect, analyze data and see the results of that data for the SAS 9 Environment. Each application provides unique information about the SAS 9 Environment and helps prepare to move content from SAS 9 to SAS Viya.      REFERENCES Documentation of the SAS Content Assessment for Windows PC Users – https://go.documentation.sas.com/doc/en/sasadmincdc/v_055/contentassessmentwin/titlepage.htm Documentation of the SAS Content Assessment for Linux PC Users – https://go.documentation.sas.com/doc/en/sasadmincdc/v_055/contentassessmentunx/titlepage.htm Documentation of the SAS 9 Content Assessment - https://go.documentation.sas.com/doc/en/sasadmincdc/v_055/contentassessment/titlepage.htm ESM and S9CA: Useful Tools for Migration from SAS 9 to SAS Viya – https://blogs.sas.com/content/sgf/2022/12/20/esm-and-s9ca-useful-tools-for-migration-from-sas-9-to-sas-viya/ Three steps to migrate OS Scheduler SAS Scheduling Jobs from SAS 9 to SAS Viya - https://blogs.sas.com/content/sgf/2023/09/29/three-steps-to-migrate-os-scheduler-sas-scheduling-jobs/         ... View more