Agentic AI in Pega Workflows
Enterprise sustainability workflows require continuous evaluation and timely decision-making. However, most implementations still rely on static rules, manual validation, and delayed mitigation actions.
This article demonstrates how Agentic AI in Pega workflows enables agents to interpret context, evaluate compliance, and drive decisions autonomously within a case — using prompt-driven logic and governed execution.
A Small Showcase Application
A Water Stewardship workflow where agents evaluate water usage against regulatory thresholds, determine compliance, and trigger mitigation actions autonomously within the case.
Watch demo : https://youtu.be/RTHN1txcxcM?si=s7Lqq2hgobZPBv7J
Agents interpret context, apply reasoning, and make decisions dynamically guided by prompts and guardrails, not predefined logic.
From Data Input to Autonomous Decisioning
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Users submit water usage data or ingestion occurs via integration
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An agent retrieves contextual data (thresholds, historical usage) via Data Pages
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Instead of executing predefined rules or data transforms
the agent evaluates compliance dynamically using prompt-driven reasoning -
Compliance status is determined by the agent (Compliant / Non-Compliant)
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A Decision Agent determines whether mitigation is required and at what severity
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Based on this, the agent flags if mitigation planning is needed
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The decision is then reviewed and approved by the user
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Once approved, GenAI Connect is invoked to generate context-aware mitigation plans aligned to the identified risk level
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Through conversational interaction with agents, users can post messages to Pulse, generate and attach PDF reports to the case, and retrieve case insights
Pega Agent Configuration Overview
Agent Instructions
The agent is instructed—via structured prompting—to retrieve the applicable regulatory threshold from configured Data Pages, compare it with the current water risk score within the case, and determine both compliance status and the need for mitigation.
All evaluation logic is executed by the agent based on these instructions, without relying on predefined rules or data transforms.
Guardrails
Strict guardrails ensure that the agent:
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Uses only authorized case properties and configured enterprise data sources
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Avoids assumptions, external inference, or unsupported calculations
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Adheres to defined compliance evaluation boundaries
These guardrails act as the control layer, ensuring:
Response Structure and Tone
The agent is configured to produce structured, system-consumable outputs, clearly presenting:
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Compliance status
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Risk level
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Mitigation requirement
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Supporting rationale
This ensures seamless integration with downstream workflow processing.
Additional Context and Data Access
Referenced Data Pages are configured in the Additional Context section, enabling the agent to securely access enterprise data such as regulatory thresholds.
This ensures that all decisions are made using trusted, real-time, and governed data sources
Knowledge Sources Configuration
Tools such as pyGetCaseData and pyGetCaseHistory are configured to provide the agent with access to:
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Current case details
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Historical interactions and activity
This allows the agent to operate with complete case context, improving accuracy and traceability.
Advanced Tools Configuration
Action-oriented tools such as pyPostToPulse and pyCreateAndAttachPDF are configured to enable the agent to:
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Post updates directly to Pulse
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Generate mitigation plan documents
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Attach PDFs to the case
This allows agents to move beyond analysis and execute governed actions within the workflow.
Mitigation Plan Generation
Based on the user’s decision to approve the agent’s analysis and findings, a GenAI Connect rule is invoked to generate mitigation actions aligned to the identified risk and context.
The rule uses evaluated inputs such as usage deviation, risk level, and facility context to produce structured, actionable mitigation plans, which can then be reviewed, executed, and attached to the case.
Conclusion
This approach demonstrates how agent-driven execution can be embedded seamlessly within Pega workflows to enhance decisioning and automation.
Agents are highly effective, but their impact depends on how well they are prompted and governed.
To ensure maximum efficiency:
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Prompts must clearly define intent and expected outcomes
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Guardrails must enforce safe and deterministic behavior
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Response structure and tone must align with workflow needs
When designed correctly, agents can interpret, decide, and act autonomously — while still allowing manual intervention when required, ensuring a governed and auditable workflow framework.