In this session of the Predictable AI Placement series, Jay Stewart, Solution Consultant Fellow at Pega, takes a deep dive into the Step Agent—the third of the five AI placement patterns—and shows how it performs in‑process analysis to support faster, higher‑quality decision‑making.

Using a compliance and audit scenario, Jay demonstrates how the Step Agent is invoked during the audit assessment stage to perform a comprehensive analysis across multiple inputs. The agent evaluates auditor‑written findings, document summaries generated by the Doc Agent, audit objectives, and internal audit rules, and then enriches this analysis by calling an agent‑to‑agent (A2A) remediation service.

You’ll see how the Step Agent:

• Runs automatically as part of the case flow (not conversational)
• Consumes structured and unstructured audit data
• Applies semantic analysis to assess findings against rules
• Calls an external remediation agent to propose next steps
• Produces a structured assessment summary written back to the case

The session walks through both runtime behavior and design‑time configuration, including:

• Defining step‑agent instructions and guardrails
• Using knowledge tools backed by data pages to retrieve case data
• Integrating agent‑to‑agent calls for remediation suggestions
• Writing agent output to a rich‑text field for human review and refinement

Jay emphasizes how the Step Agent acts as a first‑pass analyst, accelerating assessments while keeping a human firmly in control. The results can be reviewed, edited, and approved before the case progresses, ensuring accuracy and accountability.

This deep dive builds on the earlier Application Agent and Doc Agent sessions and sets the stage for the next placement patterns—Assignment Assist Agent and GenAI Connect—completing the end‑to‑end picture of how AI placement patterns work together in Pega.