Intelligent Digital Worker Governance

AI agents running in production often depend on business rules that change faster than technical teams can release code. A customer service policy changes, a screening criterion is updated, or a new operational exception is introduced, but the business must open a change request and wait for engineering before the agent can respond correctly. This delay increases manual intervention, causes repeated agent errors, and creates operational and compliance risk. Directly editing prompts or allowing uncontrolled agent learning is not a safe alternative because changes may be poorly documented, introduce conflicting behavior, or affect cases beyond the original correction. To address this challenge, AI/R Compass UOL delivers Intelligent Digital Worker Governance, a production implementation that adds a controlled human-feedback and continuous-improvement layer around an AI agent already running in the customer’s AWS environment. Authorized subject matter experts correct the agent in natural language during normal operations. Each correction is attributed, validated against approval rules and guardrails, tested against previously approved behaviors, and promoted into durable memory only when it passes the required controls. The engagement provides: Business-controlled improvement: Authorized users propose behavioral corrections without rewriting prompts or waiting for a software release. Governed memory: Approved corrections become versioned, durable knowledge that can influence future interactions. Approval and authority controls: Role-based permissions determine who may submit, approve, promote, or reverse a behavioral change. Regression protection: Previously accepted use cases are automatically tested before new behavior is promoted. Auditability and rollback: Every correction maintains its author, timestamp, approval status, test result, version history, and rollback path. Operational observability: Agent behavior, correction events, test outcomes, exceptions, and AWS consumption can be monitored. The reference solution uses Amazon Bedrock for generative inference, Amazon Bedrock AgentCore Memory for managed agent memory where appropriate, AWS Lambda for controlled execution, Amazon DynamoDB for version and workflow state, and Amazon S3 for audit and testing artifacts. Supporting services may include Amazon API Gateway, Amazon EventBridge, Amazon CloudWatch, AWS Identity and Access Management, and AWS Key Management Service. The solution is deployed through infrastructure as code into the customer’s AWS account. The customer retains ownership of the architecture, accumulated memory, business rules, integrations, test assets, and operating model. AI/R Compass UOL completes the implementation, enablement, and handover without requiring the customer to adopt a Compass-hosted SaaS platform. The outcome is a safer and faster operating model for continuously improving production AI agents. Business teams can respond to changing policies and exceptions while technology, risk, and compliance teams retain control over what becomes permanent agent behavior. Buyer Problem / Business Trigger An AI agent is already running in production, but behavioral changes require engineering support or technical change requests. Business rules, policies, qualification criteria, or operational exceptions change frequently. The same agent mistakes continue to occur because corrections are not retained as governed, reusable knowledge. Prompt updates and behavioral changes lack consistent approval, versioning, testing, or rollback controls. Risk, compliance, or operations leaders need evidence of which rules were active when an agent made a decision. The customer needs to scale production AI governance without creating a permanent engineering backlog. Expected Output / Deliverables Current-state agent behavior and change-process baseline Correction-authority and approval-control matrix Durable-memory taxonomy and conflict-precedence model Guardrail and behavioral regression specification AWS target architecture and security design Infrastructure-as-code deployment in the customer’s AWS account Integrated correction, validation, promotion, and rollback workflow Versioned audit history for behavioral changes Automated behavioral regression test suite Agent-quality, exception, and operational monitoring configuration Business-user and platform-operator training Operational runbook and handover package Expansion recommendations for additional agents and workflows