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Data remediation agents

This service will deploy Data Remediation Agents which are autonomous AI agents powered by AWS Bedrock, designed to identify, assess, and suggest fixes for data quality issues. These agents operate to monitor data quality, triage issues based on confidence levels, and engage with human stewards and owners to enable intelligent, high-confidence remediation at scale. Whether it's resolving invalid values, repairing broken relationships, or flagging data drift, these agents detect anomalies, classify root causes, and propose fix strategies tailored to the business context—creating a powerful co-pilot for enterprise data reliability.

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About

Data Remediation Agents

What is the Offering?

Data Remediation Agents are autonomous AI-powered agents designed to identify, assess, and suggest fixes for data quality issues. These agents operate to monitor data quality, triage issues based on confidence levels, and engage with human stewards and owners to enable intelligent, high-confidence remediation at scale. Whether it's resolving invalid values, repairing broken relationships, or flagging data drift, these agents detect anomalies, classify root causes, and propose fix strategies tailored to the business context—creating a powerful co-pilot for enterprise data reliability. The solution is designed to run entirely on AWS utilising the following AWS services:

Amazon S3

Amazon Bedrock

Amazon DynamoDB

Amazon OpenSearch Service

Amazon ECR

Amazon ECS / AWS Fargate

Who is it for?

The service is ideal for organisations struggling with data quality debt, governance overhead, and inconsistent remediation processes. Core beneficiaries include: Data Governance Leaders: Looking to embed proactive and intelligent quality monitoring and resolution into data operating models. Data Stewards / Owners: Wanting contextual issue insights and recommended fix actions without manual triage overhead. Platform and Engineering Teams: Needing automated diagnostics and integration of remediation into pipelines. Business Data Consumers: Who require timely, trustworthy data for decision-making and analytics.

Value Proposition

Automated Detection – Eliminates the blind spots of periodic checks by scanning for emerging data issues. Confidence-Driven Fix Proposals – Prioritises high-certainty remediation and brings humans into the loop where needed. Operationalised Data Governance – Connects issue detection with resolution workflows and ownership accountability. Learning-Enabled Remediation – Improves over time by learning from accepted fixes, contextual feedback, and domain nuances. Trust-Boosting Data Quality – Strengthens confidence in data-driven decisions through explainable, auditable remediation pathways.

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