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Case Studies

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Accelerating Data Product Development with an Agentic SDLC Framework

Accelerating Data Product Development with an Agentic SDLC Framework

Accelerating Data Product Development with an Agentic SDLC Framework

How we automated end-to-end CPG data product development using an agentic SDLC framework

How we automated end-to-end CPG data product development using an agentic SDLC framework

Accelerate SDLC automation

Accelerate SDLC automation

Improve quality and governance

Improve quality and governance

Boost productivity and reuse

Boost productivity and reuse

The challenge

The challenge

Slow, fragmented SDLC hindered scalable data product delivery

Slow, fragmented SDLC hindered scalable data product delivery

Key challenges

Manual processes across SDLC stages slowed delivery of production-ready CPG data products. Business logic captured in natural language required technical translation, creating inefficiencies. Fragmented testing, security, and monitoring reduced consistency and governance. 

  • Manual SDLC workflows delayed production readiness

  • Natural language logic required technical translation

  • Disjointed testing, security, and monitoring

  • Limited scalability with consistent governance

The solution

Agentic SDLC framework for end-to-end data lifecycle automation

Agentic SDLC framework for end-to-end data lifecycle automation

Conversational AI-driven orchestration

A Conversational AI Accelerator interprets business logic in natural language and initiates downstream workflows. This enables seamless translation from requirements to actionable development steps.

Specialized agent ecosystem

Dedicated agents automate each SDLC stage, including data extraction, schema generation, build optimization, testing, security validation, documentation, and monitoring. This ensures repeatable and consistent execution.

Google Cloud-native integration

The solution integrates with Google Cloud services such as BigQuery for analytics and Dataplex/Knowledge Catalog capabilities for governance and data quality. Gemini Enterprise Agent Platform powers the agentic workflows and model-driven orchestration.

Human-in-the-loop validation

Human review checkpoints refine requirements and validate outputs before production deployment, ensuring accuracy and trust.

Implementation approach

Implementation approach

1

Interpret business logic using conversational AI workflows

2

Trigger agent-based pipelines for each SDLC stage

3

Generate schemas and optimize BigQuery code automatically

4

Execute automated testing and security validation

5

Integrate governance using catalog and data quality services

5

Validate outputs through human-in-the-loop reviews

The impact

The impact

Scalable Automation

Scalable Automation

Automated SDLC

  • Automated build, test, security, documentation, monitoring

  • Reduced dependency on manual processes

Efficiency Gains

  • Minimized effort converting business logic to code

  • Accelerated production-ready data asset creation

Consistency & Reuse

  • Standardized workflows across data products

  • Enabled reusable agent-driven components

Governance Strength

  • Automated checks for data quality and compliance

  • Strengthened PII handling and monitoring controls

Business Enablement

  • Enabled conversational AI for UAT and reporting

  • Empowered business users with easy interaction

Automate Data Product Development

Explore Agentic SDLC

Explore Agentic SDLC