SDLC Agent
AI-Native Software Delivery for the Full Data Project Lifecycle
From a one-paragraph business requirement to production-deployed, test-covered code - with governance and traceability built in at every stage.
Solution Overview
An AI-native, multi-agent platform that automates the full software delivery lifecycle for data projects — turning a short business requirement into a complete, production-ready delivery package. The pipeline produces structured BRD and FRD documents, Jira-ready epics and user stories, architecture andlow-level design, production-grade PySpark / dbt / Glue code,infrastructure-as-code, and FR-mapped test cases with execution results. Human review gates preserve governance between stages, and a conversational AI assistant is available throughout to edit documents, merge epics, refactor code, or generate additional tests.
Challenges We Solve
Data engineering teams face slow, inconsistent, and poorly governed software delivery. Six critical pain points drive delays, technical debt, and limited traceability.
Months, not weeks, driven by manual requirements, design, and coding.
BRDs, FRDs, and design docs are missing, stale, or written after the fact.
No link between requirements, stories, code commits, and test results.
Heavy reliance on seniors for repetitive boilerplate and scaffolding.
Varying patterns across teams and projects drive technical debt.
Agent Capabilities
The Agilisium SDLC Agent combines eight core capabilities that work together as an integrated, governed software delivery platform.
Planner, Engineering, Coding, DevOps, and QA agents each own one SDLC phase.
Every output is approved, rejected, or sent back before the next agent runs.
Stage-aware chat to edit documents, merge epics, refactor code, or add tests.
Versioned BRD, FRD, HLD, and LLD produced automatically.
Bronze/Silver/Goldscripts in PySpark, dbt, Glue, or Snowflake SQL.
Every requirement traceable to test cases andpass/fail results.
Measurable Outcomes
FAQs
Who is this solution designed for?
The Agilisium SDLC Agent is built for data engineering leaders, architects, and delivery teams who need to move from business requirements to production code faster, without sacrificing governance or documentation.
Does it replace engineers?
No. The agent automates scaffolding, documentation, and boilerplate generation, while human review gates keep engineers and architects in control at every stage of the pipeline.
What code and infrastructure does it generate?
The pipeline produces production-grade PySpark, dbt, Glue, and Snowflake SQL code, along with Terraform modules and GitHub Actions workflows for CI/CD.
How does it maintain traceability?
Every artefact - BRD, FRD, Jira epic, code commit, and test case - is linked to a single workflow ID, giving 100% requirement-to-test traceability at any point in the project.
Can outputs be edited after generation?
Yes. A stage-aware conversational AI assistant is available throughout the pipeline to edit documents, merge epics, refactor code, or generate additional tests.
Blogs
Similar Case Studies
One Requirement. One Pipeline. Production-Ready Code.











































