From 7 Weeks to 10 Days
Our insurance data lake team cut routine implementation by 74% last quarter. Same compliance gates, same people, no shortcuts. The difference? We paired each human with a purpose-built AI agent that handled prep work, execution, and validation before anyone touched a file.
Chapter 1 – The Request Lands
Monday, 9:15 AM – Jira
A product owner creates "Onboard new customer feed," attaches a sample CSV, and writes two sentences of context. Done.
The StoryCraft-BA Agent kicks in:
- Reads the ticket, identifies it as a new customer feed
- Pulls current pipeline configs and sample data
- Converts the brief request into a comprehensive, INVEST-compliant user story
- Fills in what it can, flags what the BA needs to confirm
Pain removed: No more template hunting. No endless clarifications. You get a complete ticket spec generated from your existing tools.
Chapter 2 – Design & Implementation
Monday, 10:15 AM – Architect adds the agent-AILA-pipeline-architecture label.
The Design Agent:
- Downloads attached data, profiles the schema, and checks data quality
- Scans Git for similar feeds, generates mapping rules, and data-quality checks
- Produces all required change-request documentation
- Runs schema-drift and implementation checks, posts validation report
- Opens feature branch and GitLab MR, updates Jira with "DESIGN COMPLETE"
Elapsed time: 6 minutes wall-clock, 1 minute keyboard time for the architect.
Skip Excel. Skip manual analysis. Review the agent's output and move on.
Chapter 3 – Build & Test
Monday, 11:00 AM – CI pipeline triggers.
Auto-generated Glue-job code compiled. The QA Agent:
- Spins up a synthetic dataset
- Runs baseline data-quality rules
- Adds three extra checks where profiling shows high null density
- The posts pass/fail matrix as MR comments
QA and DevOps leads review, adjust when needed, and rerun automatically.
Chapter 4 – Deploy & Govern
Friday – Release window.
The Deployment Agent:
- Assembles release manifest
- Drafts the least-privilege IAM changes
- Calculates prompt hashes to prevent drift
- Files CAB record
- Promotes artifacts through environments
Humans review and approve.
Governance runs automatically: in-tenant LLMs keep data private, prompt-drift monitors block unauthorized changes, and SOC 2 / GDPR mappings attach to everything.
Results Dashboard
| Role | Effort Before | Effort After |
|---|---|---|
Product Owner / BA | 3 days | 15 min |
Architect | 2–3 days | < 30 min |
Data Engineer | 5–10 days | 1 hour review |
QA | 5 days | 45 min |
DevOps & Release | 3 days | 30 min |
Calendar time dropped from 7 weeks to a few business days. Pre-prod data-quality violations caught jumped 22%. The agent-generated rule set found problems we'd been missing.
Four Field-Tested Takeaways
- Use “declarative first”/”low code” approach: Agents excel at language understanding, pattern recognition, and writing reusable templates, and they work best when those patterns are clearly declared.
- Treat prompts as your most valuable IP: Handle agent instructions like source code: diff them, review them, and sign off on changes. Continuous prompt‑drift monitoring helps prevent surprises (and hallucinations). Prompts are technology- and agent-agnostic and can be consumed by any AI Agent.
- Rigid, hard‑coded Agentic‑AI workflows are no longer necessary: Equip a model (e.g., Amazon Q or Claude Sonnet) with the right tools and a well‑designed prompt, then let it plan, chain its thoughts, invoke tools, and reflect. An orchestrator, such as an AWS Strands agent, runs the event loop around each model call until the task is complete.
- Pair every human with a purpose‑built, narrowly focused agent: We started with StoryCraft‑BA and the Design Agent; as trust grew, QA and Deployment agents followed. Small wins compound quickly.
Watch the Full Walk-through
Check out the demo video below to see how each agent operates and the GitLab Merge Requests they generate in real time. It's automation, visibility, and dev productivity — working together.
What Is Agentic Delivery?
Agentic Delivery moves routine tasks to agents so humans can solve problems. Declarative configs become the contract; agents do the work, and engineers/business users steer the ship. You get faster delivery, higher quality, and happier teams.
Questions? Reach us at sales@dataart.com.
