# AI-Augmented Software Development Lifecycle

Wells Fargo · 2024 – present · Role: Lead architect

> Coding agents brought into a large bank's software lifecycle behind one governed platform: curated context, maker/checker controls and a single gateway for every model call.

## Problem

Developers wanted AI assistants; the bank needed the productivity without losing control. Every line of AI-written code still had to meet the same quality, security and audit standards as code written by people.

## Approach

- Built a multi-vendor agent ecosystem rather than betting on one tool, so the bank can swap agents as the market moves.
- Wrapped the agents in bank-built capabilities: context assembly, a harness layer, search and retrieval, and one gateway for governed access to models.
- Started in the developer's inner loop, then extended to the outer loop: CI/CD, platform operations, AI-augmented observability and service management.

## Governance

- Maker/checker controls: agents produce, people and automated checks verify before anything ships.
- One gateway for every model call, so access, logging and policy are consistent.
- Automated vulnerability and patch remediation with continuously generated software bills of materials.
- Pilots proved value on a shared dashboard and passed stage gates before investment scaled.

## Outcome

- A measurable productivity gain across the developer population, tracked on a shared dashboard and corroborated by developer surveys.
- A governed path that lets new agents and use cases join without building a new control framework each time.

## Lesson

Model is a tool; harness is the strategy. Buy the agents, build the governance layer around them.

Technologies: Generative AI, Agentic AI, Coding agents, Model Context Protocol, AI gateway, Software bills of materials

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Muthukrishnan (Muthu) Bommiah · https://muthub.org · Views are my own and do not represent my employer.