01
Agentic Developer Workflows
Multi-agent orchestration for real engineering workflows — spec intent, repo context, and test generation working together in production.
Southwest Airlines · Dallas, TX
Turning AI adoption from experimental to production-grade in safety-critical software.
Agentic WorkflowsSpec-Driven DevAI GovernanceAviation Software
350+
Engineers supported across the org
50+
Repositories with zero-touch AI standards
85%+
Automated code quality & test coverage
~20 min
P2 incident resolved with AI + specs
What I work on
AI in the daily craft of engineering — not in the flight systems.
02
Spec-Driven Development
Specifications as living, version-controlled source of truth that AI agents, developers, and testers all reference.
03
Enterprise AI Governance
Freshness checks, conflict detection, and code-spec alignment guardrails that scale AI adoption safely across a large org.
Engineering AI into the daily craft of building software.
I lead AI adoption across a 350-engineer organization in one of the most safety-critical industries in the world — designing the systems that let teams move faster without ever compromising on the rigor aviation demands.
My work sits at the intersection of AI and how software actually gets built — not AI in flight systems, but AI in the daily craft of engineering. I design and deploy agentic developer workflows that make teams more consistent, more reliable, and measurably faster.
In the past year I've shipped nine AI initiatives in production — from Spec-Driven Development to KSPA, an agent that auto-provisions AI coding standards across 50+ repositories. Along the way, we've resolved safety-critical incidents in minutes instead of hours and enforced 85%+ code quality automatically, before a line of code is pushed.
Outside of engineering, I judge international projects through Technovation Girls and am an active member of the Worldwide Women's Association.
Skills & Stack
10 · core
AI / Agentic SystemsKiroAmazon BedrockMCP (Rovo · GitLab)Spec-Driven DevelopmentJavaPythonNode.jsGitLabSonarQube
Portfolio
Real systems, in production, at scale.
Each project below is a working system solving a real engineering problem across a 350-engineer organization — not a demo. Case-study format: the problem, the approach, and the measurable outcome.
01 · Case study
Spec-Driven Development
Specifications as a living, version-controlled source of truth.
KiroGitLabDocs-as-Code
Problem
Incident response often stalls because the spec is stale, missing, or lives in a doc no agent or developer references.
Approach
- Check specs into the repo alongside code, versioned per branch.
- AI agents, developers, and testers all ground on the same spec.
- Every change to code triggers a spec-alignment check.
Outcome
A Priority 2 incident resolved in ~20 minutes because the spec was already there.
02 · Case study
KSPA — Kiro Standards Provisioning Agent
Auto-provisions AI coding standards across 50+ repositories.
KiroJavaNode.jsPythonGitLab
Problem
350 developers, 50+ repos, three languages — keeping AI coding standards, hooks, and steering files in sync manually is unsustainable.
Approach
- Agent runs on branch checkout for Java, Node.js, and Python projects.
- Provisions hooks and steering files — zero manual setup.
- Central updates propagate to every developer, full audit logging.
Outcome
Zero-touch AI standards across 50+ repositories, always current.
03 · Case study
Self-Healing Code Quality
SonarQube violations fixed by an AI agent hook before push.
KiroSonarQubeAgent Hooks
Problem
Static analysis catches issues, but the round-trip between violation and fix eats developer focus.
Approach
- SonarQube integrated directly into the Kiro workflow.
- Agent hook detects and auto-fixes violations pre-push.
- Companion hook generates unit tests to enforce 85%+ coverage.
Outcome
85%+ code quality and test coverage enforced automatically — no manual test writing.
04 · Case study
Spec Integrity Guardrails
Governance that protects AI-grounding documents at scale.
GovernanceCI GatesKiro
Problem
As spec-driven adoption grew, specs began drifting from code, contradicting each other, or silently going stale.
Approach
- Freshness checks flag specs that lag behind code changes.
- Conflict detection surfaces overlapping or contradictory specs.
- Code-spec alignment enforcement blocks drift at the PR gate.
Outcome
Reliable AI grounding as spec coverage scales across the org.
05 · Case study
AI Test Suite Generation via Multi-Agent Orchestration
True multi-agent orchestration in a production workflow.
KiroRovo MCPGitLab MCPMulti-Agent
Problem
Generic AI test generation ignores real repo history and true spec intent, producing shallow coverage.
Approach
- CLI orchestrates Kiro with Rovo MCP and GitLab MCP servers.
- Agents cross-reference spec intent against actual repo context.
- Test cases reflect real code history and requirements, not guesswork.
Outcome
The most technically novel piece of the portfolio — grounded, context-aware test generation.
Speaking
Talks & presentations.
Sharing what actually works when scaling agentic AI across a large, safety-critical engineering organization — with the failure modes, guardrails, and metrics that made it real.
Upcoming · 2026
Kong API + AI Summit 2026
Los Angeles, CA
"When AI Agents Meet Reality: Lessons from Scaling Developer Productivity Across 300 Engineers"
Selected Speaker
Past · 2025
Southwest Airlines AI Bootcamp
Southwest Airlines
"Presenter — Agentic Developer Workflows in Practice"
250+ engineers
Past · Ongoing
Internal Developer Forums
Southwest Airlines
"Spec-Driven Development: From Concept to Org-Wide Adoption"
Engineering org
Invite me to speak
I'm open to conferences, meetups, and internal engineering summits on agentic workflows, spec-driven development, and AI governance in regulated industries.
Request availability →Research & Publications
Bringing what actually works into the literature.
The gap between AI research and AI in production engineering is where most adoption stalls. My writing aims to close it — with primary evidence from a 350-engineer org shipping in a safety-critical domain.
In progressIEEE Software
AI Agentic Developer Workflows in Aviation
A case study on scaling agentic AI across a 350-engineer aviation software organization. Covers spec-driven grounding, multi-agent orchestration for test generation, governance guardrails for AI-authored artifacts, and measured outcomes — including incident-response acceleration and automated quality enforcement.
Venue
IEEE Software
Status
Authoring
Domain
Aviation · Dev Tooling
Coming soon
Additional talks, whitepapers, and conference proceedings will be linked here as they're published. Follow along on LinkedIn for updates.
Recognition & Community
Where the work is seen.
Selected awards, judging roles, and community memberships that reflect the impact of the work beyond a single organization.
- 2026Judging
Gold Judge — Technovation Girls
Judged 11+ international projects from young women building technology solutions to real-world problems, at the Gold judging tier of the global program.
- ActiveCommunity
Member — Worldwide Women's Association (WWA)
Active member of a global network advancing women's leadership across industries and geographies.
- 2026Speaking
Selected Speaker — Kong API + AI Summit
Selected to present in Los Angeles on scaling agentic AI across a 300+ engineer organization.
In practice
“AI belongs in the daily craft of engineering — the specs, the reviews, the tests — not in the flight systems. That's where trust is built.”
Contact
Let's connect.
Fellow engineers, researchers, and leaders thinking about AI in safety-critical, large-scale engineering environments — I'm always glad to talk. Speaking invitations, collaboration on agentic workflow research, and internal engineering summit talks all welcome.
Email
mohanmahima.m1@gmail.com
Preferred for speaking & research inquiries.
LinkedIn
/in/mahima-mohana
Best for professional networking & updates.
Response time
I typically respond within a few business days. For speaking invitations, please include event name, date, audience size, and topic focus.