Loyola Marymount cuts AWS Lambda update cycle from two months to half a day using Kiro
Loyola Marymount University has reduced the time required to update Python runtime versions across more than 500 AWS Lambda functions from two months to half a day, according to a case study published by AWS Public Sector.
The shift follows LMU’s adoption of Kiro, a spec-driven artificial intelligence development tool from Amazon Web Services. The university’s two-person cloud team used Kiro to inventory, remediate, test, and validate Lambda functions across dozens of AWS accounts, embedding institutional standards into reusable specification files.
AWS Public Sector highlighted the results in a LinkedIn post, stating that LMU’s cloud team cut Lambda runtime updates “from 2 months to half a day using AWS Kiro.”
Scaling cloud operations with a small team
LMU, a private university in Los Angeles serving more than 10,000 students, has built its AWS environment over the past decade under the leadership of Rob Larmon, Manager of Cloud and Automation. As the institution’s cloud footprint expanded to multiple accounts and workloads, maintaining governance and updating legacy code became increasingly resource-intensive.
When AWS launched Kiro’s pilot program in July 2025, Larmon joined the waitlist. He said: “I could take all our standards, requirements, and the way we do things at LMU and put them into spec files.”
Unlike traditional AI coding assistants that rely on conversational prompts, Kiro’s spec-driven model allows teams to encode institutional standards once and reuse them across projects. According to AWS, this reduced drift and eliminated the need to restate requirements repeatedly.
Larmon first tested Kiro by updating generic Shibboleth CloudFormation templates that did not align with LMU’s tagging and security standards. He said: “These were templates I would have been proud to build myself.”
Automating runtime remediation
LMU identified more than 500 Lambda functions running outdated Python runtimes, some built in-house and others delivered by vendors. Larmon asked Kiro to build a cross-account scanner, tester, and remediator tool that could inventory functions and generate an executive summary.
In four to six hours, Kiro generated the scanner and embedded LMU’s governance structure for reuse. The tool then ran across accounts for half a day, producing updates, executing checks, and flagging functions ready for production deployment.
“The drudgery Kiro saved,” Larmon said. “I would not have attempted to do this at all before.”
He added that Kiro now delivers cloud solutions four to 10 times faster than previous workflows.
Governance, documentation, and wider rollout
Beyond runtime updates, LMU uses Kiro to generate compliant infrastructure templates in AWS CloudFormation, Terraform/OpenTofu, and AWS Cloud Development Kit, aligned with the university’s standards. Larmon said: “LMU has consistent, enterprise-grade deployments across all AWS accounts through Kiro’s steering and standards model. This eliminated much of the drift, inconsistency, and re-work LMU used to fight.”
Kiro also integrates with Confluence and Jira to align documentation and work tracking with the live state of the cloud environment. Larmon said it provides “documentation, diagrams, and work tracking that reflect the actual state of our cloud environment, not whatever was last manually updated.”
He now uses Kiro more than two hours per day and said it feels like he has “a three-to-10-person development team at my fingertips.”
Looking ahead, LMU plans internal workshops and lunch-and-learns to expand adoption across technical teams. Larmon said: “The faster we can get teams on these tools, the faster we’ll see tremendous gains across any platform we manage. This is a real game-changer.”
As universities continue migrating core systems to cloud environments, LMU’s case illustrates how spec-driven AI tools are being positioned not only as coding assistants, but as governance and compliance engines for small teams operating at enterprise scale.
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