Beyond the Autocomplete: How IEEE Columbia Tackled Enterprise Agentic AI

Software engineering is undergoing a quiet, fundamental shift. For years, artificial intelligence in software development has largely been framed as an upgraded autocomplete tool suggesting the next line of syntax. But at a technical session hosted by the IEEE Computer Society Columbia Section Chapter (Region 3), a different narrative took center stage: the rise of agentic AI in enterprise architecture and academic preparation.

Led by Chapter Chair Sairohith Thummarakoti, the event brought together approximately 20 attendees, including UNC Greensboro Computer Science faculty members, Industry Advisory Board representatives, and local tech professionals.

Live Demo: Enterprise Application Modeling with Pega Blueprint

A central highlight of the event was a live demonstration of Pega Blueprint delivered by Sairohith Thummarakoti. The demo showcased how generative AI and large language models can automatically convert high-level business requirements into structured enterprise application designs.

By generating initial workflows, data structures, and application models automatically, agentic AI tools accelerate early solution design and significantly reduce traditional development overhead.

Moving Beyond Simple Syntax Generation

The core takeaway challenged the conventional view of AI coding tools. While speed and syntax completion are useful perks, the true value lies in supporting complex, multi-step development tasks from debugging and testing to full architectural design.

"One of the most interesting insights is that the greatest value of AI coding agents is not simply faster code generation, but their ability to support complex, multi-step development tasks,” said Thummarakoti. “This challenges the traditional view of AI as just a coding assistant and demonstrates its potential as a collaborative engineering tool."

This shift allows software teams to reallocate focus toward high-level system architecture, logic validation, and cross-functional problem solving.

Industry-Academia Alignment for Next-Gen Careers

As AI autonomous tools reshape development workflows, academic institutions face the challenge of aligning curricula with emerging industry expectations. The presence of university faculty provided a collaborative setting to discuss workforce readiness.

"The presentation gave us valuable insights into how technology and the job market are changing. It helped professors better understand the skills students need to succeed and prepare them for the next generation of AI-driven careers,” said Shivani Reddy Palley, Lecturer in Computer Science, University of North Carolina at Greensboro (UNCG).

To maintain code quality, security, and reliability when integrating AI agents into real-world projects, the group advocated an incremental strategy:

1. Low-Risk Pilots: Introduce AI agents into small, isolated tasks first.
2. Automated Verification: Pair AI output with rigorous automated test suites and required human review.
3. Measured Scaling: Expand deployment across enterprise projects based on clear performance metrics.

Check out IEEE Computer Society AI Resources for information, tools, and benefits available to you.