TL;DR overview
- AI-first engineering at Cisco uses autonomous agents and SonarQube to eliminate large-scale technical debt.
- A three-month pilot program successfully cleared 27,000 technical debt issues, boosting productivity up to 3x.
- The autonomous agent "Coda" independently resolves Jira stories by patching code and generating pull requests.
- Cisco ensures code quality at scale through a structured investigate, plan, and implement verification loop.
Gartner® names Sonar a Magic Quadrant™ Leader
Cisco has overhauled its internal engineering strategy, rebranding its core productivity units to "AI-first engineering" and deploying autonomous agents to eliminate tens of thousands of technical debt issues across its massive developer landscape.
Speaking at the Sonar Summit 2026, Cisco’s Distinguished Engineer of AI-First Engineering Stephen Byrnes revealed how the networking giant is moving beyond simple copilot assistants toward an agentic SDLC capable of maintaining rigorous standards for thousands of developers.
The shift to AI-first engineering
The organizational shift began 18 months ago when Cisco’s Engineering Productivity team realized that AI was no longer a peripheral tool, but the cornerstone of their roadmap.
"It didn't take long, as we were seeing these models and tools improving, that we decided this is the centerpiece of the strategy. That’s when we basically rebadged the team to be called ‘AI-first engineering’." - Stephen Byrnes, Distinguished Engineer at Cisco
This wasn't just a change in name. Cisco established internal guilds that now attract over 500 engineers monthly and a Webex community of 4,000 members sharing real-time AI breakthroughs. The goal was to move quality from table stakes—something everyone knew they had to do—to an engineering accelerator.
Eliminating 27,000 tech debt issues in three months
One of the most striking outcomes of this transformation was a pilot program aimed at tech debt zero. By rotating an AI-specialized engineer into a high-priority partner program, Cisco was able to align SonarQube’s telemetry with AI coding assistants.
The results were immediate: the team cleared roughly 27,000 technical debt issues in a single three-month window.
"We're not just keeping quality high, but we're actually able to go faster because we've cleared a lot of that tech debt that's been there for some time." - Stephen Byrnes, Distinguished Engineer at Cisco
Byrnes noted that some teams are seeing productivity gains of up to 3x by using these automated cleanup workflows.
Meet Coda: The autonomous teammate
Central to Cisco's strategy is Coda, a custom-built autonomous agent that Byrnes describes not as a tool, but as a remote employee.
Unlike standard IDE plugins that wait for a human to type, Coda operates as a full user within Cisco’s Jira environment. A developer can assign a Jira story to Coda, directing it to a specific SonarQube instance and repository.
"Coda will wake up, go to SonarQube, pull out all the details of that particular incident, develop a plan, and start working away like a normal human staff member." - Stephen Byrnes, Distinguished Engineer at Cisco
The agent handles the slog—patching, upgrading dependencies, and fixing code quality issues—before generating a pull request (PR) for human review. This shift allows Cisco’s high-cost tech talent to focus on architectural design rather than syntax-level maintenance.
AI engineering verification at scale
As code volume increases due to AI generation, Cisco is leaning on a trust and verification layer to prevent a productivity paradox where humans are overwhelmed by PR reviews.
The company's current workflow follows a structured investigate → plan → implement loop:
- Investigate: Agent is fed context from SonarQube issues and API documentation.
- Plan: The agent produces a markdown document detailing the fix and verification checks.
- Implement: A fresh agent session executes the plan, ensuring no new quality issues are introduced.
"AI does make it easier to deliver additional velocity, but if you just focused on AI engineering and not the rest, you probably would see quality going down. You’ve got to work on all those parts together." - Stephen Byrnes, Distinguished Engineer at Cisco
Byrnes concluded that the move to AI-native engineering is actually improving code hygiene, as agents require well-structured documentation and clean ReadMe files to be successful—the very things human developers often neglect.
Want to see the full technical breakdown of how Cisco achieved these results? Read the full Cisco Case Study here.
Solving the AI tech debt challenge is why recognition like Gartner's Magic Quadrant for Technical Debt Management matters to us, not as validation that the problem is solved, but as confirmation that the industry is taking it as seriously as we are.
