The artificial intelligence boom created a massive market for high-performance computing. AMD, which builds the CPUs and GPUs essential to this infrastructure, needed to accelerate delivery to keep up with explosive demand. To do so, the company consolidated over 20,000 engineers onto GitHub Enterprise, replacing a fragmented collection of legacy tools with a single unified platform. Building on this foundation, the company deployed GitHub Copilot to automate complex development tasks using autonomous AI agents.

The combination of a unified platform and agentic AI delivered three consecutive years of double-digit productivity gains and enabled an "always-on" workflow where agents identify and fix bugs around the clock. As a result, AMD automatically triages over 1 million nightly simulations and has compressed feature development cycles from weeks to days.

Unifying 20,000+ engineers on a single platform

A fundamental change in semiconductor architecture moved AMD from manufacturing single silicon dies to engineering "chiplets"—products composed of multiple dies that function as integrated systems. This change increased the engineering burden. According to Alex Starr, Corporate Fellow at AMD, the complexity of verifying that these integrated systems function correctly created a ratio of roughly four verification engineers for every one hardware designer.

To sustain this workflow without exponentially increasing headcount, the company looked to unify its siloed hardware, firmware, and software teams. Moving away from a historically fragmented environment of disparate systems and approaches, AMD established a single source of truth. "We use GitHub Enterprise as the foundational platform across hardware and software," says Starr. "The hardware world is adopting more software practices to meet the opportunity of AI to accelerate things."

We use GitHub Enterprise as the foundational platform across hardware and software. The hardware world is adopting more software practices to meet the opportunity of AI to accelerate things.

Automating verification at scale

With its hardware and software repositories consolidated on a single platform, AMD deployed GitHub Copilot to address its most resource-intensive bottleneck: pre-silicon validation. Before a chip is manufactured, AMD runs approximately 1 million virtual simulations every night to test the design. Previously, triaging the failure logs from these runs required significant manual engineering hours each morning to distinguish between real bugs and test artifacts. Today, that manual morning triage time has been reduced to zero.

AMD now employs GitHub Copilot coding agent to automate this triage. This agentic workflow analyzes simulation logs, cross-references historical data to identify known issues, and proposes fixes. "We use GitHub Copilot to do a lot of that heavy lifting for us," Starr notes. "When engineers come back in the morning, they're able to see, 'Okay, most of these have been taken care of.'"

To make this possible, AMD collaborated with GitHub to train the model on hardware description languages like Verilog. While general-purpose AI models are proficient in software languages, they initially lacked the specific context required for hardware design. This partnership allowed AMD to fine-tune the tool for its specific engineering environment.

We use GitHub Copilot to do a lot of that heavy lifting for us. When engineers come back in the morning, they're able to see, 'Okay, most of these have been taken care of.

Compressing feature cycles from sprints to days

While GitHub Copilot handles verification overnight, the benefits of the unified platform extend into the daily workflow of human engineers. By integrating agentic workflows directly into the development environment, AMD has shifted the role of AI from a simple autocomplete tool to a comprehensive, autonomous tool.

"The work we do at AMD is incredibly complex, and GitHub Copilot helps augment our engineers to handle that complexity," says Xavier Galvez, Program Director, Internal Use Of AI. "It's not replacing the engineer; it's removing the toil."

The work we do at AMD is incredibly complex, and GitHub Copilot helps augment our engineers to handle that complexity. It's not replacing the engineer; it's removing the toil.

This shift has produced measurable changes in developer velocity. Internal data shows that when engineers utilize agentic workflows—where the AI proposes, tests, and validates code autonomously—acceptance rates for AI-generated code jump to over 50% in some projects, compared to roughly 30% with standard completion tools. Because engineers can run multiple agent sessions in parallel, they generate significantly more output while focusing on creative and architectural work.

For DevOps engineer Anna Safonov, this capability has compressed timelines that previously felt immovable. Recently, she used Copilot to generate a dual-language script with full documentation in just 10 minutes—a task that normally takes a day and a half. "Work that used to take a full sprint can now be accomplished in days with Copilot," she says. "It allows us to focus on the architecture and the logic, rather than the syntax." This always-on capability has fundamentally changed the team's capacity. "When you're sleeping, work is still happening," notes Galvez. "The five-day workweek has effectively extended to a seven-day workweek."

Work that used to take a full sprint can now be accomplished in days with Copilot. It allows us to focus on the architecture and the logic, rather than the syntax. When you're sleeping, work is still happening. The five-day workweek has effectively extended to a seven-day workweek.

Automatic security vulnerability resolution

Accelerating development often comes with a trade-off in security, but AMD is using AI to invert that relationship. By deploying GitHub Advanced Security alongside Copilot, the company is moving from merely finding vulnerabilities to automatically fixing them.

This capability is powered by Copilot Autofix, which analyzes security alerts and generates code suggestions to remediate them. This has allowed AMD to launch what they call a "historical agent"—an automated workflow specifically tasked with combing through years of legacy code to identify and patch dormant vulnerabilities. As a result, approximately 70% of vulnerability classes are now resolved automatically.

"Copilot Autofix turns long lists of vulnerabilities into fixes without requiring 10,000 engineer hours," says Galvez. By offloading this remediation to AI, AMD ensures that its security posture strengthens in lockstep with its development velocity, rather than becoming a bottleneck.

Copilot Autofix turns long lists of vulnerabilities into fixes without requiring 10,000 engineer hours.

Pioneering self-healing software and autonomous ecosystems

As AMD accelerates its roadmap, it is closely monitoring the impact of AI on code integrity. By tracking the "maintainability index" of AI-generated code, the company has confirmed that the surge in velocity has come with no measurable drop in quality.

This confidence is enabling a more ambitious vision: self-healing software where AI agents detect defects in the field, understand the root cause, propose a fix, and surface it for review before users ever notice a problem. "I've seen one agent looking at the defect ticket, another agent looking at the code making the fix, another agent testing the code," says Galvez. "If all of these agents work hand in hand, you effectively have an autonomous ecosystem."

When we look at the productivity gains we are targeting with these agentic workflows. We're not aiming for 5 or 10 percent. With GitHub Copilot we are talking about exponential improvement.

"It's a foundational, transformational technology. The best days are still to come," says Alex Androncik, Senior Director of Software Development. As AMD continues to push the boundaries of agentic AI, the scale of their ambition is clear. "When we look at the productivity gains we are targeting with these agentic workflows," concludes Galvez, "we're not aiming for 5 or 10 percent. With GitHub Copilot we are talking about exponential improvement."