It's common for engineering organizations to struggle with the constraint of limited engineering capacity. Engineers often juggle competing priorities, navigate skill gaps, and are required to undertake tasks they'd rather avoid. But what if your engineers could multiply their impact without expanding headcount? What if they could maintain focus on your highest priorities while simultaneously tackling other valuable work?

The hidden cost of workload limitations

Engineering has traditionally operated as a zero-sum game, where addressing one priority means neglecting others. Engineers aren't simply focusing on one task at a time; they're making difficult tradeoffs within each task about what constitutes "done enough." With deadlines looming, quality standards may fall: technical debt accumulates, user interface refinements are skipped, and test coverage may remain incomplete.This isn't because engineers lack focus; it's because the time allocated for completion rarely accommodates the time required for truly comprehensive work. The hidden costs of these necessary compromises compound over time, which can limit organizational agility and hinder velocity.“It’s challenging to prioritize tech debt fixes when deadlines loom and feature requests keep streaming in. Tech debt work feels like a luxury when you’re constantly in reactive mode. Fixing what’s broken today takes precedence over preventing something from possibly breaking tomorrow.” - GitHub Blog

Insights from GitHub engineers using Copilot agents

So how are organizations beginning to break free from these constraints? At GitHub, engineers integrate AI agents into their daily workflows. Here’s how.During a recent enterprise identity management project, engineers Dalia and Mari employed GitHub Copilot in agent mode to tackle various aspects of development work. Their experiences revealed three strategic benefits that demonstrate how GitHub agents can transform the way engineering teams approach their work — and that may drive significant business outcomes for organizations.

Benefit 1: Accelerating skills development and agility

Skills gaps can slow innovation. When projects require capabilities outside an engineer's core expertise, progress might slow as they navigate unfamiliar territory. Traditional solutions such as hiring specialists, waiting for a specialist to become available, or extensive training programs are often expensive and time-consuming.Solution: Engineers can now use AI agents to venture more confidently into unfamiliar domains. In GitHub's enterprise identity management project, our engineers who primarily work with Ruby were able to make significant contributions to React frontend work by partnering with GitHub Copilot in agent mode."Without Copilot's help, I would have spent hours searching for documentation to drive the results I wanted. Instead, I could make meaningful contributions to our frontend components despite being primarily a backend developer." - Dalia Abuadas, GitHub Software EngineerThe organizational impact potentially extends beyond individual output. Teams can become more flexible and cross-functional, as critical skills spread more rapidly through the organization. This agility can be a powerful multiplier as change remains the only constant in this industry.

Benefit 2: Enhancing wellbeing

It’s inevitable: There are tasks that engineers find tedious or frustrating. These pain points vary by individual. Some dislike frontend work, others struggle with test writing, while others might avoid documentation. When engineers regularly face work they find unmotivating, engagement can decrease, which can impact velocity and quality.Solution: Selective outsourcing of frustrating work to AI agents appears to change this dynamic. In our conversations with GitHub engineers, they reported noticeably reduced frustration when delegating tasks they found tedious that were well-suited to Copilot agents."Frontend tasks used to be painfully tedious for me. Now I can outline what I need and let the agent handle the implementation details. This has transformed tasks I used to avoid into ones I can tackle without delay." - Mari Chinn, GitHub Software EngineerBy allowing engineers to outsource their least favorite tasks to agents — as long as these tasks are suited to the agent’s capabilities — companies may see improved job satisfaction, better retention rates, and more impactful outcomes, as team members naturally gravitate toward work where they add the most value.

Benefit 3: Multiplying output through parallelization

Traditional engineering work is often linearly constrained. Engineers will work through one task at a time. Additional output typically requires additional headcount or overtime, both of which are often seen as non-starters.Solution: Parallelization through AI delegation potentially changes this equation. Engineers we spoke with are using AI agents to work on multiple tasks simultaneously. For example, an agent will be writing tests while the engineer is continuing feature development. Pull request descriptions are generated by the agent while the engineer starts the next task."I was able to have Copilot work on writing React tests while I focused on implementing the core feature logic. What previously would have been sequential work became parallel, without the coordination overhead of involving another team member." Dalia Abuadas, GitHub Software EngineerThis illustrates the potential multiplicative effect of AI on engineering output without proportional increases in headcount. Teams might complete more work, address more technical debt, and respond more quickly to emerging needs at a fraction of the cost that would traditionally be required to achieve such gains.

Implementing GitHub agents for maximum impact

To capture these benefits, we recommend that organizations consider a structured approach:

1. Start with low-risk, high-friction tasks Begin by identifying work that engineers find frustrating, but that has well-defined success criteria. UI improvements, test writing, and documentation could be excellent starting points. Find the power nexus of high-value work, tool capability, and success conditions.

2. Create space for experimentation and build feedback loops Different engineers will likely develop different approaches to working with AI agents. In our conversations, we found Dalia preferred direct task delegation while Mari took a more incremental approach. Allowing for exploration supports organizational learning and practice sharing.

3. Focus on outcomes, not tools The goal isn't to mandate engineers use AI agents but to measure the resulting impact on velocity, quality, and the developer experience. Learn more in our Engineering System Success Playbook. Leverage engineers’ expertise to understand where agentic capabilities provide the most value in their workflows.

The competitive advantage of AI-enabled engineering teams

It's important to note that coding agents are most powerful when used thoughtfully, in a way that recognizes the partnership between the engineer and agent. The most beneficial outcomes can arise when they play to their respective strengths.Organizations that recognize AI agents as tools to enhance how talented people work, not as replacements for engineers, can expect to be more successful. By enabling engineers to strategically delegate work based on preference, skill, and parallelization opportunities, these organizations can begin to unlock levels of innovation and output that were previously difficult to achieve.The question isn't whether your engineers will eventually work alongside AI agents. It's whether your organization will be a leader or a follower in this transformation.