How agentic AI can multiply team output and free engineers to innovate

Published via GitHub Executive Insights | Authored by Bronte Van der Hoorn

Every team has a backlog: the bugs, minor improvements, and maintenance tasks that never seem to make it to the top of the priority list. But when these tasks get postponed, they quietly increase operational risk and slowly erode the team’s ability to innovate in a timely manner. What if you could finally make progress on some of these neglected items, without pulling people away from your most urgent priorities?

Partnering for progress: How AI agents help engineers address the backlog

GitHub's agentic AI features can begin to clear away this neglected work. Of course, it’s important to note that developers remain essential: AI is most powerful when it augments your team’s capabilities. For example, a small team of GitHub engineers were working on improvements to one of our APIs. They were keen to explore how they could effectively employ GitHub Copilot coding agent as part of their team. One of the use cases that arose was around leveraging Copilot coding agent to work on an issue they found while engineers were focused on other new features.

“I didn’t even realise that someone on our team had spotted a potential problem, recorded it in an issue, and assigned it to Copilot coding agent to take a look while they were focusing on the other tasks in our project. Having this fix created, tested, and ready to release when we needed it saved us a lot of time when we realized the problem was impacting customers!” - Brittany Ellich, GitHub Senior Software Engineer

Real-World impact: Faster response, lower risk

This approach of assigning GitHub Copilot coding agent to a different task while focused on another task paid off when a customer reported the bug the following day. Thanks to a fix already prepared by GitHub Copilot coding agent for when the team member was back in the office the next day, the team could push a resolution to production in just an hour instead of a full day of manual effort. In practical terms, AI can systematically reduce the hidden risks that build up when routine work is postponed.

“It was a great feeling to be able to fix the bug with the change from Copilot coding agent and know that addressing the issue hadn’t impacted our velocity on our original project’s scope” - Brittany Ellich, GitHub Senior Software Engineer

Compounding benefits: quality, trust, and team focus

AI didn’t just help the team catch up. It actually started working on the bug before it was impacting customers, enabling the team to be delivering on their feature work whilst also improving customer experience. Over time, having AI chip away at these “never get to” tasks can make the whole organization more resilient and adaptable.

Using AI where it has biggest benefits

It’s important to acknowledge the importance of allocating the right work to agentic AI. Sometimes changes like large-scale codebase updates are better driven by developers taking the lead with AI by their side. Debugging could take longer if the AI introduced problems that were hard to spot.

Our API team has been finding the most benefit in using agentic AI to undertake small, well-scoped tasks such as adding tests, updating documentation, or tackling localized technical debt. In other words, AI is not a silver bullet. Instead, it’s a powerful tool for freeing your team to focus on what matters most. Let your engineers determine what feels right to delegate, so that they can remain firmly in the driver’s seat.

Employing GitHub’s agentic capability through our playbook

Agentic AI is not about replacing engineers. It’s about multiplying what your engineers can get done with the same resources. With guidelines from GitHub’s Engineering System Success Playbook, you can use AI agents to work on clearing your backlog:

1. Identify:
Surface the backlog items that are always pushed aside in favor of urgent work. These are often simple enhancements, test case coverage, documentation updates, or overlooked technical debt impacting a single file. Ask your teams to consider what items never seem to get done, but would reduce risk or improve quality if tackled.

2. Match backlog items to agents and pilot:
Select a small set of these tasks and use Copilot coding agent or agent mode to address them in parallel with ongoing projects. Set clear boundaries so the AI is working on well-scoped jobs. Gather feedback on what worked and what didn’t. Remember you’re looking for important work that the AI can do well, whilst minimising the time investment required by your engineers. You don’t want them distracted from completing the prioritised work in the sprint.

3. Scale:
As you learn where AI delivers the most value for a team, expand its use to other similar teams and backlog categories. Make AI a standard part of your approach for maintenance, risk reduction, and continuous improvement. Encourage teams to keep refining the process, sharing lessons so the benefits compound across your organization.

The bottom line

“We’re still exploring where agentic capabilities can create more impact. We’ve found a lot of value in investing in tech debt removal and modernization efforts that improve our codebase maintainability.” - Brittany Ellich, GitHub Senior Software Engineer

A key benefit of agentic capabilities is parallelization of work. It’s the ability to finally make progress on more things that matter, not just what’s urgent. By applying a thoughtful, step-by-step approach that is mindful of balancing parallelizing with causing unnecessary distractions, you can unlock significant value and reduce risk with relatively little investment. The backlog doesn’t have to be a graveyard of good ideas. With GitHub's agentic capabilities, it can become a way to deal with little things that make a big difference.

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