Before, when developers handled issues directly, even a simple question meant re-reading context, crafting a response, double-checking nothing was missed. Trying to remember if a similar issue came up before, digging through old threads to see how it was handled. This triage and analysis work quietly, steadily drains time and focus—regardless of how hard the actual issue is.
I thought: maybe AI should be doing this, not developers.
So I applied an agentic AI workflow to real work—analyzing GitHub issues, responding, and in some cases, even fixing code.
I compared the old human-driven workflow against the agentic AI workflow across 10 GitHub issues.
The criteria were simple.
From the organization's perspective:
→ How much time does it actually take to resolve one issue?
From the user's perspective:
→ How fast do they get a response after filing an issue?
The results were clear.
The old workflow took an average of 2.4 developer-days per issue.
After agentic AI handled triage and first-pass resolution, that dropped to 0.8 days.
That's a 66.7% cost reduction for the organization.
The change from the user's side was even more dramatic.
Previously, first response took over 3 days on average.
With agentic AI, first response came in 4-5 seconds.
Users now get near-instant feedback after filing an issue.
But what impressed me most: even with just simple policies and a basic domain knowledge structure, the agentic AI workflow stayed consistent in knowing what to do and what not to do.
- Simple questions got answered and closed
- Small fixable bugs got PRs created directly
- Risky or complex issues got handed to humans without hesitation
Bottom line: after applying agentic AI, only 4 out of 10 issues actually reached developers.
About 60% were handled without any developer involvement.
Developers no longer need to spend hours on repetitive triage, responses, and context reconstruction.
Instead, they define the boundaries of automation and design how AI and humans should collaborate.
I believe that's becoming the new role of developers.
Next up: building a dashboard to see "how is agentic AI performing right now" at a glance.
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