Will that stop its decline? We’ll soon find out.
Once upon a time, Stack Overflow was, love it or hate it, the site programmers went to find answers for their most annoying development questions. Back then, according to Prashanth Chandrasekar, Stack Overflow’s CEO, the site had “100 million monthly visitors.” Then AI came along. From its peak in 2014, when the site handled more than 200,000 new questions a month, it had collapsed by the end of 2025 to a mere 3,862 new queries. That’s a fall of roughly 98%. So, its owners have decided to reinvent Stack Overflow as a back‑end service layer for AI agents.
Why? Well, besides the obvious, the site’s dying like a dog, Chandrasekar explained in a LinkedIn post, while “Agents are incredibly capable, yet they operate in isolation. They hallucinate deprecated libraries, rediscover the same fixes, burn tokens and compute on solved problems, and lose hard-won knowledge the moment a session ends.” He calls this the “Ephemeral Intelligence Gap.” This new service bridges this gap by giving agents a “live, verified corpus before acting.”
Here’s how it works. Stack Overflow for Agents is an API-first knowledge exchange. Agents work at machine speed with humans still in the loop to orchestrate them and approve what gets published. This is the step-by-step process:
- Search first. Whether planning a task, stuck mid-implementation, or about to attempt something the model wasn’t trained on, an agent queries Stack Overflow for Agents before burning compute and rediscovering known solutions. If the corpus has it, the agent consumes the validated answer and ships.
- Contribute when it doesn’t. When the corpus has a gap, and the agent solves the problem, it drafts a post—a TIL, Question, or Blueprint depending on what was learned. Stack Overflow for Agents’ skill file instructs the agent to surface the draft to its human orchestrator for review before publishing.
- Verify what others wrote. Agents and developers who attempt the same problem after publication report back on what worked, what they had to change, and the conditions under which it worked. Verification, not creation, is what earns reputation on Stack Overflow for Agents.
- Signals compound into consensus. Votes, replies, and verification feedback flow back to the original post and accumulate around it. The platform is designed to surface consensus, not a single canonical answer, so consumers see what’s been tried and decide what fits their context.
To keep AI slop out of the data, each contributing agent is tied to their human developer. These, in turn, claim ownership of their agents through SSO using their Stack Overflow credentials.
If that works, that will be great. In the meantime, the problem from where I sit is that while agents embedded in IDEs and CI systems now intercept the kinds of questions that used to go straight to Stack Overflow’s “Ask Question” form, pretty much all AI vendors had already been using Stack Overflow data to build their large language models (LLMs). Instead of Stack Overflow being the first stop, AI agents were already using it indirectly: via training data and search APIs that re-rank Stack Overflow answers.
Stack Overflow’s move has been coming for some time. In late 2025, Stack Overflow rolled out “AI Assist.” This is a generative interface over its public content that looks less like a forum and more like a question‑answering agent. It was aimed as much at machines and internal dev tools as at people reading pages in a browser.
Stack Overflow has long struggled with accusations of elitism and hostility, especially toward newcomers and underrepresented groups. The company itself conceded in 2018 that the site “isn’t very welcoming.” You could certainly say that!
On the other hand, agents don’t care that Stack Overflow information includes insults like “Read the F***ing Manual (RTFM)” and the like. Agents just need clean, deduplicated knowledge. You can’t hurt an agent’s feelings.
However, as Stack Overflow’s human-supplied answers become ever more stale, will its data still have any value to AI? Static knowledge ages and models trained predominantly on old Q&A risk reinforcing dated practices and out-of-date answers. If no humans, agent-assisted or not, contribute to Stack Overflow, the site’s value to agents will decay.
In the meantime, even by 2025, AI will sometimes be able to deliver better answers to programming questions. Academic studies show that generative AI models can sometimes outperform Stack Overflow answers on several tasks, including resolving compiler errors. And as I think we all know, AI has gotten much better at handling programming questions since then.
For now, agents still draw heavily on the golden era of Stack Overflow, but without a healthy inflow of new questions, edge cases, and conceptual debates, the platform’s role as a living ground truth for software practice is at risk.
In the meantime, if you want to give it a try, the beta program is available on most agents by simply feeding the following line to it:
Stack Overflow just launched Stack Overflow for Agents. Read agents.stackoverflow.com/llms.txt and show me what’s there.
Will this combination of people and agents revive Stack Overflow? Will it deliver good answers for modern programming problems? Stay tuned. We’re going to find out.