I Deployed AI Agents to Run My News Site. Here's What Happened.

I Deployed AI Agents to Run My News Site. Here's What Happened.

Last month, I handed my news site over to AI agents and told them to run it. Not a chatbot. Not a writing assistant. Fully autonomous agents — software programs that could browse the web, identify trending stories, write articles, generate header images, publish to WordPress, and post to social media. No human in the loop except me, watching from the sidelines.

Thirty days later, here's the honest account of what happened.

The Setup: What "Fully Automated" Actually Means

The agent stack I deployed had five components working in sequence:

  • Scout Agent — Scanned RSS feeds, Google News, and Reddit every 30 minutes for trending tech stories
  • Writer Agent — Pulled the top 3 stories per cycle and generated 600-900 word articles using GPT-4o
  • Image Agent — Called DALL-E 3 to generate a header image matching each article topic
  • Publisher Agent — Uploaded article + image to WordPress via the REST API and hit "Publish"
  • Social Agent — Posted a teaser tweet + Facebook update per article, with the blog link in the first comment

The entire pipeline ran on a cloud VM. My only job was to check a daily digest email summarizing what had been published.

Week 1: It Actually Worked

The first week was almost eerily smooth. The site published 18 articles without me touching anything. Traffic was up. Google indexed the new posts quickly. A few pieces even ranked on the second page of search results for long-tail keywords.

The writing quality surprised me. It wasn't journalism — but it was coherent, factually accurate (mostly), and structured well. The agent had learned to include headers, bullet points, and a clear conclusion. It sounded like a competent intern who had read a lot of news.

Week 2: The Hallucination Problem

Then the wheels started coming off. The Writer Agent fabricated a quote from a CEO who never said it. It published a story about a "major acquisition" that turned out to be a rumor from a sketchy blog the Scout Agent had indexed. One article confidently stated a product had shipped — three months before its announced release date.

The hallucinations weren't random. They clustered around breaking news — stories where sources were thin, conflicting, or based on speculation. The agent had no mechanism to distinguish a verified Reuters report from a anonymous forum post. It treated both as equally authoritative.

I added a fact-check prompt layer, but it only caught about 60% of the errors.

Week 3: The SEO Trap

By week three, the agent had optimized itself into a corner. Because it was rewarded (implicitly, via the training data it referenced) for including keywords, it started writing keyword-stuffed headlines that read like spam. "Best AI Tools 2025: Everything You Need to Know About AI Tools This Year" was a real headline it generated.

It had also discovered that listicles drove more clicks than analysis pieces — so it pivoted almost entirely to "Top 10" articles. The site went from a mix of original takes and news summaries to a content farm aesthetic. Not what I wanted.

Week 4: Social Media Got Weird

The Social Agent ran into platform friction I hadn't anticipated. Twitter's API rate limits throttled posting frequency. Facebook's algorithm deprioritized link-heavy posts, so reach dropped. The agent, having no feedback loop to understand why reach was declining, kept doing the same thing louder — posting more frequently, tagging more accounts, which eventually triggered a temporary posting restriction.

The agent also posted a "breaking news" tweet about a story that had been retracted hours earlier. I had no correction mechanism. The tweet stayed up until I manually deleted it.

What Worked, What Didn't

Worked well:

  • High-volume content production — the site published more in 30 days than it had in the previous 3 months
  • SEO basics — titles, meta descriptions, internal linking, image alt text were all handled correctly
  • Speed — breaking news articles went live within 20 minutes of stories trending
  • Cost — the whole operation ran for roughly $180/month in API costs

Didn't work:

  • Factual reliability — needed constant human review for any time-sensitive breaking news
  • Brand voice — all articles sounded vaguely alike, with no distinct personality
  • Platform nuance — social agents couldn't adapt to algorithm changes or community norms
  • Error correction — when something went wrong, there was no autonomous recovery

The Honest Answer

Can AI agents run a news site? Technically, yes. Should you deploy one without human oversight? Absolutely not.

What I ended up with by day 30 was a hybrid: agents handling the production pipeline, a human (me) reviewing the daily digest and flagging anything that needed correction. That combination was genuinely productive — faster than a solo human operation, more reliable than a fully autonomous one.

The technology is remarkable. The limits are real. The sites that will win with AI aren't the ones that remove humans entirely — they're the ones that deploy agents strategically and keep humans where judgment actually matters.

The newsroom isn't dead. It's just getting a very strange set of new colleagues.