Sharing relevant industry news is one of the most reliable ways to stay visible without constantly producing original content. The problem? Manually scanning 20 sources every morning, deciding what's worth posting, and writing a hook takes an hour you don't have. An AI content curation agent does that work for you — pulling from the sources you trust, filtering out noise, and drafting posts your audience actually wants to read.
This guide walks through building a news curation agent from scratch: where it pulls sources, how it scores relevance, and how it hands you post-ready drafts every day. No code required for most of it.
What an AI News Curation Agent Actually Does
An ai feed curator is a workflow that runs on a schedule and performs four jobs in sequence:
- Collect — pulls fresh articles from RSS feeds, newsletters, and social accounts you define.
- Filter — removes duplicates, off-topic pieces, and anything below a relevance threshold.
- Summarize — condenses each keeper into a two-sentence takeaway.
- Draft — writes a ready-to-post caption with your angle, plus a link and hashtags.
The goal isn't full automation where posts publish untouched. The best setups keep a human approving the final queue — you get 90% of the work done for you and spend five minutes picking winners. That balance is what makes automated content curation sustainable instead of robotic.
Step 1: Define Your Sources and Topics
Garbage in, garbage out. The single biggest factor in curation quality is the source list. Aim for 15 to 30 high-signal sources — enough for daily volume, few enough to stay quality-heavy.
Where to pull from
- RSS feeds from trade publications, competitor blogs, and analyst sites. Most sites still expose a feed at /feed or /rss.
- Newsletters — forward them to a dedicated inbox your agent can read.
- Social accounts — specific journalists, founders, or industry voices worth monitoring.
- Google Alerts for named keywords, delivered as RSS.
Then define your topics as a short list of themes with example keywords. For a fintech brand that might be: payments infrastructure, embedded finance, regulation, fraud prevention. The agent uses these to decide what belongs in front of your audience and what to skip.
Rule of thumb: if you wouldn't share it in a Slack channel with your team, your agent shouldn't surface it. Set the bar there.
Step 2: Build the Filtering Logic
Raw feeds are noisy. A publication might push 40 articles a day and only three matter to you. This is where the agent earns its keep. Give your model a scoring prompt that rates each article 1–10 on relevance to your defined topics, then only keep items above a threshold — start at 7 and tune it.
A good scoring instruction looks like this:
Score this article 1–10 for how relevant it is to a [B2B SaaS] audience interested in [sales automation, RevOps, AI tooling]. Penalize: press releases, thin listicles, articles older than 5 days, and anything paywalled. Return only the number and a one-line reason.
Layer in a few hard rules on top of the AI score:
- Dedupe by headline similarity so five outlets covering the same story don't all make the cut.
- Recency cutoff — nothing older than 3–5 days unless it's evergreen.
- Source caps — max two items per source per day so one prolific blog doesn't dominate.
If you've already built other AI workflows, this filtering stage will feel familiar. The same principles that power a good AI agent workflow for content creation apply here: clear scoring criteria, tight guardrails, and a human checkpoint before anything ships.
Step 3: Summarize and Add Your Angle
A link with no context gets ignored. Curation adds value when you tell people why the article matters. Have your agent generate a summary plus a point of view, not just a headline restated.
Prompt the agent for a real take
Instead of "summarize this article," instruct it to produce a post structured as:
- Hook — the one insight worth stopping for.
- Why it matters — a sentence tying it to your audience's world.
- A question or prompt — to invite replies.
Feed the agent your brand voice guidelines so drafts don't sound generic. A concrete example for a marketing-ops audience:
New data shows 60% of teams still route leads manually — and lose an average of 10 hours a week doing it. If your handoff between marketing and sales still lives in a spreadsheet, this is your sign. How are you routing yours right now?
That's shareable because it leads with a number, ties to a pain point, and ends with a question. Train your agent on 5–10 examples like this and quality jumps immediately.
Step 4: Schedule and Route the Drafts
Decide how the finished drafts reach you. Two workable models:
- Daily digest — the agent posts 5–8 ranked drafts into a Slack channel or shared doc every morning. You approve or edit in one sitting.
- Auto-queue with approval — drafts drop straight into your publishing queue as unpublished, and you approve the ones you like.
The second model is faster once you trust the agent. Platforms built for this let you run the whole pipeline — sourcing, scoring, drafting, and approval — in one place. SocialAgentry's features include scheduled agents that curate from your sources and drop post-ready drafts into an approval queue, so the daily grind of finding and formatting content disappears.
However you route it, keep a human in the loop for the first month. Approve manually, note which drafts you rejected and why, and feed that back into your scoring prompt. Curation agents get noticeably sharper after a few weeks of correction.
Step 5: Close the Loop With Engagement
Curation isn't just broadcasting — the replies matter. When someone comments on a shared article, that's a conversation your team should join fast. If volume is high, pair your curator with an agent that handles the first response, which you can set up using the approach in using AI agents to respond to comments and DMs.
You can also connect curation to your pipeline. If a prospect engages with an industry post you shared, that's a warm signal worth acting on. Wiring your CRM into the flow — covered in connecting your CRM to social media with automation — lets you track which curated content drives real conversations, not just likes.
Metrics That Tell You It's Working
Track these weekly to know if your news curation agent is pulling its weight:
- Approval rate — what percentage of drafts you actually post. Below 40% means your scoring threshold or sources need tuning.
- Engagement per curated post vs. original content. Good curation often matches or beats original posts on saves and shares.
- Time saved — track it honestly. Most teams recover 4–6 hours a week.
- Source performance — which sources produce your best posts. Prune the ones that never make the cut.
Common Mistakes to Avoid
- Too many sources. Forty feeds sound thorough but bury signal in noise. Start narrow and expand.
- Sharing without a take. A naked link performs worse than a curated one with a one-line insight. Always add the angle.
- Set-and-forget. An unmonitored agent drifts off-topic within weeks. Review its picks and correct.
- Ignoring recency. Nothing kills credibility like sharing week-old "news." Enforce a tight cutoff.
Once your curation agent is humming, it becomes one node in a bigger system. Many teams pair it with lead workflows — see marketing automation workflows every team should set up — so the content you share feeds directly into nurture and follow-up instead of living in isolation.
FAQ
How much content should a curation agent post per day?
Start with one to three curated posts a day, mixed with your original content. Quality beats volume — flooding your feed with links trains your audience to scroll past. As you learn which posts land, you can increase frequency, but most brands see the best results keeping curated content around 30–40% of their total output.
Will an AI feed curator make my brand sound generic?
Only if you skip the voice training. Feed the agent 5–10 examples of your best-performing posts, a short do's-and-don'ts list, and clear instructions to add a point of view rather than restate headlines. With those guardrails, drafts read like your team wrote them. The human approval step catches anything that slips through.
Can I automate curation completely without reviewing posts?
You can, but don't — at least not at first. Run a human approval step for the first month while the agent learns your standards. Once your approval rate consistently clears 70–80%, you can shift lower-risk sources to auto-publish while keeping review on higher-stakes topics. The goal is confident automation, not blind automation. Want to try it? Try SocialAgentry free and build your first curation agent in an afternoon.