Troubleshooting Data Stream Misfires

When the content misses the mark, sometimes the problem is upstream.

If your Agent is returning off-topic, low-quality, or oddly specific content — the issue might not be the Agent or the prompt. It might be the Data Stream feeding it.

Data Streams power research-based Agents by pulling in live articles from across the internet. They give the AI context: what’s trending, what’s factual, what to prioritize.

But if a Data Streams is misconfigured — or just not delivering enough signal — the output won’t have the right material to work with.

This guide walks you through how to diagnose and fix Data Streams misfires.

What a Data Streams Misfire Looks Like

Here’s what you might see when a Data Streams isn’t working as expected:

The Agent produces vague or generic content
Outputs are oddly narrow or hyper-specific (e.g. “The ban on street vending in Ottawa”)
Content doesn’t match the intended topic or tone
No content is returned at all, or the article says “not enough signal found”
It’s clearly based on outdated or irrelevant sources

These issues usually stem from what the Data Streams is (or isn’t) finding at the time of generation.

Common Causes

1. Too Many Filters

Overlapping inclusion/exclusion rules or narrow keyword clusters can choke the signal.

Fix:
Try simplifying your filter logic. Remove overly specific included items or boosted penalties, and regenerate the stream preview to see if it improves.

2. Not Enough Sources

If your Data Streams is pulling from too small a pool of sites, it might not find anything timely or useful — especially within a 24–48 hour window.

Fix:
Add more relevant sources to your stream, or remove unnecessary source restrictions. You can also switch from “Priority Only” to “Expanded Sources” mode.

3. Conflicting Item Logic

Sometimes an item is both boosted and penalized, or included and excluded in different filter groups.

Fix:
Review the score bar logic and resolve contradictions. The same keyword or entity shouldn’t appear in both “Boosted Items” and “Excluded Items.”

4. Expired or Broken Feed URLs

In some cases, custom sources or added RSS feeds may no longer be live or valid.

Fix:
Test the URL manually, or review your Source Filters. If a source consistently fails to load in previews, remove it or replace it with a more reliable one.

5. Wrong Time Window

If your Data Streams is set to a short publication window (e.g. 24 hours), it may miss slower-moving stories or niche coverage.

Fix:
Try expanding the Publication Time Frame to 72 hours or more. You’ll often see a dramatic improvement in variety and relevance.

How to Test Your Stream

Go to the Data Streams panel in Studio
Click into the affected stream
Use Preview Content to see what articles are currently being returned
Look at the timestamps, topics, and source mix
Adjust filters or scoring logic if the articles don’t match your intent

Pro Tip: Use Clear Filter Group Labels

Name your filter groups clearly — e.g. “Included Items – Financial,” “Boosted Sources – News,” or “Excluded Entities – Politics” — so you can quickly audit what's driving your stream behavior.

Clarity here helps prevent downstream confusion later when outputs seem “off” but the prompt and Agent config are technically correct.

Summary

If an Agent output feels disconnected, underdeveloped, or irrelevant, don’t start by rewriting the prompt. Start by checking the Data Streams.

Use Preview Content to see what the AI is seeing
Expand your time frame or widen your filters if signal is weak
Clean up contradictions in item logic
Make sure your source list is reliable and broad enough

You’re not just prompting — you’re curating the raw material behind the scenes. When the Data Streams is strong, the Agent gets smarter automatically.