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To build an AI agent system that covers about 100 items a day from RSS, Google News, Reddit and YouTube, split the work into two layers. Source adapters handle what is specific to each platform: access route, pagination, quotas, retries and errors. A shared pipeline then turns every result into one normalized record, removes duplicates, filters for relevance, fetches full pages only where they are needed, and produces an evidence-backed brief that a person reviews before anything is published or acted on.
Treat 100 items a day as a planning target, not a measured throughput. No independent published benchmark shows that an agent pipeline reliably handles that volume across these four source types, so size the system by testing it against your own sources and keep the figure as a goal you check against real logs.
What the 100-a-day figure does and does not mean
One hundred items a day works out to about four items an hour (100 ÷ 24 ≈ 4.2). Raw volume at that level is modest. The hard part is keeping four different sources reliable, each with its own failure modes. The number is useful for sizing: it tells you how many feeds, queries and channels to watch, how much fetch and model budget to plan for, and how much human review time to expect.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsBe precise about what you count. Raw hits from four sources overlap heavily, so 100 raw hits and 100 unique items after deduplication are very different workloads. Decide which one the target refers to before you size quotas or review capacity. The title says “blogs,” but the workload is mixed source items; a Reddit post or a YouTube video counts toward the same total.
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The pipeline at a glance
The flow below follows the pattern described in two implementation examples, one matching this topic and one published on DEV Community. They are practitioner examples, not official guidance from Google, Reddit or any other platform.
- Source registry: a configuration file or table listing every feed, query, channel and subreddit you monitor.
- Source adapters: one per platform, each fetching items and handling its own pagination, authentication, quotas, retries and errors.
- Normalized records: every item converted to one shared schema.
- Deduplication on platform IDs first, canonical URLs second.
- Relevance filtering with cheap checks (date, source, keywords, language) before any expensive step.
- Selective full-page or metadata fetch, only for items that passed the filter.
- Summarization, classification and scoring that produce structured claims with evidence.
- Evidence-backed brief assembled from those claims.
- Human review of uncertain, high-impact or conflicting findings.
Observability runs across all nine stages, so that a silent failure in one adapter cannot look like a quiet news day.
Step 1: Keep sources in a registry, not in code
Store each monitored source as data. Adding a feed should mean adding a record, not writing a new script. A workable registry entry carries the following fields:
- Source type (RSS/Atom, Google News, Reddit or YouTube) and the platform identifier: feed URL, query string, subreddit name, or channel or playlist ID.
- Language and region, which matter for Google News and for filtering.
- Poll interval in minutes, set per source rather than globally.
- An enabled flag, so you can pause a source without deleting its history.
- A terms-review date recording when you last checked that platform’s current terms for your use case.
{
"source_id": "rss-example-engineering",
"source_type": "rss",
"feed_url": "https://blog.example.com/feed.xml",
"poll_interval_minutes": 60,
"language": "en",
"enabled": true,
"terms_reviewed_on": "2026-10-01"
}The example above is illustrative; the domain is a placeholder for your own feed.
Step 2: Build one adapter per platform
Each platform works differently, so resist a single generic scraper. The table compares the four sources on the seven axes that matter for a maintainable adapter. Where the sources reviewed for this guide do not establish a value, the cell says so.
| Axis | RSS / Atom | Google News | YouTube Data API | |
|---|---|---|---|---|
| Access route and permitted use | Publisher-supplied feed; use is governed by each publisher’s access rules | Query and topic RSS feeds. A secondary implementation example reports that Google publishes this RSS for personal use and warns about commercial use; official Google terms were not confirmed for this guide | Official OAuth API recommended for production by a secondary implementation example. Current official terms not verified for this guide | Official Data API with search over video, channel and playlist resources. Use terms not verified for this guide |
| Freshness and polling | Set per feed in the registry. ETag or Last-Modified conditional requests where the server supports them | Not stated in the sources reviewed | Not stated in the sources reviewed | ETags allow an HTTP 304 Not Modified response when the cached version is still current |
| Quotas, rate limits and pagination cost | Not stated; manage load with per-feed intervals and backoff | Not stated in the sources reviewed | Not stated; official rate limits not verified for this guide | search.list has a daily quota of 100 calls in the official reference checked in 2026. The revision history records a June 2026 transition to granular quotas, so verify your project’s live quota |
| Stable identifiers | An item ID or link where the feed supplies one; canonical URL as the fallback | Redirect links that may need resolving to the publisher page before canonicalizing | Not stated in the sources reviewed | Platform resource IDs for videos, channels and playlists as the primary key |
| Metadata completeness | Links and summaries; full article text is not guaranteed | Feed items that point to publisher pages; completeness not stated | Not stated in the sources reviewed | Partial resources are documented in the official API overview, so requests can be limited to the parts you need |
| Full-content retrieval | Full page fetch only for items that pass the relevance check, subject to publisher access rules | Full text only from the resolved publisher page, subject to that publisher’s rules | Not stated in the sources reviewed | Not stated in the sources reviewed; check the API terms before fetching beyond search metadata |
| Failure and coverage signals | Last successful poll, HTTP status and item age per feed | Last successful poll, redirect-resolution failures and item age per query | Last successful poll, error responses and item age per subreddit | Last successful poll, quota usage, 304 responses, errors and item age per channel or query |
The table shows why a fair numeric ranking of the four sources is not possible. Only the YouTube quota and conditional-request behavior are documented in official platform material reviewed for this guide. For the others, plan with conservative polling and verify before you commit.
RSS and Atom feeds
Treat a feed as a discovery index. It tells you that something was published and where; it does not hand you the article. Use the feed summary for first-pass filtering, and reserve full-page fetches for items that look relevant.
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Poll each feed at an interval that fits its publishing rhythm, and record the interval in the registry. When a feed supports conditional requests, store the ETag or Last-Modified value from the last response and send it back on the next poll. A 304 response means nothing changed. If the server ignores these headers, do not treat that as an error; log that the feed does not support conditional requests and fall back to a normal fetch with a sensible interval.
Google News
Google News sources in this kind of pipeline are usually query-based or topic-based RSS feeds, with region and language parameters that control which edition you receive. Items often link to a Google redirect rather than the publisher’s own URL. Resolve those redirects before deduplication, and store both the original link and the resolved link so you can trace an item back to what the feed delivered.
Licensing is the main open question. The secondary implementation example reports that Google offers this RSS for personal use and warns against commercial use. That report could not be confirmed against Google’s official terms for this guide, so check the current terms yourself before using Google News output in any product or client deliverable, and record the review date in the registry.
Reddit is the source where you most need to verify the access route before writing code. A secondary implementation example recommends the official OAuth API for dependable production use. Third-party reports conflict on whether unauthenticated JSON or RSS access behaves consistently, so do not build a production adapter on that assumption.
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YouTube Data API
YouTube is the only source here with official quota figures in the material reviewed for this guide. The search.list reference states a daily quota of 100 calls, and Google’s revision history records a transition to granular quota buckets beginning in June 2026. That means 100 is a method-specific figure at the time of checking, not a permanent account-wide allowance. Open the quota page for your project in the Google Cloud console and confirm what applies before you size anything.
Because search calls are the scarce resource, use search.list for discovery and poll known channels or playlists for ongoing coverage. Store the ETag from each response and send it back with the next request. If the cached version is still current, the API returns 304 Not Modified, which avoids transferring an unchanged resource again. Confirm the quota cost of each method in its own reference page rather than assuming it matches search.list.
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A rough budget illustrates the trade-off. If your project still has a 100-call daily allowance for search.list and you run 10 queries six times a day, that uses 60 calls, leaving 40 for retries and manual checks. If the quota model has changed for your project, redo the arithmetic with the live figure.
Step 3: Normalize every item into one record
Convert every adapter’s output into a shared record before any other processing. Downstream stages should never need to know which platform an item came from. Store at least these fields:
- source, source_item_id and the source registry ID that produced the item
- canonical_url and original_url (the link as delivered, before redirects or cleanup)
- title, author or channel name, and excerpt or platform metadata
- published_at, as reported by the platform, which may be missing
- observed_at, the time your system first saw the item, which is always present
- provenance: adapter version, request time, HTTP status and the query or feed that returned it
Store timestamps in UTC and keep published_at and observed_at separate. Freshness checks depend on the first; coverage checks depend on the second.
Step 4: Deduplicate on IDs first, URLs second
Use a platform’s stable identifier as the primary deduplication key where one exists. Where it does not, or where the same story arrives through several sources, fall back to the canonical URL. Canonicalizing means resolving redirects, removing tracking parameters, normalizing the host and path, and preferring the page’s declared canonical link when it differs from the address you fetched.
Keep a memory of seen items, for example a hash of the canonical URL per item, so repeated polls are cheap to check. The example implementations use this URL-hash approach; it is a reasonable starting point, not a standard.
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Edits matter. When an item changes after first publication, record the new version as an update to the existing record instead of discarding it or creating a duplicate. Keep the update history where a change could alter a finding, such as a corrected figure or a revised date.
When one story appears through several sources, keep one canonical item with multiple provenance entries. That lets the brief say “reported by three sources” without counting the same article three times in your daily total.
Step 5: Filter before any expensive work
Run inexpensive checks before you fetch pages or call a language model. Apply them in this order, because each one costs less than the next:
- Date window and source enabled flag.
- Language and region rules from the registry.
- Keyword and topic rules, including exclusion terms that remove known noise.
- A cheap relevance score based on title and excerpt.
Keep rejected items with a reason code instead of deleting them. Review a random sample of rejects regularly; if relevant items are landing there, the keyword rules are too narrow. Filtering before expensive steps is an implementation recommendation from the examples reviewed. It has not been measured as a cost saving for any specific workload.
Step 6: Fetch full pages selectively
Fetch complete pages only for items that passed the filter. Before you fetch, check the publisher’s access rules and any robots directives that apply to your use. Throttle requests per domain so that one busy publisher does not absorb your whole fetch budget.
Record a status for each fetch attempt: fetched, failed or blocked. When full text is unavailable, you may still use the feed summary or platform metadata, but mark the item as metadata-only in the brief so that readers and reviewers know the claims rest on less text.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Step 7: Extract structured claims, not free-form summaries
Ask the model for structured output: a list of claims, each with a verbatim evidence quote and the source details needed to check it. Then verify the mechanical part of that output in code. Confirm that each evidence quote appears in the fetched text, and reject or flag any claim whose quote cannot be found. A language model’s summary is not evidence on its own; every sentence in a brief should trace back to a claim record.
Keep classification labels and relevance scoring rubrics fixed and versioned, and store the model name and prompt version with each output so that results can be reproduced or compared later.
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{
"claim": "The legacy endpoint is scheduled for removal on 1 March 2027",
"evidence_quote": "The legacy endpoint will be removed on 1 March 2027.",
"source_url": "https://example.com/posts/api-changes",
"publisher": "example.com",
"published_at": "2026-10-02T09:00:00Z",
"retrieved_at": "2026-10-02T11:14:00Z",
"quote_verified": true,
"status": "unverified",
"reviewer": null
}This example is illustrative and uses a placeholder domain. In a live system, the quote_verified field should be set by the string check described above, not by the model.
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Step 8: Route findings to human review
Every item in the system should carry one visible status: fetched, filtered, failed, unverified or verified. Only items marked verified should appear in a brief as established facts. Send the following to a reviewer:
- Claims where the evidence quote failed the string check or the source fetch failed.
- High-impact claims, such as pricing, legal, security or medical statements, and dates that drive decisions.
- Conflicting claims across sources, where two publishers report different numbers or outcomes.
- Potentially sensitive material, including named individuals and allegations.
Show the reviewer the evidence quote next to the claim, with the publisher and publication time. Log each decision, including rejections, so that you can later tune filters and prompts against real outcomes.
Step 9: Monitor coverage so silence is not mistaken for no news
An adapter that fails quietly looks exactly like a quiet news day unless you measure it. Track these values for each source and each query or channel:
- Last successful poll time.
- New items found since the last run.
- Duplicate rate.
- Quota usage, where the platform defines a quota.
- Retries and failures, with error codes.
- Age of the latest processed item.
Set alert rules that match each source’s normal cadence. Two rules are a sensible start, to be tuned against your own logs: alert when a source has no successful poll for twice its scheduled interval, and alert when a normally active source produces no new items over a window you have measured as typical. Put a coverage line at the top of every brief that names the sources that succeeded in the window and any that did not.
Where coverage is incomplete
Do not treat search results or feed contents as a complete archive. A feed shows what the publisher currently lists, a search returns what the platform ranks for your query, and neither guarantees that every relevant item appears. Your system will miss some items, and the brief should say so. Name the sources and query windows behind each brief so that readers can judge what the output covers.
Check the terms before deployment
Before you run the pipeline on live sources, check each platform’s current official documentation and terms, and record the date in the registry. Pay particular attention to commercial use, redistribution of excerpts and any requirement to attribute content. Do not rely on the examples in this guide, which were not checked against each platform’s live policy pages, as the basis for a deployment decision. Re-check the YouTube quota and the Google News terms whenever you change how you use either source.
Start with the source you can verify most easily, usually a set of RSS feeds you have permission to read, and add the other adapters one at a time. Each new adapter should pass the coverage checks in Step 9 for at least one full polling cycle before it feeds briefs.
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