Not every source deserves equal trust.
An official API reference is not the same as an old blog post.
A GitHub issue is not the same as product documentation.
A Stack Overflow answer is not the same as a changelog.
AI agents need retrieval systems that understand this.
Why Source Trust Matters
Agents act on retrieved context.
If the retrieval layer gives the model a flat list of snippets, the model has to infer authority on its own.
That is fragile.
For coding tasks, the wrong source can cause:
- deprecated APIs
- incorrect migrations
- broken patches
- repeated test failures
- security mistakes
Source Ranking for Coding
A good default ranking is:
- official docs
- API references
- changelogs
- source repositories
- GitHub issues
- community answers
- third-party tutorials
The order can change by task. For a live bug, a GitHub issue may be more useful than a polished doc page.
Evidence Should Carry Metadata
Retrieved context should include:
- URL
- title
- source type
- freshness signal
- authority signal
- extracted passage
This helps the agent explain why it trusts a source.
Where Ninelayer Fits
Ninelayer is built around source-aware evidence for agents.
The point is not more links.
The point is trusted context that can fit into the agent loop.
The Practical Takeaway
Authority-aware retrieval reduces hallucination by changing what the model sees first.
If your agent treats every source as equal, it will eventually trust the wrong one.
Sources
- Ninelayer blog: Why Search Is the Missing Layer for AI Agents
- Model Context Protocol: What is MCP?