When AI agents should answer — and when they should not
AI agents often become confidently wrong when a user describes something they know exists but can't name — a half-remembered book, film, song, or event. The fragment may carry enough signal to locate the answer, but not enough for a model to answer safely from memory.
This is the class of query Search Fragments is built for — there is a live demo on the homepage.
What a fragmented query looks like
Here is a real example from our test set:
“There’s a piece of music in a David Lynch scene where almost nothing happens. A woman sits at a table. The music is almost unbearable. I can’t remember the film.”
A keyword filter finds nothing useful — there is no clear title, composer, or scene description to match against. A plain LLM recall is worse: the model has seen enough Lynch to generate a plausible-sounding answer, but not enough to know which film and scene this actually is. The result is fluent, specific, and wrong. The fragment has real signal — it just needs evidence-grounded resolution, not a confident guess.
Why agents bluff
The core problem is that generation optimises for the plausible next token, not for whether evidence supports the specific claim being made. Confidence tracks how well an answer fits the language — how fluently it continues the query, how naturally it resolves the pattern — not how well the underlying facts support it. Nothing in the generation step checks that support: a fluent, specific, wrong answer is produced by exactly the same machinery as a fluent, specific, right one. This is how hallucination happens silently: the agent produces something that passes surface inspection and fails only when someone checks the underlying facts.
Fragmented queries make this worse. The query contains real signal — a director, a mood, a setting — which activates plausible-but-unverified associations. The model recognises the pattern without being able to verify the answer, and still commits to a response. The more recognisable the fragment, the more confidently wrong the hallucination tends to be.
The well-balanced answer
The fix is confidence calibrated to evidence. Rather than forcing a single answer at every confidence level, a resolution should produce one of three honest shapes:
- Resolved A named answer, with confidence justified by the evidence found.
- Shortlist Ranked web sources to read and confirm by eye — results returned, but no confident semantic identification made.
- No resolution An explicit no-resolution — not resolvable from the clues given.
Built to decline rather than guess.
This matters because a confident-wrong answer is the worst kind of failure for an agent: it looks correct, passes downstream checks, and propagates into whatever the agent produces. An honest decline is far less harmful — it can be routed to a human, retried with a different tool, or surfaced to the user as a gap. Silence about uncertainty is what causes real damage.
What a baseline agent invented
On 50 hard, under-documented fragments, a baseline agent produced three specific fabrications — a real film falsely attributed to a real director who had no connection to it, a specific named sketch that was never performed, and a specific researcher and location for a study that has no single canonical paper. Search Fragments did not assert an answer to any of them.
Try it
Try a fragmented query in Search Fragments — the demo runs a real resolution on whatever you put in, right now.
Connect
Search Fragments is a remote MCP server. Two tools: resolve_fragment and verify_claim. Free, no signup.
{
"mcpServers": {
"search-fragments": {
"type": "remote",
"transport": "streamable-http",
"url": "https://searchfragments.com/api/mcp"
}
}
}