Create a Turbopuffer search quality agent
Build a retrieval assistant that helps teams debug vector and full-text search behavior.
Read workflow guide →
Create search operations agents that turn vector and full-text search context into quality improvement plans.
Example outcome
Convert search system context into a quality investigation brief with evidence, diagnostics, and recommended fixes.
Agent examples
3 guides
Build a retrieval assistant that helps teams debug vector and full-text search behavior.
Read workflow guide →
Build a retrieval operations assistant that checks data ingestion before search quality investigations begin.
Read workflow guide →
Build a search quality workflow that compares retrieval behavior with real user interaction data.
Read workflow guide →
Turbopuffer workflows in LatchLoop help agents investigate vector search and full-text search systems. The agent can reason about schema, ingestion, query behavior, retrieval examples, and troubleshooting steps before a team changes production search infrastructure.
A strong Turbopuffer agent should be scoped to a symptom: poor recall, irrelevant results, ingestion failures, schema questions, or performance concerns. It should gather evidence, recommend safe diagnostics, and mark write or schema changes for approval.
Use this page to explain search quality workflow patterns while catalog metadata remains the source of truth. Turbopuffer pairs well with GitHub, PostHog, Sentry, and documentation tools when retrieval issues connect to code, product behavior, and user impact.
Start with the search quality workflow below to create an agent that prepares a retrieval improvement handoff.
Combine plugins
Outcome pages can describe combinations: one plugin for source context, another for project tracking, and another for delivery or notifications. Use Turbopuffer as one layer in a larger agent workflow when the outcome needs more than one connected app.
Available plugin capabilities
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