How an AI agent can convert ingestion context into a health brief with failures, schema risks, diagnostics, and validation steps
This workflow gives an AI agent a defined job, a bounded set of records, and a result a person can review. The agent reads the relevant Turbopuffer context, applies the rules in the prompt, and keeps the source behind every recommendation. It returns a proposed handoff rather than taking consequential actions on its own.
Can an AI agent convert ingestion context into a health brief with failures, schema risks, diagnostics, and validation steps?
Yes. Start with the scope, date range, decision rules, and fields that identify the right records. The agent can collect the evidence, compare states or sources, mark conflicts and missing data, and organize the result around the outcome above. A reviewer then checks the matches and judgment calls before approving messages, record updates, bookings, purchases, publishing, or other write actions. The guide below shows the records, boundaries, prompt, and handoff needed for this specific workflow.
What this agent helps you do
A Turbopuffer ingestion health agent reviews whether documents and vectors are entering the search system as expected. It focuses on ingestion failures, schema mismatches, missing metadata, and validation examples.
When to use this workflow
Use it after pipeline changes, when retrieval quality drops, before launching a new search feature, or when documents are missing from results.
How Turbopuffer gives the agent context
Connect Turbopuffer and scope the task to the namespace, schema, document batch, ingestion window, or symptom. Ask the agent to begin with diagnostics and keep schema or write changes approval-ready.
Example starter prompt
Review Turbopuffer ingestion health for this namespace. Identify missing or malformed records, schema risks, failed batches, diagnostic checks, and validation steps before we change production data.Suggested workflow steps
The agent gathers examples, checks schema assumptions, compares expected and actual records, and prepares diagnostics ranked by risk.
Record the Turbopuffer namespace, document identifier, batch or ingestion window, schema fields, vector presence and dimensions where relevant, metadata filters, and expected source count. Sample successful as well as missing records.
Questions this workflow answers
Are documents missing from search because they failed ingestion, were malformed, or landed in the wrong namespace?
The agent begins with expected source counts and a defined batch or time window. It compares source identifiers with records present in the target namespace and samples successful, missing, and recently changed documents. For each record it checks identifier, schema fields, vector presence and dimensions where relevant, metadata values, and filterable fields.
Missing search results are separated from ingestion failures. A document may exist but be filtered out by type, tenant, language, or status metadata. Another may have an empty vector, wrong dimension, stale version, duplicate ID, or partial write. The agent records the first divergence and avoids declaring the whole pipeline broken from one bad example.
Diagnostics are ordered from read-only inventory to controlled test ingestion. Any sample write uses isolated data and an approved namespace. Schema, delete, bulk rewrite, and production reingestion actions remain proposals with impact and rollback notes.
The handoff includes control totals, missing and malformed examples, schema differences, failed batches, hypotheses, and validation queries. Pipeline owners confirm source completeness and approve repairs. After a fix, the same source set is checked for count, field quality, retrievability, and duplicates before the incident is closed.
Expected handoff
The handoff should include ingestion findings, example records, diagnostic steps, recommended fixes, and validation checks. Pair with GitHub for pipeline code context.
The agent should trace a small set of expected documents from source record to extraction, chunking, embedding, namespace, and indexed object. At each stage it records stable ID, timestamp, content or chunk count, schema version, error, and retry state. A source count that exceeds the index count may reflect failed jobs, deliberate filters, deletes, or lag, so totals alone cannot name the cause. Sampling one missing record from each failure pattern reveals whether malformed input, an empty body, size limit, namespace typo, or stale retry queue is responsible. The validation plan then rechecks those same IDs and reconciles control totals without reingesting the entire corpus blindly.