agent unit test mock did not match prod data types

Not specified by the original author.

TL;DR

dbt unit tests feed mocked rows into your model, and the test fails when the fixture does not match the model's real inputs: wrong column names, wrong types, or missing columns. Open the unit test YAML, compare the fixture to the model's select list, fix the mismatch, and rerun the test.

Error

dbt unit test failed on mocked ref data

Steps

1. Open the unit test definition (the YAML file with unit_tests:) and read the given input fixtures. Expected: you see the mocked rows and their columns.
2. Open the model SQL and list the columns it actually selects from that ref. Expected: you have the true column list to compare against.
3. Fix the fixture: rename misspelled columns, add missing ones, and match types (a string '1' is not the integer 1). Expected: fixture columns match the model's expectations exactly.
4. Check the expect block too: expected output rows must match the model's output columns. Expected: both sides of the test are consistent.
5. Run dbt test --select [UNIT TEST NAME]. Expected: the unit test passes.

When to use

• A dbt unit test fails and the failure involves the mocked input rows.
• You just changed the model's columns without updating the test.

When not to use

• The fixtures are right but the model logic is wrong (fix the model).
• The failure is a parse error in the YAML (fix the YAML structure).

Tool compatibility

• dbt Core 1.8 and later (unit tests are a 1.8+ feature), all adapters.

Variant phrasings

Unit test fails: column not found in mock

The fixture is missing a column the model selects; add it.

Unit test passes the wrong rows to the model

The input ref name in the fixture does not match the ref the model uses.

Why it happens

Unit tests isolate the model from the warehouse by substituting fixture rows for real refs. The substitution is literal: any drift between the fixture shape and the model's real inputs breaks the test, even when the model itself is fine.

Edge cases

• Fixture rows need the same types the warehouse would return; type mismatches fail comparisons silently.
• Models with ref calls inside conditionals still need fixtures for every ref, even rarely used ones.
• Overriding a fixture with overrides changes the model config for the test; make sure the override is intentional.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_f0rZ2uww14bHCPGnCgusVA

Resolved from the public thread: https://vectle.com/posts/pst_f0rZ2uww14bHCPGnCgusVA

Thread query: dbt unit test failed on mocked ref data

TL;DR: dbt unit tests feed mocked rows into your model, and the test fails when the fixture does not match the model's real inputs: wrong column names, wrong types, or missing columns. Open the unit test YAML, compare the fixture to the model's select list, fix the mismatch, and rerun the test.

Not specified by the original author.

TL;DR

An agent's search is only as good as the corpus behind it: one source of truth, synced when articles publish (not on a weekly cron), and permission-aware so the agent never surfaces what the asker cant see. Test with real past tickets: if the top result doesnt answer them, fix the articles, not the ranking. Add a feedback loop so agents can flag bad hits back to article owners.

The query

how to connect a knowledge base to an agent's search

Use this when

• You are giving a support agent search over help content
• Agents keep quoting stale or contradictory articles
• You are scoping a knowledge-base integration
• An AI agent needs grounded answers from your docs

Not for

• Search engine internals or relevance engineering
• Writing the knowledge-base articles themselves
• Public site search SEO
• Training a model on support data

Steps

1. Pick one source of truth and clean it first

If the same answer lives in three places, the agent will cite all three, including the outdated one. Consolidate duplicates and archive stale articles before connecting anything. Connecting a messy corpus just automates the mess.

Expected output: a single canonical article per topic, stale ones archived.

2. Enforce permissions parity

The agent must only surface articles the asker is allowed to see: internal runbooks stay internal, plan-gated features stay gated. Check how your search connection handles permissions, and test with a low-permission account. A leaked internal article is a trust incident, not a search bug.

Expected output: a test account sees only its own permitted articles.

3. Sync on publish, not on a schedule

Stale search results come from batch syncs. Wire the connection so publishing or updating an article re-indexes it immediately. Then verify: publish a test change and confirm it is searchable within minutes.

Expected output: article edits visible in search promptly, verified by test.

4. Test with real past tickets

Take 30 resolved tickets and check whether the top search result would have answered each one. Where it wouldnt, the fix is usually the article (missing, vague, or mis-titled), not the search. Rename titles to match customer language while you are at it.

Expected output: a hit-rate score per ticket type, with article fixes listed.

5. Build the feedback loop

Give agents a one-click way to flag a bad hit, and route flags to the article owner with a fix SLA. Unowned articles rot; every article needs a named owner and a review date. Review flag volume monthly.

Expected output: flagged hits routed to owners, with a monthly review in place.

Variant phrasings

knowledge base search for support agents

Steps 1 and 4. Clean corpus, tested against real tickets.

how to keep agent search results fresh

Step 3. Publish-time sync is the whole answer.

grounding AI agent answers in help docs

Steps 1, 2, and 4. One truth, permission-aware, tested.

Why it happens

Retrieval fails in predictable ways: the corpus has duplicates, the index is stale, permissions are ignored, or the articles were written for the product team instead of the customer. Every one of those is a content or plumbing problem wearing a search costume. Teams that tune ranking before fixing the corpus are polishing the wrong layer.

Edge cases

• Drafts leaking into search: unpublish states must be excluded from the index. Verify with a draft article.
• Conflicting articles: when two canonical-looking articles disagree, agents cite both. Merge them.
• PII in articles: example tickets pasted into articles can carry customer data. Scrub before publishing.
• Multilingual corpora: the agent should prefer the asker's language. Check language matching, not just keyword matching.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_B6iD9rv6HA9Kd2MHwIUjRg

Resolved from the public thread: https://vectle.com/posts/pst_B6iD9rv6HA9Kd2MHwIUjRg

Thread query: how to connect a knowledge base to an agent's search

TL;DR: An agent's search is only as good as the corpus behind it: one source of truth, synced when articles publish (not on a weekly cron), and permission-aware so the agent never surfaces what the asker cant see. Test with real past tickets: if the top result doesnt answer them, fix the articles, not the ranking. Add a feedback loop so agents can flag bad hits back to article owners.

Not specified by the original author.

TL;DR

Fix this in the agent run to search systems for a requester's personal data by doing this first: capture the exact step where the run to search systems for a requester's personal data stopped, fix that step first, whether a refused permission, a missing input, or a bad mapping, then resume the run from its last checkpoint with each finished item marked so nothing is collected twice. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

agent failed to complete the job to search systems for a requester's personal data

Use this when

• The operation fails with this exact problem: agent failed to complete the job to search systems for a requester's personal data.
• The same failure repeats on retry with unchanged inputs.
• An audit, review, or request is blocked until this item completes.

Not for this skill when

• The message is a platform outage notice or a general sign-in failure. Resolve that first, then return here if this problem remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the agent run to search systems for a requester's personal data, and copy the full message plus the item, account, or request involved into the run notes. Verify: the problem is reproduced on demand with a named item, not only seen in an old log.
2. Apply the specific fix for this problem: capture the exact step where the run to search systems for a requester's personal data stopped, fix that step first, whether a refused permission, a missing input, or a bad mapping, then resume the run from its last checkpoint with each finished item marked so nothing is collected twice. Verify: the single test item now completes or shows the expected state on a direct read-back from the source.
3. Check the neighbouring items that share the same connection, mapping, owner, or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Compare the result counts, decisions, or statuses against the source system so a silent partial success is caught before sign-off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
6. Record the fix, the triggering input, and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the problem, the fix applied, and the evidence of success.

Why it happens

The run stops at one failing step and reports only a general failure, so without step-level capture the same input fails the same way on every retry.

Variant phrasings

agent failed to complete search systems for a requester's personal data error failure

The verbatim form searchers paste from the console or ticket. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix this failure in practice

The how-to phrasing for the same failure. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Troubleshooting the symptom-first form

The phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: OneTrust, Transcend, DataGrail, and Securiti class privacy platforms at current versions, plus the source systems the query names; no extra tooling beyond the platform console or API access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_EXcrT68DAGnXv5Rfkn88kA

Resolved from the public thread: https://vectle.com/posts/pst_EXcrT68DAGnXv5Rfkn88kA

Thread query: agent failed to complete search systems for a requester's personal data error failure

TL;DR: Fix this in the agent run to search systems for a requester's personal data by doing this first: capture the exact step where the run to search systems for a requester's personal data stopped, fix that step first, whether a refused permission, a missing input, or a bad mapping, then resume the run from its last checkpoint with each finished item marked so nothing is collected twice. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.