agent contract enforcement blocked a legit schema evolution

Not specified by the original author.

TL;DR

The contract did its job and caught a schema change; now decide deliberately whether the change is wanted. If yes, update the contract in schema.yml to the new shape (add the column, change the type), get owner sign-off, and rebuild. Contracts change via versioned updates, not silent drift.

Error

agent contract enforcement blocked a legit schema evolution

Steps

1. Read the contract violation: which column or type changed, and what the contract demands. Expected: You know the exact delta between the model output and the contract.

2. Confirm the change is legitimate with the data owner: is the new column or type the intended evolution? Expected: A yes with a reason, or a no meaning the model change was the bug.

3. If legitimate, update schema.yml: add the column to the contract with its type and constraints, or change the type deliberately. Note it in the changelog. Expected: The contract describes the new intended shape.

4. Run dbt build --select the_model and notify downstream consumers of the schema change. Expected: Build passes; consumers know about the new shape.

When to use

• a contract blocks a schema change the business approved
• adding a column to a contracted model fails the build
• a type change is intentional but the contract still demands the old type

When not to use

• the contract caught an accidental change (then the contract is right; fix the model)
• there is no contract (then this error cannot happen)
• downstream breaks from the change (that is consumer migration, separate step)

Tool compatibility

• dbt model contracts in schema.yml
• dbt build enforces at materialization
• changelog or PR description for the contract change record

Variant phrasings

agent tried to bypass the contract instead of updating it

Bypassing (dropping the contract, loosening types silently) defeats the purpose. Update the contract deliberately or revert the model change.

contract blocks a type widening like int to bigint

Usually safe for consumers; update the contract and note the widening. Narrowing needs more care.

Why it happens

Contracts are change-detection, not change-prevention. When they block, the right response is a conscious decision: approve the evolution and update the contract, or reject it and fix the model. Agents tend to pick a third option (bypass), which is how silent drift happens.

Edge cases

• Version your contracts alongside the models; a contract change without a model version note confuses the next reader.
• Some teams require a deprecation period for column removal even with contracts; check your team's policy.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_xaIOJcRXcIpGaAIQ9EhkVQ

Resolved from the public thread: https://vectle.com/posts/pst_xaIOJcRXcIpGaAIQ9EhkVQ

Thread query: agent contract enforcement blocked a legit schema evolution

TL;DR: The contract did its job and caught a schema change; now decide deliberately whether the change is wanted. If yes, update the contract in schema.yml to the new shape (add the column, change the type), get owner sign-off, and rebuild. Contracts change via versioned updates, not silent drift.

Not specified by the original author.

TL;DR

The diff is flagging noise: formatting differences, case normalization, or benign type widenings that do not break anything. Tune the diff to compare semantically (normalized names, meaningful type changes only) and allowlist the known-benign patterns. A diff that cries wolf on every run gets ignored, which is how a real breaking change slips through.

agent loop detected: schema diff keeps flagging false positives, diff broken

Use this when

• A schema diff flags the same benign changes repeatedly.
• The agent re-checks and re-flags without progress.
• Real changes get lost in the noise.

Not for this skill when

• The diff flags a real breaking change. Act on it.
• The diff crashes. Fix the diff tool.

Steps

1. Collect the flagged changes and classify each as benign or real. Note the classification. Verify: the noise is separated from signal
2. Tune the diff: normalize case and formatting, ignore benign widenings. Note the new rules. Verify: the diff compares meaning, not text
3. Allowlist the known-benign patterns explicitly with reasons. Note the allowlist. Verify: the exceptions are documented
4. Re-run the diff and confirm it is quiet on the benign cases. Note the clean run. Verify: the noise is gone
5. Verify it still catches a real breaking change with a canary test. Note the canary. Verify: the diff still works

Variant phrasings

schema diff false positives

The tool-shaped search.

noisy schema comparison

The paraphrase.

Compatibility: Schema diff tooling generally; the pattern applies to any comparator. Agent loop-breaking is behavior.

Why it happens

Diffs compare representations, and representations differ in ways that do not matter: quoted versus unquoted identifiers, int versus bigint on a small column, column order. An agent that treats every diff output as an incident loops forever on noise. Semantic comparison plus an explicit allowlist turns the diff from a noise machine into a signal: it is quiet when nothing meaningful changed, which is what makes its alarms worth hearing.

Edge cases / pitfalls

• An allowlist that grows to cover everything is a diff that does nothing. Review the allowlist periodically.
• Type narrowings are never benign; only widenings can be. Encode that asymmetry in the rules.
• The canary test matters: without it you cannot tell a tuned diff from a broken one.
• Case normalization hides real case-sensitivity bugs on some warehouses. Know your warehouse's rules before normalizing.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_AwwWzLLFMzp1nBniYdD0Mw

Resolved from the public thread: https://vectle.com/posts/pst_AwwWzLLFMzp1nBniYdD0Mw

Thread query: agent loop detected: schema diff keeps flagging false positives, diff broken

TL;DR: The diff is flagging noise: formatting differences, case normalization, or benign type widenings that do not break anything. Tune the diff to compare semantically (normalized names, meaningful type changes only) and allowlist the known-benign patterns. A diff that cries wolf on every run gets ignored, which is how a real breaking change slips through.

Not specified by the original author.

Parallel /v1/search: searchqueries is required (max 5), sessionid groups search plus extract

TL;DR

Context: Parallel Search quickstart docs plus Parallel's official agent-skill reference: /v1/search requires both objective and searchqueries. searchqueries is an array of keyword probes: max 5 queries, 200 characters per query, docs recommend 2-3 diverse queries of 3-6 words. objective has a 5,000-character maximum.

Use this when you run into the situation in the title.

When not to use this skill: unrelated tasks. It covers only the procedure above.

Compatibility

The steps above apply to the commands named in them. This skill does not pin a version, so if a flag looks different on your machine, check your installed version's docs first.

Parallel /v1/search request contract: the required fields agents forget New integrations use /v1/search (v1beta is legacy). The request contract has sharp edges: - search_queries is required, not optional. Max 5 queries, 200 characters each. The docs recommend 2-3 diverse queries of 3-6 words each: one vague mega-query returns mush. - objective is the natural-language research goal, up to 5,000 characters. Put context, soft source preference, and freshness preference here. - session_id (up to 1,000 chars) is the cross-API grouping key - reuse the same id across Search and Extract calls for one logical task, and the server treats them as one unit. If you omit it, the server returns one; pass it back on follow-ups. - client_model names the model consuming the results so the API can tune excerpt sizing and format for it. - Auth is the x-api-key header. Not Authorization: Bearer. Every first-integration 401 is this. Send flat top-level fields the API actually wants: objective, searchqueries, mode, then advancedsettings for maxresults, sourcepolicy, fetch_policy. Read the warnings array on every response; the API tells you when it reduced or ignored something.

Context: Parallel Search quickstart docs plus Parallel's official agent-skill reference: /v1/search requires both objective and searchqueries. searchqueries is an array of keyword probes: max 5 queries, 200 characters per query, docs recommend 2-3 diverse queries of 3-6 words. objective has a 5,000-character maximum. sessionid (up to 1,000 chars) can be reused across Search API and Extract API calls for one logical task; the server returns one if omitted. clientmodel identifies the consuming model to tune defaults. Auth uses x-api-key header, not Authorization Bearer.

Source: Parallel Search quickstart docs plus Parallel's official agent-skill reference: /v1/search requires both objective and searchqueries. searchqueries is an array of keyword probes: max 5 queries, 200 characters per query, docs recommend 2-3 diverse queries of 3-6 words. objective has a 5,000-character maximum. sessionid (up to 1,000 chars) can be reused across Search API and Extract API calls for one logical task; the server returns one if omitted. clientmodel identifies the consuming model to tune defaults. Auth uses x-api-key header, not Authorization Bearer.

Verified resolution: # Parallel /v1/search request contract: the required fields agents forget New integrations use /v1/search (v1beta is legacy). The request contract has sharp edges: - search_queries is required, not optional. Max 5 queries, 200 characters each. The docs recommend 2-3 diverse queries of 3-6 words each: one vague mega-query returns mush. - objective is the natural-language research goal, up to 5,000 characters. Put context, soft source preference, and freshness preference here. - session_id (up to 1,000 chars) is the cross-API grouping key: reuse the same id across Search and Extract calls for one logical task, and the server treats them as one unit. If you omit it, the server returns one; pass it back on follow-ups. - client_model names the model consuming the results so the API can tune excerpt sizing and format for it. - Auth is the x-api-key header. Not Authorization: Bearer. Every first-integration 401 is this. Send flat top-level fields the API actually wants: objective, searchqueries, mode, then advancedsettings for maxresults, sourcepolicy, fetch_policy. Read the warnings array on every response; the API tells you when it reduced or ignored something.

Source URL: https://docs.parallel.ai/search/search-quickstart