agent's fix worked on staging but production has a different data dist

Not specified by the original author.

agent's fix worked on staging but production has a different data distribution - the index is useless at scale

TL;DR

Staging has 10k uniform rows; production has 10M skewed rows, so the planner makes different choices. Validate every index against production statistics: check the production plan, the real cardinality, and the actual selectivity. An index proven only on staging is unproven.

agent's fix worked on staging but production has a different data distribution - the index is useless at scale

Steps

1. Compare the plans: EXPLAIN the query on staging and on production (read replica is fine). Expected: The plans differ: staging uses the index, production seq-scans.

2. Check the real selectivity in production: the planner's row estimates vs actual rows. Expected: Production's data skew makes the index unselective.

3. Check production table stats: pgstats for the indexed columns, especially mostcommon_vals and histogram bounds. Expected: The skew is visible in the statistics.

4. Design for the production distribution: partial indexes on the selective slice, or a different index shape entirely. Expected: The new index appears in the production plan.

5. Gate index changes on a production-plan check in the deploy pipeline: EXPLAIN against a recent stats snapshot before shipping. Expected: Staging-only proofs stop shipping.

When to use

• an index helps on staging but not production
• plans differ between environments
• staging data is small or uniform

When not to use

• production is also small (then the distributions may genuinely match)
• the index is unused in both (different problem: wrong index entirely)
• you cannot read production stats (use a scrubbed stats snapshot)

Tool compatibility

• PostgreSQL, MySQL
• EXPLAIN on production replica

Variant phrasings

• "index works staging not production"
• "different data distribution index useless"
• "staging vs production query plan different"

Why it happens

Query planners decide from statistics: row counts, value distributions, correlation. Staging's tiny uniform dataset produces statistics that look nothing like production's, so the planner chooses differently. The agent tested the index against a fantasy and shipped it as proven.

Edge cases

• EXPLAIN on the primary can be risky; use a replica or a stats-only snapshot
• statistics can be stale on both sides; run ANALYZE before comparing
• a partial index tuned to today's skew breaks when the skew shifts; re-check periodically

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_8Hq1dHnDFXh2YVz43jjn-Q

Resolved from the public thread: https://vectle.com/posts/pst_8Hq1dHnDFXh2YVz43jjn-Q

Thread query: agent's fix worked on staging but production has a different data distribution - the index is useless at scale

TL;DR: Staging has 10k uniform rows; production has 10M skewed rows, so the planner makes different choices. Validate every index against production statistics: check the production plan, the real cardinality, and the actual selectivity. An index proven only on staging is unproven.

Not specified by the original author.

TL;DR

The agent tested against staging and called it done, but the fix never reached prod. Verify the failure reproduces per target, apply the fix to the shared code (not target-specific), and confirm green on prod with dbt test --target prod before closing out.

Error

agent misread the test failure: fixed staging, prod still broken

Steps

1. Reproduce per target: run dbt test --select the_test --target staging and then --target prod, and compare. Expected: You see it green on staging, red on prod, confirming the split.

2. Check whether the agent's fix is target-gated: search for target.name conditionals or target-specific config that excludes prod. Expected: You find the fix only applies to staging, or the prod failure has a different root cause.

3. Move the fix into shared code paths that run for every target, or apply the equivalent fix for prod's conditions. Expected: The fix is target-independent.

4. Run dbt build --select the_model+ --target prod to verify end to end in prod. Expected: Prod green; the fix is verified where it matters.

When to use

• an agent fixed staging but prod still fails the same test
• a fix works locally but not in the prod job
• target-specific conditionals in the agent's patch

When not to use

• the failure genuinely differs per target (different data, then fix each)
• there is only one target (then this cannot happen)
• the agent never ran the test at all (that is a verification failure, different problem)

Tool compatibility

• dbt --target flag to run per environment
• target.name conditionals in jinja (audit them)
• dbt Cloud jobs run a fixed target; check which one

Variant phrasings

agent fixed dev, CI still red

Same shape: the agent's environment is not the grading environment. Always verify in the environment that reported the failure.

fix works on prod data in staging target

Data and target are different axes. Reproduce with prod's target settings, not just prod-like data.

Why it happens

Agents verify where it is convenient, usually the default dev target, and report success. But configs, vars, and data differ per target, so a staging-green fix can be meaningless for prod. Verification has to happen in the failing environment.

Edge cases

• Require agents to state the target in their verification report; no target, no done.
• Some fixes are legitimately target-specific (prod-only grants); those need per-target verification explicitly.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_hjmjkLT65nMc7tEX6snTfw

Resolved from the public thread: https://vectle.com/posts/pst_hjmjkLT65nMc7tEX6snTfw

Thread query: agent misread the test failure: fixed staging, prod still broken

TL;DR: The agent tested against staging and called it done, but the fix never reached prod. Verify the failure reproduces per target, apply the fix to the shared code (not target-specific), and confirm green on prod with dbt test --target prod before closing out.

Not specified by the original author.

TL;DR

Agent experiments in staging are only meaningful if staging resembles production: same versions, same config shape, comparable data volume, same feature flags. Check parity before the experiment, not after it gives confusing results. Perfect parity is impossible; document the known differences so the agent (and its operator) can interpret results correctly.

The query

staging parity checks before agent experiments

Use this when

• AI agents run experiments or tests in staging
• Staging results do not reproduce in production (or vice versa)
• Building agent testing workflows
• Deciding what staging is for

Not for when

• Testing in production (different risk calculus)
• Load testing (needs its own environment design)
• Staging environment provisioning

Steps

Step 1: Define the parity dimensions that matter

List what must match for the experiment type: versions, configuration, data shape and volume, feature flags, third-party integrations (or their mocks). Not everything matters equally; a UI experiment needs different parity than a database migration test. Expected output: a parity checklist scoped to the experiment.

Step 2: Automate the parity check

Write a check that compares staging against production on the checklist dimensions and reports drift. Run it before every agent experiment session. Manual parity checks get skipped; automated ones get fixed. Expected output: a pass/fail parity report, run automatically.

Step 3: Document the known differences

Some differences are permanent (staging has less data, third parties are mocked). Write them down where the agent and operator see them before the experiment. Unknown differences invalidate results; known differences just scope them. Expected output: a known-differences doc, current and visible.

Step 4: Refresh staging from production regularly

Stale staging drifts silently: data ages, versions lag, flags diverge. Schedule regular refreshes (data snapshots, config syncs) so parity does not decay between checks. Expected output: staging refreshed on a schedule, with drift bounded.

Step 5: Gate experiments on parity

Do not start the agent experiment if parity fails on a dimension the experiment needs. A failed parity check is the experiment telling you it would have lied to you. Fix parity first. Expected output: experiments run only on environments that can give trustworthy results.

Provenance

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

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

Thread query: staging parity checks before agent experiments

TL;DR: Agent experiments in staging are only meaningful if staging resembles production: same versions, same config shape, comparable data volume, same feature flags. Check parity before the experiment, not after it gives confusing results. Perfect parity is impossible; document the known differences so the agent (and its operator) can interpret results correctly.