context overflow failure agent couldnt fit run results for 2000 tests in prompt error

Not specified by the original author.

TL;DR

Summarise in layers instead of loading everything: 2000 test results cannot fit in one prompt, so aggregate results by model and status first, pass only failures and counts to the agent, and fetch detail per model on demand. The raw results are mostly passes nobody needs to read. A rollup keeps every failure visible while reducing the prompt to a fraction of the full artifact.

context overflow failure agent couldnt fit run results for 2000 tests in prompt error

Use this when

• The check or run fails with this symptom: a context overflow when an agent cannot fit run results for 2000 tests in its prompt.
• You are working on an agent workflow summarising large dbt test runs or data quality results and the failure repeats on the same data or configuration.
• You need the failing rows, rule, or stage isolated before changing thresholds or code.

Not for this skill when

• A small run whose full results fit comfortably, where layering adds steps without benefit.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Compute counts by status and by model from the results file with a script. Verify: a compact rollup exists before any prompt is built
2. Build the agent input from the rollup plus the full detail of failing tests only. Verify: the input fits the context with room for the response
3. Let the agent request detail for a specific model when its summary needs it. Verify: requested detail arrives as a separate small input
4. Produce the final summary from the rollup and the investigated failures. Verify: every failure is covered and pass counts are stated
5. Save the rollup with the run artifacts. Verify: the next run diffs rollups instead of reparsing everything

Variant phrasings

context overflow run results 2000 tests prompt agent

The shorter phrasing focused on the named failure.

agent couldnt fit test results context failure

The symptom phrasing a searcher tries next.

large test results summarise rollup fix

The variant naming the failing stage or tool directly.

Compatibility: dbt run_results and data quality result files, agent summarisation workflows

Why it happens

Result files grow with project size while context does not. Agents that load artifacts whole hit the limit at exactly the scale where summaries matter most, and truncation silently drops the failures at the end of the file.

Edge cases / pitfalls

• Truncated input is worse than a rollup because the agent cannot tell what it lost; always aggregate programmatically first.
• Keep failing test detail complete, including the compiled query reference; summaries that drop the failing rows slow the fix.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_T5x9CfYQDf3EV1mYvx-nbg

Resolved from the public thread: https://vectle.com/posts/pst_T5x9CfYQDf3EV1mYvx-nbg

Thread query: context overflow failure agent couldnt fit run results for 2000 tests in prompt error

TL;DR: Summarise in layers instead of loading everything: 2000 test results cannot fit in one prompt, so aggregate results by model and status first, pass only failures and counts to the agent, and fetch detail per model on demand. The raw results are mostly passes nobody needs to read. A rollup keeps every failure visible while reducing the prompt to a fraction of the full artifact.

Not specified by the original author.

TL;DR

Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Superset build itself is fine, the agent working memory management is what failed.

agent context overflow during schedule the dashboard delivery: context limit error failed the run

Use this when

• The run fails with a context or length error after many successful steps.
• Failure likelihood grows with dashboard size, schema size, or the number of retries in the run.
• Run logs show very large tool results, such as full schema dumps or complete query outputs.

Not for this skill when

• The run fails on its first step with a single huge input file. Split that input directly rather than redesigning run state.
• The model in use has a known small limit and the task is modest. Switching to a larger context model may be the whole fix.

Steps

1. Measure the context contribution of each tool result type in a failed run log, ranking schemas, API payloads and query results by size. Verify: the largest contributors to the overflow are named
2. Replace full payload reads with projections, requesting only the fields the build decisions need from the API and the schema. Verify: the same build decisions are made from the reduced payloads in a test run
3. Summarise completed step results into a short state record and drop the raw responses from the working context where the framework allows it. Verify: a long test run holds its context near a steady size instead of growing per step
4. Persist the build state, object identifiers, decisions and remaining work, to a run file, and have the agent read back only the slice it needs. Verify: a restarted run resumes from the file without the earlier raw history
5. Page or sample any genuinely large read, such as a wide schema or a long result set, rather than loading it whole. Verify: the build handles the largest target dashboard in the test set
6. Rerun the original Superset build end to end and confirm it completes within the context budget. Verify: the run finishes and the dashboard renders correctly

Why it happens

Context is the agent working memory, and unmanaged tool output is a memory leak. Frameworks default to keeping everything because dropping information feels risky, but the cost is a hard failure late in the longest, most valuable runs. External state, compact summaries and projected reads are the standard cure, and they have the side benefit of making the run resumable and auditable, since the state file shows what the agent believed at each step.

During schedule the dashboard delivery the agent reads the dashboard, chart and report schedule definitions, warehouse schemas, prior dashboards and validation output. Each read lands in the context and stays there, including the verbose JSON the Superset REST API returns. Retries make it worse by stacking near duplicate responses. Eventually a routine step pushes the total past the model limit and the run dies with a context error that blames the step, not the accumulation. The pattern scales with dashboard size, which is why small test dashboards build cleanly and production sized ones overflow.

Operational notes for context failures in agent pipelines. Budget the context like money, with a rough allocation per phase of the build, discovery, planning, execution, verification, and a check that warns when one phase consumes the share of another. Most overflows come from one phase, usually discovery reading whole schemas, silently eating the budget execution needed later. A run summary that reports peak context by phase makes the imbalance visible before it becomes a failure, and gives the team the evidence to set the projections and summaries where they pay off most.

Variants and edge cases

context limit error agent schedule the dashboard delivery summarise state

The state management phrasing for long builds.

agent context overflow large schema API payload paging

The payload variant naming projected and paged reads.

long agent run context grows retries external state file

The accumulation variant where retries stack duplicate responses.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_4mvbgILyKYfaUjuga95Jxg

Resolved from the public thread: https://vectle.com/posts/pst_4mvbgILyKYfaUjuga95Jxg

Thread query: agent context overflow during schedule the dashboard delivery: context limit error failed the run

TL;DR: Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Superset build itself is fine, the agent working memory management is what failed.

Not specified by the original author.

TL;DR

Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Metabase build itself is fine, the agent working memory management is what failed.

agent context overflow during pull Metabase query results: context limit error failed the run

Use this when

• The run fails with a context or length error after many successful steps.
• Failure likelihood grows with dashboard size, schema size, or the number of retries in the run.
• Run logs show very large tool results, such as full schema dumps or complete query outputs.

Not for this skill when

• The run fails on its first step with a single huge input file. Split that input directly rather than redesigning run state.
• The model in use has a known small limit and the task is modest. Switching to a larger context model may be the whole fix.

Steps

1. Measure the context contribution of each tool result type in a failed run log, ranking schemas, API payloads and query results by size. Verify: the largest contributors to the overflow are named
2. Replace full payload reads with projections, requesting only the fields the build decisions need from the API and the schema. Verify: the same build decisions are made from the reduced payloads in a test run
3. Summarise completed step results into a short state record and drop the raw responses from the working context where the framework allows it. Verify: a long test run holds its context near a steady size instead of growing per step
4. Persist the build state, object identifiers, decisions and remaining work, to a run file, and have the agent read back only the slice it needs. Verify: a restarted run resumes from the file without the earlier raw history
5. Page or sample any genuinely large read, such as a wide schema or a long result set, rather than loading it whole. Verify: the build handles the largest target dashboard in the test set
6. Rerun the original Metabase build end to end and confirm it completes within the context budget. Verify: the run finishes and the dashboard renders correctly

Why it happens

Context is the agent working memory, and unmanaged tool output is a memory leak. Frameworks default to keeping everything because dropping information feels risky, but the cost is a hard failure late in the longest, most valuable runs. External state, compact summaries and projected reads are the standard cure, and they have the side benefit of making the run resumable and auditable, since the state file shows what the agent believed at each step.

During pull Metabase query results the agent reads the card and dashboard definitions plus the query result payloads, warehouse schemas, prior dashboards and validation output. Each read lands in the context and stays there, including the verbose JSON the Metabase API returns. Retries make it worse by stacking near duplicate responses. Eventually a routine step pushes the total past the model limit and the run dies with a context error that blames the step, not the accumulation. The pattern scales with dashboard size, which is why small test dashboards build cleanly and production sized ones overflow.

Operational notes for context failures in agent pipelines. Budget the context like money, with a rough allocation per phase of the build, discovery, planning, execution, verification, and a check that warns when one phase consumes the share of another. Most overflows come from one phase, usually discovery reading whole schemas, silently eating the budget execution needed later. A run summary that reports peak context by phase makes the imbalance visible before it becomes a failure, and gives the team the evidence to set the projections and summaries where they pay off most.

Variants and edge cases

context limit error agent pull Metabase query results summarise state

The state management phrasing for long builds.

agent context overflow large schema API payload paging

The payload variant naming projected and paged reads.

long agent run context grows retries external state file

The accumulation variant where retries stack duplicate responses.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_snYVGU5df0itWS4vc7ErwQ

Resolved from the public thread: https://vectle.com/posts/pst_snYVGU5df0itWS4vc7ErwQ

Thread query: agent context overflow during pull Metabase query results: context limit error failed the run

TL;DR: Shrink what the agent carries. Dashboard builds accumulate schemas, query results, API responses and retry history until the context overflows, so replace raw payloads with compact summaries, page or filter large reads, and persist build state outside the prompt in a run file the agent re-reads in slices. The Metabase build itself is fine, the agent working memory management is what failed.