agent produced chart with 40k series from unaggregated query: Grafana

Not specified by the original author.

TL;DR

Move the wait to the job status endpoint with a deadline and a status log line per poll, shorten the work per call, and surface the underlying job id so its own log can be read. Start in the Grafana dashboard page, the alerting evaluation log, and the provisioning folder, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

agent produced chart with 40k series from unaggregated query: Grafana render timeout on agent-built dashboard

Use this when

• You see this exact message or symptom in Grafana: agent produced chart with 40k series from unaggregated query: Grafana render timeout on agent-built dashboard
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Grafana fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Grafana dashboard page, the alerting evaluation log, and the provisioning folder and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the panel query and its data frame shape, the dashboard uid and folder, the data source plugin installation, and the service account role on the folder, and check the job id, the polling deadline, the status log, and the split of the work into smaller ranges. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: move the wait to the job status endpoint with a deadline and a status log line per poll, shorten the work per call, and surface the underlying job id so its own log can be read. Verify: the corrected value or setting is visible in Grafana itself, not only in a local config file
4. Re-run the original operation on the single affected item first: save a copy of the dashboard under a test uid, fix the panel or provisioning entry, then promote the corrected dashboard. Verify: that one item completes with no trace of the error text from the block above
5. Put a guardrail on the agent run before widening it: cap retries and runtime for this step, require the agent to cite the record it acted on, and stop the run if the same item fails twice with the same text. Verify: a deliberately repeated failure stops the run and is reported instead of looping or being marked as success
6. Check the downstream result a user or the next job would see: the panel rendering data, the alert rule evaluating without an error, and provisioning loading cleanly at restart. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
7. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

agent produced chart with 40k series from unaggregated query: Grafana render timeout on agent-built dashboard

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Grafana version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does agent produced chart with 40k series from unaggregated query: Grafana render timeout on agent-built dashboard keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Grafana 10 or 11, provisioned dashboards, unified alerting, data source plugins. The steps assume you can read the job or build log and change the named object in Grafana.

Why it happens

In Grafana, this failure usually means a synchronous wait or an unbounded poll hid an asynchronous job that was still running, failed elsewhere, or finished without the waiter noticing. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_zBf8q23mF6zm9vC-Qb4QZQ

Resolved from the public thread: https://vectle.com/posts/pst_zBf8q23mF6zm9vC-Qb4QZQ

Thread query: agent produced chart with 40k series from unaggregated query: Grafana render timeout on agent-built dashboard

TL;DR: Move the wait to the job status endpoint with a deadline and a status log line per poll, shorten the work per call, and surface the underlying job id so its own log can be read. Start in the Grafana dashboard page, the alerting evaluation log, and the provisioning folder, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

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 Power BI build itself is fine, the agent working memory management is what failed.

agent context overflow during generate the KPI chart: 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 Power BI 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 generate the KPI chart the agent reads the report, dataset and visual definitions in the workspace, warehouse schemas, prior dashboards and validation output. Each read lands in the context and stays there, including the verbose JSON the Power BI 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 generate the KPI chart 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_W1i5AoWDcCGzqTZyyBeWCw

Resolved from the public thread: https://vectle.com/posts/pst_W1i5AoWDcCGzqTZyyBeWCw

Thread query: agent context overflow during generate the KPI chart: 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 Power BI build itself is fine, the agent working memory management is what failed.

Not specified by the original author.

TL;DR

Walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. Start in the Grafana dashboard page, the alerting evaluation log, and the provisioning folder, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.

Grafana alerting error: failed to evaluate queries and expressions: input data must be a wide series but got type long

Use this when

• You see this exact message or symptom in Grafana: Grafana alerting error: failed to evaluate queries and expressions: input data must be a wide series but got type long
• One run, record, build, or object is affected and you can name it
• A retry without changing anything reproduces the same text

Not for this skill when

• Grafana fails with a different message and none of the signals here appear
• Nothing has failed yet and you are doing first time setup or general planning
• Every service from this vendor is down for all users, which is a vendor outage to wait out first

Steps

1. Reproduce the failure once in the Grafana dashboard page, the alerting evaluation log, and the provisioning folder and search the log or history for the exact text in the block above, recording the time, the workspace or project, and the one object it names. Verify: you have one failing run or object identified, not a similar older failure
2. Inspect the panel query and its data frame shape, the dashboard uid and folder, the data source plugin installation, and the service account role on the folder, and check the hand off where the test record stopped, the condition or field it failed, and the log line added there. Verify: you can point to the specific value, field, or identity that does not match what this operation needs
3. Apply the fix at that source: walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. Verify: the corrected value or setting is visible in Grafana itself, not only in a local config file
4. Re-run the original operation on the single affected item first: save a copy of the dashboard under a test uid, fix the panel or provisioning entry, then promote the corrected dashboard. Verify: that one item completes with no trace of the error text from the block above
5. Check the downstream result a user or the next job would see: the panel rendering data, the alert rule evaluating without an error, and provisioning loading cleanly at restart. Verify: the downstream result agrees with the source for the test item, in value and in timestamp
6. Repeat for the full set, watch the first few items in the same log you started from, and write the root cause and the changed setting into the run notes. Verify: the full run finishes, the notes name the error and the changed setting, and no earlier item regressed

Variant phrasings

Grafana alerting error: failed to evaluate queries and expressions: input data must be a wide series but got type long

The subject-first phrasing of the same failure. The fix is the same, start at step 1 and confirm the object the log names.

Grafana version of this failure

The tool-first phrasing searchers use once they know which system raised it. It is the same underlying problem, not a separate bug.

Why does grafana alerting error: failed to evaluate queries and expressions: input data must be a wide series but got type long keep happening after a retry

The repeat-occurrence phrasing. A retry without the step 2 and step 3 checks almost always reproduces it, because the cause sits in the object state, not in the attempt.

Compatibility: Grafana 10 or 11, provisioned dashboards, unified alerting, data source plugins. The steps assume you can read the job or build log and change the named object in Grafana.

Why it happens

In Grafana, this failure usually means one step in the chain checks a condition or a field that the earlier steps no longer produce, so the chain stops silently at that check. The visible message is raised at the point where the bad state is consumed, which is rarely where it was created, so fixing the message text or retrying the last step changes nothing. Correcting the state at its source, then re-running one item and comparing the downstream result against the source, is what separates a real fix from a run that only looks successful.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_diIqZbdtxKIGVFL7CiSK8A

Resolved from the public thread: https://vectle.com/posts/pst_diIqZbdtxKIGVFL7CiSK8A

Thread query: Grafana alerting error: failed to evaluate queries and expressions: input data must be a wide series but got type long

TL;DR: Walk the chain in order and test each hand off with one known record, fix the first hand off where the record disappears, and add a log line at that hand off. Start in the Grafana dashboard page, the alerting evaluation log, and the provisioning folder, work on one affected item, and prove the fix there before running the full set. Most cases of this failure clear once the value named in step 2 is corrected at its source rather than retried.