Debugging Mwaa Workflow
AWSOfficialPublished by AWS
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- Review and audit
- Cloud and DevOps
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic, read-only diagnostic framework for troubleshooting Amazon Managed Workflows for Apache Airflow (MWAA). It handles both Provisioned (Python DAG) and Serverless (YAML) environments by routing failures through a standardized 4-step process: detection, identification, categorization, and context analysis. It leverages AWS CLI commands to query MWAA APIs and CloudWatch logs, providing actionable root cause analysis, impact assessments, and remediation recommendations while maintaining production safety by never executing state-mutating commands autonomously.
What it does
- Detects and categorizes MWAA workflow failures including task crashes, import errors, and IAM denials.
- Queries MWAA Provisioned environments via invoke-rest-api and CloudWatch logs.
- Analyzes MWAA Serverless workflows using GetWorkflowRun and task instance logs.
- Provides a structured diagnostic report with root cause, impact, and remediation steps.
- Offers specific troubleshooting checklists for common issues like DAGs not appearing in the UI.
- Maintains security by performing only read-only operations and suggesting safe remediation commands.
When to use it
- When a DAG or task fails unexpectedly in an MWAA environment.
- When a DAG is not appearing in the Airflow UI.
- When diagnosing worker OOM (SIGKILL) or dependency drift issues.
- When investigating 'AccessDenied' or 'EntityNotFound' errors in workflow logs.
- When needing to distinguish between definition errors and task-execution failures.
Advantages
- Standardizes the diagnostic process across different MWAA deployment models.
- Prioritizes safe, read-only investigation to prevent accidental production changes.
- Provides clear, actionable remediation paths for complex issues like dependency drift.
- Reduces manual log-diving by automating the retrieval of relevant error details.
- Includes specific guidance for common, non-obvious issues like scheduler scan intervals.
Usage examples
- “Why did my workflow fail?”
- The agent will identify the environment flavor, list recent failed runs, pull the relevant error logs, and provide a structured report with the root cause and remediation steps.
- “My DAG is not showing up in the UI, help me debug.”
- The agent will walk through the DAG registration checklist, checking for import errors, scan intervals, dag_id collisions, and S3-sync delays.
- “Why is my worker crashing with a SIGKILL?”
- The agent will analyze the worker logs, confirm the environment class and memory constraints, and recommend moving heavy tasks to external services like Glue or EMR.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/aws/debugging-mwaa-workflow/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip debugging-mwaa-workflow.zip -d .agents/skills
Documentation by AWS
Debugging MWAA Workflows
AWS MCP server (optional but recommended): running the AWS CLI commands in this skill through the AWS MCP server gives sandboxed execution and audit logging. Every command here also works with the plain AWS CLI, so the skill does not require the MCP server or any MCP-only tools.
Diagnose and root-cause Amazon MWAA workflow failures, then report root cause, impact, and recommended remediation. Routes by flavor, then runs a shared 4-step diagnostic spine.
Guardrail — where this skill's own files live (MCP vs local install)
This skill can be loaded two ways, and they resolve the skill's own bundled files from different places. Determine how the skill was loaded before reading a reference:
Loaded through the AWS MCP
retrieve_skilltool: The skill is not installed on the local filesystem. You MUST fetch each reference viaretrieve_skillwith thefileparameter (e.g.file="references/failure-catalog.md") and read the returned content. Do NOTfile_readthese paths locally — they do not exist on disk.Installed locally (e.g.
.kiro/skills/debugging-mwaa-workflow/or~/.claude/skills/debugging-mwaa-workflow/): Read the files from the local skill directory using relative paths.
This distinction applies only to the skill's own packaged files. User data and
session artifacts are always read from and written to the user's working
directory. Never fetch or write customer data through retrieve_skill.
Step 0: Detect Flavor and Scope the Failure
Detect flavor
An environment name resolvable via
aws mwaa get-environmentmeans the environment is Provisioned.A
workflow/...ARN or anyaws mwaa-serverlesscontext means the environment is Serverless. A bare run identifier does NOT indicate flavor — Provisioned DAG runs also have run ids.If neither signal is present, ask: is the target MWAA Provisioned (Python DAG) or MWAA Serverless (YAML workflow)?
Route by complexity
Simple — a single named task or run failed with a clear exception. Jump to Step 2 for that task.
Standard — a run failed and the cause is unknown. Run the full Step 1 to Step 4 sweep.
Complex — intermittent or environment-wide (multiple DAGs, "worked yesterday", nothing appearing). Run the full sweep with emphasis on Step 3.
Step 1: Identify the Failure
Provisioned: list failed DAG runs and task instances via
aws mwaa invoke-rest-api (paths /dags/{id}/dagRuns and
/dags/{id}/dagRuns/{run_id}/taskInstances). If invoke-rest-api errors
(RestApiClientException), fall back to the Scheduler and DAGProcessing log
groups. Get version and config from aws mwaa get-environment. See
references/provisioned-diagnostics.md.
Serverless: aws mwaa-serverless list-workflow-runs, then
get-workflow-run. Read RunDetail.ErrorMessage — an empty TaskInstances
with a parser message is a definition error; Workflow execution failed with
populated TaskInstances is a task-execution failure. See
references/serverless-diagnostics.md.
Step 2: Get Error Details and Categorize
Pull the real exception past boilerplate:
Provisioned: read the Task log group first, then Worker/Scheduler/ DAGProcessing as the symptom directs.
Serverless: list-task-instances then get-task-instance to get each
task's LogStream, then read that stream in CloudWatch.
Then categorize in priority order — infra, then drift, then code-data —
using references/failure-catalog.md. The
category determines the Step 3 checks.
Step 3: Check Context (Why It Happened)
Run the context checks for the matched category from references/failure-catalog.md. Do not stop at the surface exception: a SIGKILL is an OOM story, a fresh import error on unchanged code is a drift story, a sensor timeout is an upstream-health story.
Step 4: Provide Actionable Output
Report in this exact structure:
Root Cause: <one-line diagnosis with the evidence that proves it>
Impact: <what failed, which runs, blast radius>
Immediate Fix: <the smallest change that unblocks>
Prevention: <the change that stops recurrence>
Commands: <exact read-only commands run, plus remediation commands for the user to run>Run only read-only operations. Present state-mutating remediation (clear/rerun/backfill for Provisioned; start-workflow-run or fix-and-redeploy for Serverless) as commands for the user to run, with the impact stated. Never execute them autonomously (production safety).
For the fix-and-redeploy path, use authoring-mwaa-workflow to regenerate a
compliant artifact.
Gotchas
Serverless has no Airflow web UI, no REST API, and no CLI token. Do not attempt
create-web-login-token,invoke-rest-api, or any Airflow REST path for Serverless.For Provisioned, always use
aws mwaa invoke-rest-api(notcreate-web-login-token+ curl).invoke-rest-apireaches VPC-only web servers without network access.GetWorkflowRun.RunDetail.ErrorMessagedistinguishes a definition error (emptyTaskInstances) from a task-execution failure (Workflow execution failed, populatedTaskInstances). Read it before pulling task logs.The Serverless log group defaults to
/aws/mwaa-serverless/{workflow-id}/but can be a custom group; confirm viaget-workflowLoggingConfigurationbefore assuming the path.A DAG not appearing has several causes — an import/parse error, the scheduler scan interval (
scheduler.dag_dir_list_interval, ordag_processor.refresh_intervalon Airflow 3.x) not yet elapsed, adag_idcollision, or S3-sync delay — and is rarely a broken DAG. CheckGET /importErrorsandGET /dags/{dag_id}via invoke-rest-api (and the DAGProcessing logs); see the failure catalog's "DAG not appearing in the UI" checklist before concluding the code is wrong.A worker SIGKILL is an OOM signal. Recommend moving work to Glue/EMR/Lambda; scaling workers alone does not fix per-task memory pressure.
MWAA re-resolves dependencies on environment update, so an unchanged DAG can start failing on import with no code change. Treat no-code-change import failures as drift.
Serverless
PythonOperator/BashOperatortasks run custom code from a--codepackage. A run that fails to extract the package or hitsModuleNotFoundErroris a packaging problem (wrong-platform wheel, missing dep, bad layout), not a YAML definition error. See the failure catalog's serverless custom-code section.
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
RestApiClientException (Provisioned) | Mis-scoped execution role or service error | Fall back to Scheduler/DAGProcessing log groups |
ResourceNotFoundException on get-workflow-run | Wrong workflow ARN or run id | Re-list with list-workflow-runs |
| Task log stream empty (Serverless) | Wrong log group assumed | Read LoggingConfiguration from get-workflow |
| No task logs but run FAILED | Definition/parse error | Read RunDetail.ErrorMessage; fix the YAML |
References
references/provisioned-diagnostics.md — Provisioned data sources and read-only commands
references/serverless-diagnostics.md — Serverless API, log group, failure classes
references/failure-catalog.md — category-keyed context checks and remediation
Security Considerations
Read-only by default: diagnosis uses only read/list/describe calls. Remediation (clear/rerun/backfill, IAM or key-policy changes) is presented as commands for the user to run, never executed autonomously.
Least-privilege IAM: when an
AccessDeniedis a genuine permission gap, recommend the minimalAction/Resourcefrom the error — never a wildcard; distinguish it from a nonexistent-resource typo (do not broaden IAM then).Cross-account: KMS key-policy / assume-role changes are human-gated and coordinated with the resource owner.
No secret exposure: do not surface credentials or connection strings from logs or API responses in the diagnosis output.
Files
- ATTRIBUTION.mdAdded by AutoGineer926 B
- LICENSELicence file added by AutoGineer9.9 KiB
- NOTICELicence file added by AutoGineer67 B
- SKILL.md9.1 KiB
- references/failure-catalog.md11.4 KiB
- references/provisioned-diagnostics.md6.0 KiB
- references/serverless-diagnostics.md2.9 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs an MCP server
Security
No issues found by our static scan· No issues found
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Attribution and licence
- Published by
- AWS
- Publisher
- AWS (Official)
- Source
- aws/agent-toolkit-for-aws · plugins/aws-data-analytics/skills/debugging-mwaa-workflow · commit 0a218f8
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- NOTICE (added by AutoGineer from NOTICE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Also distributed in
- aws/agent-toolkit-for-aws
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