agent-loop-ext
Crash-resilient external agent loop with state persistence and CI/CD integration
Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance
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Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance
namespace: aiwg name: eval-workflow platforms: [all] description: Run evaluation tests against a multi-agent workflow to assess orchestration quality and failure archetype resistance
Run automated evaluation tests against a multi-agent workflow.
/eval-workflow flow-security-review-cycle /eval-workflow flow-inception-to-elaboration --scenario distractor-test /eval-workflow flow-deploy-to-production --verbose --strict
| Argument | Required | Description | |----------|----------|-------------| | workflow-name | Yes | Workflow (flow command) to evaluate |
| Option | Default | Description | |--------|---------|-------------| | --scenario | all | Specific scenario to run | | --verbose | false | Show detailed test output | | --output | stdout | Output file for results | | --strict | false | Fail on any test failure | | --timeout | 300 | Maximum seconds per scenario |
1. **Load Workflow**: Read flow command definition 2. **Select Scenarios**: Based on --scenario flag or all applicable 3. **Setup Workspace**: Create isolated `.aiwg/working/` test space 4. **Execute Flow**: Run workflow against each scenario 5. **Validate Outputs**: Check artifact presence, structure, and content 6. **Generate Report**: Output results with pass/fail per assertion 7. **Cleanup**: Remove test workspace
{
"workflow": "flow-security-review-cycle",
"timestamp": "2026-04-01T10:30:00Z",
"scenarios": {
"grounding-test": {
"passed": true,
"score": 1.0,
"assertions": [
{"name": "threat-model-created", "passed": true},
{"name": "security-gate-run", "passed": true}
],
"duration_ms": 45000
},
"distractor-test": {
"passed": false,
"score": 0.7,
"assertions": [
{"name": "correct-assets-only", "passed": false, "evidence": "Distractor file referenced in output"}
],
"duration_ms": 38000
}
},
"summary": {
"passed": 4,
"failed": 1,
"total": 5,
"score": 0.80
}
}# Full evaluation of a workflow /eval-workflow flow-security-review-cycle # Single scenario with verbose output /eval-workflow flow-inception-to-elaboration --scenario grounding-test --verbose # Strict mode with output saved /eval-workflow flow-deploy-to-production --strict --output .aiwg/reports/deploy-eval.json
| Metric | Target | |--------|--------| | Artifact creation | 100% | | Grounding compliance | >90% | | Distractor resistance | >80% | | Recovery success | ≥80% | | Overall | ≥85% |
Evaluate workflow: $ARGUMENTS
Reusable project context and specialist workflows for the AI tools you already use. Plan software, coordinate specialist reviews, prepare campaigns, investigate incidents, organize research, curate media, and maintain operational knowledge.
Repo: jmagly/aiwg
Crash-resilient external agent loop with state persistence and CI/CD integration
Detect requests for iterative autonomous agent loops and route to the appropriate loop executor
Automatically execute tests when code-generating agents modify source files, enforcing the execute-before-return pattern
Enable agent loops to learn from similar past tasks and share patterns across loops
Query and manage the executable feedback debug memory
Execute tests on generated code and iterate until passing