slug: agenta
provider: Agenta
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Software & Technology
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 3
edges:
- tag: Evaluations
  spec_file: agenta-evaluations-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.78
  evidence: POST /evaluations/runs/  createEvaluationRun  Create an evaluation run; queryEvaluationMetrics  Query
    aggregated evaluation metrics
  reason: Creating and querying LLM evaluation runs, results and metrics is model-quality evaluation within
    the AI/ML lifecycle (MLOps), which BC-610.60 covers.
- tag: Evaluators
  spec_file: agenta-evaluators-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.72
  evidence: POST /evaluators/  createEvaluator  Create an evaluator; GET /evaluators/catalog/templates/  List
    the built-in evaluator catalog templates
  reason: Evaluators are the scoring components used to assess LLM outputs — configuration of AI model
    evaluation machinery, i.e. AI/ML lifecycle management (MLOps).
- tag: Applications
  spec_file: agenta-applications-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.7
  evidence: 'POST /applications/revisions/commit commitApplicationRevision Commit a new revision of an
    application variant; schemas: PromptConfig, ApplicationVariant, Revision'
  reason: Manages LLM applications, their variants and versioned prompt configurations on an open-source
    LLMOps platform — model/prompt lifecycle and MLOps, i.e. Artificial Intelligence Management. Some
    ambiguity with generic software engineering surfaces, so 0.7.