| name | tiered-audit-remediation-workflow |
| description | A specialized workflow for implementing agentic "closed-loop" remediation where a visual failure (detected by a Vision LLM) triggers a creative rewrite (by a reasoning LLM). |
Skill: Tiered Audit Remediation Workflow
Description
A specialized workflow for implementing agentic "closed-loop" remediation where a visual failure (detected by a Vision LLM) triggers a creative rewrite (by a reasoning LLM).
Trigger Conditions
- When transitioning from text-only semantic consistency checks to multimodal (image + text) auditing.
- During the development of "Agent-in-the-loop" systems for generative media pipelines.
Implementation Protocol
1. The Multimodal Audit (The Eyes)
Implement a bridge method that accepts:
target_image: The generated asset.reference_images: Anchors/characters to ensure consistency.prompt_context: The original intent.
The output must be structured JSON containing: is_consistent, confidence, issues (list), and error_category.
2. The Semantic Fallback (The Safety Net)
Always maintain a text-only semantic audit as a fallback. If the Vision model fails or is unavailable, the system should still check if the prompt's description contradicts the Lore Bible using standard LLM reasoning.
3. The Creative Remediation (The Brain)
When an audit fails:
- Tier 1 (Visual Failure): Pass the
issues from the Vision model + the original prompt to a high-reasoning LLM (e.g., Hermes-3).
- Prompt Instruction: "Analyze this visual mismatch and rewrite the prompt to explicitly fix it while maintaining cinematic style."
- Tier 2 (Semantic/Logic Failure): Use standard text-based remediation if no image is present.
4. The Feedback Loop
Integrate the remediation_prompt back into the VisualAgent or Orchestrator to re-trigger the generation cycle.
Pitfalls & Lessons Learned
- Latency: Visual audits are slow. For demos, ensure models are "warm" and use fast inference endpoints.
- Schema Drift: Ensure the Auditor's output schema matches exactly what the Remediation Engine expects (e.g.,
error_category must be a consistent Enum).
- False Positives: Vision models can be overly sensitive to lighting/shadows; weight
confidence heavily in decision logic.
