Miadi Review

Refactoring Agent Loops into Accountable Circles

Oct 19, 2026 · model: gemini-3.7-flash · channel: JGWill

Watch source

Refactoring Agent Loops into Accountable Circles

Source: screenwalk-26101915-Five-Circles-Creative-Research-and-Miadi-Review-Evolution-part-002 — JGWill https://youtu.be/qOX-cHS_0Lg

Summary

This screenwalk demonstrates refactoring unbounded conversational agent loops into structured, accountable circles within the MIYADI orchestration ecosystem. Moving between ChatGPT Chimera and the review management service, the operator integrates human intention, academic governance standards, and automated evaluation frameworks to turn raw screencasts and model interpretations into reproducible, traceable engineering artifacts.

Aspects

Five-Circle Relational Governance (00:41-01:25)

The architecture separates multi-agent tasks into five accountable circles: Story, Stewardship, Engineering, Evaluation, and Continuity [00:41-01:25]. This structure refactors open conversational loops into bounded, auditable contracts, preserving human creative authorship while coordinating technical deliverables across distinct operational domains [12:20-13:30].

Web Review Service Integration (01:50-04:15)

The operator transitions directly to the live MIYADI review service to update Markdown documentation versions [01:50-03:52]. Review entries capture provenance metadata, video IDs, and explicit version lineage, ensuring that interactive AI explorations commit directly into shared development artifacts rather than remaining ephemeral chat outputs [03:45-04:15].

Multimodal Evaluation Circle Inquiry (04:46-05:10)

The evaluation circle defines a formal methodology to verify AI video comprehension [04:46-05:10]. It compares direct video API ingestion against transcript-only baselines and UI scraping, isolating timestamp accuracy, source grounding, and creative utility without assuming hallucinated indexing capabilities [13:08-14:00].

Formal Provenance and Standards Grounding (17:10-17:35)

The enrichment aligns agent workflow classification with recognized academic and industry standards [14:50-15:10]. By integrating ISO human-centred design, W3C PROV-O ontology, and OCAP data governance principles, conversational outputs are disciplined against rigorous external metadata and ethical boundaries [17:10-17:35].

End-results and Core Aspect

Core Aspect — Accountable Circle Orchestration (01:14-01:26)

The implementation grounds agentic execution in verifiable handoffs and durable versioning across the five circles [01:14-01:26]. Unbounded LLM dialogue is transformed into an auditable graph where every insight, prompt, and code revision links to tangible provenance and human intention [18:20-18:40].

Results

  • Version-Controlled Markdown Review Enrichment (03:25-06:03): Structured Markdown enrichment is generated and committed across iterative review versions on the web portal [03:25-03:55]. This embeds structured subsections, source links, and clear circle handoffs directly into the platform repository [05:45-06:03].
  • Reproducible Video Benchmark Protocol (13:08-16:00): A formal evaluation protocol is established to test multimodal model fidelity against original source footage [13:08-13:35]. The suite measures timestamp grounding, factual claims, and prompt variation across independent model APIs [15:35-16:00].
  • Traceable Persona Lineage in GitHub (18:05-18:38): Agent contributions from personas like Mia, Miette, JeremyAI, and Aureon are recorded into GitHub issue tracking [18:05-18:38]. Screenshots, prompts, and interpretive responses are locked into a single traceable lineage without unverified automation triggers [03:59-04:12].