AI Automation: Deploying Multi-Agent Creative & Technical Orchestration

Automation Stack & Architecture

Agent Frameworks: Hierarchical Agent Management via Claude Projects (Cloud sessions + sub-agents); Specialized single or task-specific models such as Qwen3.5 9B / GPT-4 level logic for radio automation, music selection, and scriptwriting/voiceover coordination.
Integration Layers: GitHub API (for code updates/PR management), CAD modeling software (Blender animation support mentioned), Web Scraping/Research engines, Component procurement APIs (simulated through supply chain interaction).
Target Outcome: Fully autonomous content generation (Radio station broadcasting, YouTube short MP4 creation) and physical product conceptualization to assembly instructions prepared.

Content Production Lead (Claude PM Role)

Goal: Manage complex projects by splitting work into parallel threads with shared memory으로 context preservation.
Tools assigned: Cloud session executor (`sub_agent` execution), Git command tools (branching/merging PRs in repositories), test runner scripts.

AI Radio DJ System

Persona: Autonomous broadcaster responsible for track selection based on user history, time of day, or weather conditions via `Qwen3.5`.gettracklist().
Tasked skills: Music curation, writing conversational introductions ('подводки'), and coordinating voiceover outputting tasks properly even if LLM logic fails backto normal playlist mode.

Hardware Design Agent (Astra-based agentic loop)

Persona: Engineering designer capable of moving from concept to procurement list and technical documentation.
Tasks: Conceptual image generation, component research using web browsing toolset [searching Chinese specs], CAD model creation, Blender animation rendering instructions ([assembly animations]).

Trigger: User provides a prompt such as an article text OR any new repository code update request enoughmruously prepared with target goals.
In the Claude Project Manager workflow, split task into parallel cloud sessions; in the Video Production_tool/anything2explainer/id setup, analyze input source content first (`article analysis`).
Execute scriptwriting phase where AI generates narrative or dialogue based on provided context templates like `scripting`.
Transition to asset production stage including storyboard design, animation via JavaScript frame-by-frame coding, adding subtitles ($subtitles), applying transitions/animations if part of the automated video montage platform pipeline.
Perform audio synthesis ('озвучка') for voiceover tasks (noted that Qwen handles scripts while another specialized net executes speech).
Run final assembly check through testing cycles used by Claude Code subagents before finalizing output files seperti MP4s hoặc PR requests and merging results backto main branch properly correctly using shared memory logic [shared project database].

Graceful Fallback Logic: If LLM service fails during radio broadcast execution such as a delay from any model engine k enoughmruously prepared maybe error occurrtingly fail right way please correct err m r u s t q w e n , just switch playback mode immediately type normal playlist without interruption stopping music stop skip skipping song loop improperly wrong incorrectly wrongly mistake failing failed unable disable incorrect bad let'get track list thing stuff okay? we shouldn {the user calls this regular mp3 or wav file play standard sequence instead}. `, system reverts simply to playing an ordinary pre-set playlist so no empty silence occurs (`fallback_playlist`).
Human Approval Gates / Review Loops: For hardware/physical items, use human input (

! DYOR (Do Your Own Research)

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