Architecture
adpanel-core: Stateful Multi-Shot Agent Architecture
An open-weights, stateful AI video agent framework engineered to solve temporal and character degradation in synthetic media generation loops.
Technical Narrative
Traditional text-to-video foundation models are context-blind. When generating sequential frames or multi-shot campaign blocks, standard architectures cannot enforce asset persistence across separate API calls, leading to rapid degradation of product layout, lighting, and character fidelity.
adpanel-core shifts the paradigm from simple prompting to a stateful, long-running execution loop. The core engine programmatically intercepts unstructured input data (e.g., product photo matrices, CSV tables, Shopify endpoints) and converts them into a standardized, vector-embedded brand matrix. An internal SQLite state plane maps tracking parameters and visual seeds directly into downstream generation pipelines.
The Multi-Modal Reflection Loop
To ensure enterprise-grade asset safety, adpanel-core implements an asynchronous post-generation validation layer. After a media clip is synthesized, a vision-LLM node audits the frame buffer against the original brand geometry. If visual anomalies, broken physics, or warped logos are detected, the agent logs the failure trace, modifies the semantic prompt parameters, and re-executes the rendering routine automatically.
Code Architecture
# adpanel-core: Stateful Tracking and Verification Loop
import adpanel_core as apc
def execute_campaign_generation(product_id, target_shots):
brand_context = apc.memory.lock_brand_matrix(product_id)
stateful_timeline = apc.orchestrator.compile_shotlist(target_shots)
for shot in stateful_timeline:
raw_frame = apc.inference.generate_video_block(
context=brand_context,
seed=brand_context.visual_seed,
prompt=shot.semantic_prompt
)
# Trigger Multi-Modal Reflection Audit
audit_passed, error_logs = apc.reflection.verify_frame_buffer(
frame=raw_frame,
geometry=brand_context.geometric_bounds
)
if not audit_passed:
corrected_prompt = apc.reflection.heal_prompt(shot.semantic_prompt, error_logs)
raw_frame = apc.inference.generate_video_block(context=brand_context, prompt=corrected_prompt)
apc.storage.commit_to_sandbox(raw_frame)