Inspiration
E-commerce teams work with fragmented data across stores, inventory, analytics, advertising, payments, SEO, and content tools. Most tools report metrics, but they do not explain why something changed, connect the signals across systems, or provide a safe path from insight to action.
AgentCommerce AI was created as a unified commerce intelligence workspace that combines operational intelligence with semantic SEO, AI visibility, content, and knowledge graph capabilities.
What it does
AgentCommerce AI follows a simple operating loop:
Detect → Explain → Decide → Approve → Verify → Remember
The platform helps a commerce team:
- Detect revenue, conversion, inventory, performance, SEO, and visibility issues.
- Explain possible causes and business impact.
- Audit SEO and AI-search readiness.
- Generate structured content, FAQs, schema recommendations, and internal-link suggestions.
- Connect products, categories, content, campaigns, and customer journeys through a knowledge graph.
- Ask a read-only commerce copilot questions about the workspace.
- Create proposed actions that require approval before execution.
- Record audit history, verification results, and rollback metadata.
The current demo includes deterministic adapters for commerce, analytics, advertising, payment, performance, and search data. These adapters allow the complete workflow to be demonstrated without requiring real customer credentials.
The unified workflow
- Connector data is normalized into a common commerce signal model.
- Detection rules identify anomalies, risks, and opportunities.
- Diagnosis logic ranks likely causes using severity, confidence, evidence, and estimated business impact.
- The agent creates an explainable recommendation and action plan.
- The action remains pending until a user approves it.
- Verification records whether the expected outcome occurred.
- The result is stored in the workspace memory and audit trail.
How we built it
The application contains:
- A Python backend with REST-style application routes.
- SQLite persistence for the local demo workspace.
- Workspace-scoped authentication and protected CRUD operations.
- A browser frontend with dashboard, detection, SEO/GEO, content, commerce intelligence, knowledge graph, copilot, actions, approvals, audit, memory, connectors, and settings pages.
- A connector registry and normalized demo data layer.
- Detection and diagnosis services.
- Agent and action-policy services.
- SEO/GEO analysis and content recommendation modules.
- Knowledge graph entities and relationships.
- Append-only audit records.
- HMAC-bound approval tokens, session protection, CSRF protection, kill-switch support, action allowlists, preconditions, postconditions, and verification records.
Safety and trust
AgentCommerce AI is designed around approval-gated execution. In demo mode, proposed actions do not make live changes to a Shopify store, advertising account, payment provider, or search platform.
The system records who requested an action, what evidence supported it, what permissions were required, whether it was approved, and what verification result was produced.
OpenAI and Codex usage
OpenAI Codex was used during development to inspect the repository, implement backend and frontend features, improve the application architecture, create tests, write documentation, and debug the local application workflow.
OpenAI reasoning was also used for product architecture, agent workflow design, safety boundaries, detection logic, SEO/GEO planning, and acceptance criteria.
Model disclosure: GPT-5.6-LUNA(MAX)
The current public demo uses deterministic fallback data and rules so that judges can run it locally without API keys. A production deployment would add a configured OpenAI provider, Shopify OAuth, webhooks, background workers, encrypted secret storage, production observability, and managed infrastructure.
Challenges
The main challenge was combining operational commerce intelligence and semantic search visibility into one consistent workflow without allowing an AI system to make unreviewed production changes.
Another challenge was keeping the demo useful without requiring judges to configure multiple third-party accounts. For that reason, the project separates connector contracts from provider credentials and includes deterministic demo fixtures.
What's next
The next implementation stage is Shopify-first production connectivity:
- Shopify OAuth and installation flow.
- Webhook ingestion and incremental synchronization.
- Encrypted credential storage.
- Background job processing.
- PostgreSQL and object storage.
- Real OpenAI provider integration.
- Staging previews, live execution, rollback, and observability.
- Multi-tenant deployment with production monitoring.
AgentCommerce AI is intended to become a commerce operating system where data, intelligence, content, and safe execution work together in one workspace.
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