We completed a major internal refactor of the EDMA operational platform.

The system now follows a strict service architecture:

Interface → Service Layer → Gateway → Database

This change ensures:

• no direct database access from the interface

• all business logic enforced centrally

• improved security and validation

• cleaner integration for AI agents and external services

During this refactor we migrated 40+ database operations into controlled services.

This dramatically improves the reliability and scalability of the platform.

1. Preparing the Platform for AI Agents

One of the key goals of this release was preparing EDMA for the next stage:

AI agents interacting directly with business operations.

To make this possible we built a centralized service layer that now manages all core system operations.

These services include:

• client management

• product and supplier systems

• order processing

• invoicing and payments

• cost tracking

• shipments

• document management

Future AI agents will interact with the platform through these services, allowing them to safely automate workflows without direct database access.

This architecture is a core building block for the upcoming AI Bot Studio.

2. Product & Supplier System Rebuilt

The product and supplier infrastructure was fully migrated to the new architecture.

New components introduced:

ProductService

SupplierService

These services now handle:

• product creation and editing

• supplier relationships

• product-supplier connections

• bulk catalog operations

This upgrade enables the platform to support large-scale catalogs and automated sourcing workflows, which will be important for enterprise integrations and AI automation.

3. Finance System Safety Improvements

Financial workflows were upgraded to improve reliability and consistency.

Invoice & Payment Synchronization

The system now ensures that:

1. payments are created first

2. invoices are only marked as paid after successful payment creation

This prevents situations where an invoice could previously appear paid without an associated payment record.

This change strengthens the financial integrity of the platform.

We also implemented stricter lifecycle controls for operational records.

This means that:

• orders must follow valid lifecycle stages

• invoices cannot jump between invalid states

• cost records must move through correct operational transitions

These controls help prevent corrupted business data and make automation safer.

4. Order Editing Reliability Improvements

Order editing logic was redesigned to prevent data loss during complex updates.

Previously, certain rare failure scenarios could cause order lines to be removed incorrectly.

The system now guarantees that:

• existing order lines are preserved during failures

• partial updates cannot destroy existing data

• errors are surfaced clearly to the user

This change significantly improves operational safety when modifying orders.

Shipment Intelligence

Another piece we implemented is shipment tracking.

Each shipment includes information about origin port, destination port, carrier, container allocation and estimated arrival timelines.

This gives the platform a clear picture of the logistics side of the operation.

As part of this refactor we removed a large amount of legacy infrastructure that existed from early prototypes.

This included:

• unused database helpers

• duplicated service logic

• obsolete write paths

Cleaning this up reduces complexity and allows us to build new features much faster moving forward.

A platform where users can build and deploy AI business agents capable of interacting with real operational workflows.

These agents will automate tasks across:

• orders

• inventory

• finance

• logistics

• document processing

The runtime environment that will allow multiple AI agents to operate safely within EDMA.

This layer will manage:

• permissions

• resource usage

• coordination between agents

• execution monitoring

In parallel, work continues on the EDMA Layer-2 architecture designed to support the long-term ecosystem.

This network will enable:

• AI agent identity

• decentralized coordination between agents

• tokenized execution incentives

• distributed infrastructure for automation

More technical details on the Layer-2 design will be shared in upcoming development releases.

This development cycle focused on something less visible but absolutely critical:

building the infrastructure required for scalable AI automation.

With the service architecture now complete, the platform is ready for the next phase of development.

The groundwork for AI agents, decentralized automation, and the EDMA Layer-2 ecosystem is now firmly in place.

More updates coming soon.