Compliance and Audit in Agent Workflows: What Supply Chain Leaders Need to Know

The rise of AI in logistics is creating powerful new possibilities, but not without risk. In highly regulated areas of the supply chain, such as customs documentation, hazmat handling, and cross-border operations, introducing autonomous software agents raises important questions about compliance, auditability, and control.

While the concept of AI "agents" is generating significant buzz, it's critical for logistics professionals to understand the difference between agent-based AI and the more widely adopted workflow automation platforms that dominate today’s enterprise stack.

In this article, we’ll break down:

• What agentic workflows are and how they differ from traditional automation

• Why compliance and auditability are critical in workflows

• How legacy systems (EDI, XML, CSV) complicate AI integration

• What to look for in a platform, now and in the future

What Are Agentic Workflows?

An agentic workflow involves software that can autonomously make decisions, take action, and adapt its behavior based on goals. For example, an AI agent could be asked to “prepare all customs paperwork for a shipment to Brazil” and then independently:

• Retrieve documents

• Interpret regulations

• Fill out forms

• Communicate with third-party systems or brokers

• Submit filings

This contrasts sharply with workflow automation platforms, like our own, which are rule-based and deterministic. These platforms execute predefined sequences of steps based on logic, APIs, and structured data flows, and they are initiated by triggers. 

Why Compliance and Auditability Matter

In logistics and supply chain operations, many workflows are subject to strict regulatory oversight:

• Customs: Automated systems must adhere to country-specific filing requirements (e.g., ACE in the U.S. or CERS in Canada).

• Hazardous Materials (HAZMAT): Compliance with DOT, IATA, and IMDG labeling, documentation, and handling rules.

• Cross-border trade: Requires traceable origin, tariff code validation, VAT documentation, and audit trails.

When AI agents act autonomously, who is responsible if they make the wrong call? 

Without appropriate constraints and visibility, agentic workflows could lead to:

• Fines or legal exposure

• Invalid or rejected customs entries

• Shipment delays

• Incomplete or untraceable audit logs

The Challenge of Legacy Data Protocols

Much of global logistics still runs on older data exchange formats:

• EDI (Electronic Data Interchange): Used for purchase orders, ASNs, invoices

• Flat files and CSVs: Still common for batch uploads

• XML schemas: Required by government systems and legacy enterprise software

These formats predate modern RESTful APIs and are structured, brittle, and domain-specific. AI agents struggle to ingest and act on this data without extensive training or preprocessing. In contrast, workflow automation platforms can integrate with these formats using parsers, mappers, and adapters,ensuring compatibility and reliability.

Agentic AI has promise, but today’s logistics still depends on structured automation to handle this legacy infrastructure with consistency.

How Workflow Automation Platforms Handle Compliance Today

Platforms like ours (think Zapier for logistics) are designed to offer:

• Deterministic execution: Every step is rule-based and transparent

• Audit trails: Every action is logged and timestamped

• Human-in-the-loop control: Review and approval steps can be added

• Integrations with legacy and modern systems: Including EDI, APIs, flat files

• Custom validation logic: Tailored to your compliance requirements

These features make workflow automation ideal for regulated environments, where traceability and compliance are not optional.

Looking Ahead: Preparing for Responsible Agent Adoption

Agent workflows introduce new complexity, especially in compliance-heavy sectors. Agent-based automation may eventually complement or extend these workflows, but not until platforms can guarantee visibility, validation, and rollback. 

To prepare, organizations should:

• Map their compliance-critical workflows

• Identify areas where automation is deterministic vs. discretionary

• Prioritize tools that provide full auditability

• Avoid agentic systems that lack constraint mechanisms or override controls

While agent-based AI holds promise, regulated logistics workflows require a cautious, transparent approach. Today’s compliance best practices use workflow automation platforms that support compliance, offer structured integration with legacy systems, and provide complete visibility.

Our platform is built for exactly this: workflow automation in complex, high-stakes supply chain environments. While we’re exploring the potential of AI agents, our focus remains on giving logistics teams control, reliability, and auditability - fundamentals you can’t afford to compromise.