Get Started with Agent Infrastructure

In our previous articles we talked about why supply chain organisations need to think of agents — automated AI helpers — and what kinds of problems they can solve, from acting on late shipments to optimising procurement. But here is the practical question: how do you actually build and support them?

Use Case: A Warehouse Agent

Let's say it's 4 PM and a truck hasn't arrived for its scheduled pickup. A basic alert system will tell someone to check on it. An agent, though, will:

1. Look at the current status of the shipment.
2. Check if the carrier has had similar delays.
3. Decide whether to escalate or adjust the dock schedule.

This kind of agent isn't magic — it needs a well-designed infrastructure behind it.

Infrastructure Pillars: Start Small, Think Big

Think of this infrastructure as five layers in a building. You don't need all five on day one, but knowing where you're headed helps you make the right choices now.

1. Data and Context Layer

What it does: Stores the data agents need — shipment records, carrier performance, inventory history.

Why it matters: Without clean, contextual data, an agent is guessing.

Start small: Hook up a data feed from your WMS or TMS. Even a daily export to a structured database is a beginning.

2. Agent Engine and Logic Layer

What it does: This is the "brain" of the agent — where it makes decisions.

Why it matters: This layer determines how smart your agents are.

Start small: Use simple if/then rules for your first agent (e.g., "if the truck is late by more than 2 hours, send an alert"). Over time, you can introduce AI-powered reasoning using language models.

3. Notification and Actions Layer

What it does: Sends the right message to the right person — or triggers an action automatically.

Why it matters: An agent that detects an issue but can't act on it is useless.

Start small: Use email or Slack integrations. Later, tie into your ERP or WMS to trigger actions directly.

4. Governance and Controls Layer

What it does: Keeps agents within acceptable bounds — defines what they can and can't do without human approval.

Why it matters: Trust is everything. Without controls, no one will use the system.

Start small: Set thresholds — e.g., agents can adjust dock schedules but must escalate financial decisions over $5,000.

5. Platform and Vendor Integration Layer

What it does: Connects agents to external tools and services (e.g., carrier APIs, supplier portals, weather data).

Why it matters: Agents need real-time context to make good decisions.

Start small: Connect one API — like carrier tracking — and build from there.

The Big Picture

Think of it like building a 5-story building:

• Floor 1: Foundation → Data and Context
• Floor 2: Brain → Agent Engine
• Floor 3: Voice → Notifications and Actions
• Floor 4: Rules → Governance
• Floor 5: Windows → External Integrations

You don't build floor 5 first. You start at the bottom and work up.

Why Start Now?

• The tools are more accessible than ever — cloud-based AI platforms, open-source models, and low-code tools mean you don't need a team of data scientists to begin.
• Your competitors are already exploring this — early movers get the advantage of refined models and better data.
• The cost of waiting is higher than the cost of starting — poor visibility and slow response times compound over time.

Next Steps: In Under 30 Days

1. Pick one use case (e.g., late shipment tracking).
2. Set up a basic data feed from your WMS or TMS.
3. Build a simple rule-based agent (if/then).
4. Route its output to an email or Slack channel.
5. Observe, refine, repeat.

Bottom line: You don't need a massive budget or a team of PhDs. Start with a clear problem, build a foundation, and let the infrastructure grow with your needs.