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What It Takes to Build a Logistics AI Agent

The idea of an AI agent that can book freight, resolve exceptions, or even coordinate supply chain tasks sounds like science fiction. But it’s technically feasible thanks to advances in agentic AI powered by large language models (LLMs).


Still, going from concept to a working agent is not trivial. It requires a very different architecture than traditional automation. In this article, we break down the core components required to build an AI agent for logistics, including what current limitations you need to plan for.



Splice helps organizations understand how to prepare their infrastructure for logistics AI agents.
Transitioning from concept to a functional logistics AI agent is complex, necessitating a distinct architecture from traditional automation -- and Splice supports this critical need.

Splice is a no-code workflow automation engine purpose-built for logistics. We’re helping organizations understand how to prepare their infrastructure for it -- and may have an announcement soon about the insertion of AI in our integration platform.


Core Components of a Logistics AI Agent


A real-world agent must:

  • Understand human or system input

  • Break that input into goals or substeps

  • Select tools (i.e., API calls, document parsers, databases)

  • Run those tools

  • Evaluate the outcome

  • Loop until the goal is achieved or a human needs to intervene


To accomplish this, you’ll need the following layers:


1. LLM Engine


The core brain of the agent is a large language model (e.g., OpenAI GPT-4, Claude 3, Mistral, Gemini). This model handles:

  • Parsing natural language input

  • Choosing which tools to use

  • Reasoning over data, plans, and outcomes


2. Tool Registry (a.k.a. “Action Catalog”)


Agents must know what tools they can use. A tool is typically an API endpoint, script, database lookup, or connector. You register tools with descriptions, input/output schemas, and examples.


Example: JSON

{

  "name": "create_shipment",

  "description": "Creates a shipment in the TMS",

  "parameters": {

    "origin": "string",

    "destination": "string",

    "weight_lbs": "number",

    "service_level": "enum"

  }

}


3. Dispatcher (Execution Engine)


This layer handles:

  • Executing selected tools

  • Passing input/output between tools and the LLM

  • Tracking the flow of actions

  • Logging results and errors


It can be built using AWS Lambdas, Step Functions, or similar microservices.


4. Memory & Context Manager


LLMs have a context limit (e.g., GPT-4 = 128k tokens). You can’t just keep feeding it unlimited history.


You need a memory layer that:

  • Stores prior tool results

  • Summarizes long data into short context snippets

  • Injects only relevant data back into the next prompt


This can be backed by DynamoDB, Redis, or even a vector database.


5. Prompt & Planning Logic


Agents don’t use one prompt—they need many:

  • System prompt: defines the agent’s role

  • Tool call prompt: how to request a specific function

  • Reflection prompt: how to evaluate success/failure


Prompt engineering remains critical to agent behavior.


6. Observation & Error Handling


Agents must be monitored:

  • Did the agent do what it was supposed to?

  • Did it use the wrong tool?

  • Did it loop infinitely?


You need observability tools, fallback flows, timeouts, and human-in-the-loop

approvals.


Real-World Constraints to Plan Around


Building an agent is not like writing a script or snippet of code. There are real limits:


1. Token Limits

  • LLMs can only hold so much context. Use summaries.

  • Break big tasks into smaller chunks


2. Complex APIs Are Hard to Use

  • APIs with 100+ parameters confuse LLMs

  • You may need to create "shim" functions with fewer inputs


3. Tools Must Be Described Well

  • Tools with vague input descriptions lead to bad calls

  • Always provide examples and type constraints


4. Agents Can Hallucinate

  • They may invent fields, ignore results, or loop

  • Always validate tool inputs/outputs before use


5. Latency Adds Up

  • Each tool call may take seconds

  • Agents are slower than workflows for simple tasks


Where Our Platform Fits


You don’t need to build all this from scratch. Our platform already provides:

  • Pre-built logistics integrations (TMS, carrier APIs, EDI)

  • Workflow execution and retries

  • Tool interfaces that can be exposed to an agent

  • Human-in-the-loop steps


When agents are ready for production in your organization, we can help plug them into reliable operational flows.


Final Thoughts


Agentic AI is a major leap forward—but it’s not turnkey. Building an agent requires a careful architecture of:

  • Tool selection and schema design

  • Prompt engineering

  • Context and memory limits

  • Orchestration and observability


With the right foundation, logistics teams can begin to introduce agentic capabilities where they add value, while continuing to rely on deterministic automation for reliability and speed.


 Ready to Future-Proof Your Automation Stack?

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