Would it rebook shipments on its own?

Only within limits you set — cost thresholds, approved carriers, service levels. Anything outside them is proposed with the options and costs laid out for a person to approve.

How does it know an exception has occurred?

Continuous monitoring of tracking events against expected milestones, so a missing scan is detected as an anomaly rather than waiting for a carrier to raise it or a customer to call.

How do you stop an agent doing something wrong?

Two mechanisms. First, permissions: an agent can only call the tools you granted it, and write actions are scoped as narrowly as the job allows. Second, approval gates on anything irreversible — sending external email, moving money, deleting records — so a person confirms before it happens.

How is this different from workflow automation?

Automation follows a path you defined in advance. An agent chooses the path at runtime. If you can draw the process as a flowchart that does not need constant new branches, automation is cheaper, faster, and more predictable — we will tell you when that is the better fit.

Autonomous AI Agents · Logistics

Autonomous AI Agents for Logistics

Exception handling is where operations time actually goes.

Shipments proceeding normally need no attention. Exceptions — customs holds, failed deliveries, damage, capacity shortfalls — each need investigation across several systems and a decision. Agents gather the context, propose the resolution, and escalate what genuinely needs a person.

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Step reasoning: MultiStep reasoning
Human approval: GatedHuman approval
Every action: TracedEvery action

Typical stack

LangGraph
OpenAI
Anthropic
FastAPI
Postgres

Use cases

What this handles for logistics operators

Investigate delivery exceptions across tracking, carrier, and customer records
Propose rerouting or rebooking options when capacity or timing fails
Assemble the full context for a claim or dispute before anyone opens the case
Monitor shipments continuously and act on slippage before the milestone is missed

Exceptions arrive at your team already investigated, with options rather than raw alerts.

Context

The logistics problem underneath it

Logistics runs on documents and status enquiries — bills of lading, customs paperwork, proof of delivery, and an endless stream of 'where is my shipment' calls. Both are highly automatable, and both currently consume operational staff who should be handling actual exceptions.

Shipping documents arrive in every format and get manually keyed into the TMS
Status enquiries flood phone and email, most of them answerable from tracking data
Exceptions and delays are spotted late because nobody is watching continuously
Carrier rates and capacity live across portals, spreadsheets, and inboxes

What we build

Inside an autonomous AI agent

We scope agents narrowly and give them explicit permissions. An agent that can read from five systems and write to one is dramatically safer than a general-purpose assistant with broad access. Every action is traced, every decision is inspectable after the fact, and anything irreversible waits for human sign-off.

Planner-executor architectures that decompose a goal into verifiable steps
Tool-use layers with typed inputs and strict permission boundaries per tool
Human-in-the-loop approval gates on anything irreversible or high-value
Memory and state management so an agent can resume long-running work
Multi-agent handoffs where a specialist agent takes over a subtask
Full execution traces showing every decision, tool call, and intermediate result

Deliverables

What ships with the engagement

Agent scope document defining goals, tools, and hard permission limits
Evaluation suite with recorded scenarios and expected outcomes
Trace viewer for inspecting any run step by step
Approval interface for the gated actions in your workflow
Cost and latency instrumentation per agent run

FAQ