At T3RA Logistics, a stack of narrow AI agents handles tenders, appointments, tracking, and pricing, saving tens of thousands per month and reshaping how a $30M brokerage runs freight.”
Most freight brokers talk about automation. Few can show precisely how it works, what it saves, and where it stops. At T3RA Logistics, those details are not only documented—they form the backbone of the company’s operations.
The Northern California brokerage, which moves roughly $30 million in freight each year across enterprise and defense lanes, runs on a “digital workforce” of agentic AI systems carefully designed by president and COO Mukesh Kumar. His goal was not to build a general-purpose AI dispatcher, but a set of narrow agents that excel at specific tasks with clear boundaries.
“We started with the workflows that caused the most friction for customers and the most inbox pain for our team,” Kumar explains. “Tendering, appointment setting, tracking, and rate building were at the top of the list.”
The result is a stack of four core agents, each with its own job:
- Tender Agent – Validates tenders against required fields, cross-checks documents, and assembles response packets. It only uses pre-approved pricing bands and routes anything unusual to human operators.
- Appointments Agent – Reads facility hours and rules, proposes appointment windows, and books via email or portals. It escalates if it fails to secure a slot after a fixed number of attempts.
- Tracking Agent – Sends status updates at agreed intervals, tags variances with reason codes, and issues alerts when exceptions go beyond defined thresholds.
- Pricing Agent – Constructs rates based on historical lanes, customer-specific bands, and market data. It never negotiates or commits to penalties, but drastically reduces time-to-quote.
Technically, each agent runs on top of a large language model tuned to logistics workflows, surrounded by rule-based guardrails and event-driven integrations into T3RA’s transportation management system, email, and portals. The architecture emphasizes auditability: every action, decision, and escalation is logged and reviewable.
“Agents aren’t interns,” says Kumar. “They’re coworkers with audit trails. You wouldn’t let an intern change timestamps or commit you to penalties without supervision. The same principle applies here.”
To keep things predictable, T3RA implements a traffic-light model for decisions. “Green” actions are fully automated and routine—things like confirming a normal status update or pulling in a facility’s published hours. “Yellow” actions require one-click human approval, such as accepting an edge-case appointment window. “Red” actions are blocked outright and escalated, including any attempts to override timestamps, negotiate claims, or commit to service levels that carry penalties.
This design flows directly from Kumar’s research on claims handling and carrier outreach, where the cost of a bad decision often exceeds the cost of a slower one. In his view, shipping operations are full of noisy data—bad reference numbers, inconsistent portal behavior, and incomplete tenders—that AI must learn to respect, not ignore.
“Data reality in freight is messy,” he says. “Agents that pretend it’s clean will hallucinate. We taught ours to admit when they’re unsure and to escalate instead of guessing.”
The measurable impact is significant. In side-by-side comparisons of lanes before and after agent deployment, T3RA reports:
- Double-digit reductions in touches per load, particularly in appointment scheduling and document checks.
- Improved on-time-in-full performance, with fewer missed confirmations for after-hours loads.
- A noticeable drop in exception rates, as routine updates are handled consistently and escalations are better documented.
- Approximately two full-time-equivalent hours moved from inbox management to higher-value work such as resolving escalated exceptions and nurturing customer relationships.
The Pricing Agent stands out. By automating the assembly of rates and limiting human intervention to genuine edge cases, it has cut quote cycle times from hours to minutes on many lanes. T3RA attributes roughly $40,000 per month in productivity gains to the pricing workflow alone, along with a margin lift from around 11% to 15%.
These numbers are not just internal wins; they shape how customers experience the brokerage. Faster, more accurate quotes help T3RA compete for volume without sacrificing discipline. Better tracking and appointment management reduce “where’s my truck?” calls and build trust.
What separates T3RA’s system from generic automation is the combination of agent specialization and governance. Each agent has:
- A clearly defined scope.
- A set of red lines aligned with legal and commercial risk.
- Observable metrics for success (touches per load, exception rate, response time).
- A human owner responsible for its behavior and updates.
Kumar views this as a blueprint for other mid-market freight brokers. He argues that an organization moving tens of millions of dollars in freight does not need to build custom foundation models or hire teams of AI researchers. Instead, they can start with a small set of well-scoped agents and expand from there.
“In week one, you map a single workflow and define the red-yellow-green rules,” he says. “By week four, you can have a supervised agent running in production on selected lanes, with clear KPIs.”
That step-by-step approach has turned T3RA into an early example of agentic AI in freight operations—not in the sense of sci-fi autonomy, but as a practical set of digital coworkers woven into the brokerage’s core processes.
For Kumar, the real innovation is not just the code, but the combination of systems thinking, domain expertise, and guardrail design.
“Freight doesn’t reward clever one-off hacks,” he says. “It rewards systems that show up every day, write down what they did, and make tomorrow’s work easier.”
As more logistics organizations grapple with rising costs, tightening capacity, and labor constraints, T3RA’s agent stack offers a concrete look at how AI can quietly reshape a brokerage from the inside out—one workflow at a time.
