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Job Success
AI agents · shipped, not theoretical
An agent that answers questions is a chat box. An agent that acts is software.
I have shipped two agents that change real state in real business systems — one resolving load issues for a freight brokerage, one creating invoices by voice for field technicians. That is a different engineering problem from a support bot, and most of it is the safety.
The parts that cost you money
01
Your chatbot cannot do anything
Most business AI reads a help centre and paraphrases it. The customer still waits for a human to perform the actual action, so the queue never gets shorter and nobody trusts the bot.
02
The same twenty questions, forever
Where is my load. When is the tech arriving. Why was I charged this. Every one is answerable from data already in your system, and every one interrupts somebody doing higher-value work.
03
Typing is the bottleneck in the field
A technician holding a phone in one hand at a job site will not fill in a six-field form. The data you never captured is the invoice you never sent.
04
Nobody will let AI touch production without a boundary
And they are right. An agent that can change records without a permission model and an audit trail is a liability, not a feature. This is the actual hard part.
The system that fixes it
Agents with a permission model
What the agent may do without a human, what needs approval, and what it may never touch — defined explicitly, enforced in code, not implied by a prompt.
A full audit trail
Every action the agent takes is logged with its reasoning and is reversible. When somebody asks why a record changed, there is an answer.
Voice-driven operations
Shipped in a field-service platform delivered white-label, so it cannot be named or linked: a technician creates an invoice or updates a record by voice, with the surrounding operations running automatically from there.
Agents that resolve, not escalate
Shipped in Trucks On Demand: an agent that handles incoming complaints and takes real actions on loads rather than routing them to a person.
What this page is not claiming
These are agents operating inside business systems I built. That is not the same as a research-grade autonomous agent, and it is not a claim about regulated decision-making — nothing here decides anything a regulator would care about. TraceVita is proof of LLM features in a consumer wellness app, not of clinical or HIPAA scope.
Closest thing I've already shipped
Asked before every build
What stops the agent doing something expensive?
A permission model that names exactly which actions it may take unattended, which require approval, and which are off limits entirely — plus a reversible audit trail. In practice that boundary is where most of the build time goes, and it should be.
Can you add an agent to software I already have?
Often yes, provided the system has an API or a database I can work against safely. That usually starts as a paid review so the answer is based on your actual code rather than a guess.
Which models do you use?
Whatever fits the task, the latency budget and the cost per action. That decision belongs in the build, not in a marketing page, and it should be revisited as models change.
Twenty minutes, your morning, no pitch deck.
I work 8am – 12pm US Eastern and replies typically land within ~2 hours. Bring the problem, not a spec — the first call is scoping, and it is free.