AI agent development for back-office operations

The sign is a person whose day is spent moving information between systems and making the same small decision many times: the AP clerk with the inbox, the PO list, and the accounting system open at once; the dispatcher checking a portal four times a day; the office manager who re-keys every web request into the scheduling tool.

Those jobs share a shape. Something arrives. Someone reads it, looks up two or three things, decides, records the result, and tells the next person. Most of the decisions follow a rule. A few need judgment. An agent handles the first kind and routes the second to a person, so the person's day is exceptions, not the whole queue.

The business case decides whether that is worth building. In the assessment we trace an objective, more capacity without hiring, faster cash, fewer missed deadlines, to the handoffs behind it and rank the candidates by value and risk. Sometimes the answer is an agent. Sometimes it is a plain integration, and we say so.

A chatbot waits for you to ask. An automation follows a fixed rule. An AI agent is given a job and the access to do it: it reads what comes in, decides within the limits you set, acts in your systems, and asks a person when it is unsure. The industry word for this is "agentic" AI, which means the software takes actions rather than only producing text.

The value is not that the agent is clever. It is that a task a person did every day, in four browser tabs, now happens on its own, and the person only sees the exceptions.

Three of the agents we build most often, described the way we scope them: the job as it is done today, what the agent takes over, where a person stays in the loop, and what changes on the P&L.

1. AP invoice capture and coding

The job today. Invoices arrive by email, portal, and scan. Someone opens each one, finds the PO or the vendor's last invoice, checks the amounts, codes the lines to the general ledger, keys the bill into the accounting system, and sends it to an approver. On a busy week the queue backs up, early-pay discounts expire, and the occasional duplicate gets paid.

What the agent does. It watches the AP inbox and the vendor portals. For each invoice it extracts the vendor, dates, line items, and totals, matches them to the PO or to the vendor's history, codes the lines the way your bookkeeper would, and creates the bill in QuickBooks, Sage, or NetSuite as a draft. It attaches the source document and a note that says what it matched and why.

Where a person stays. Nothing is paid without approval. Clean matches go to the approver as a one-click queue. Anything with a variance, a new vendor, a missing PO, or an amount over the threshold you set goes to a person with the discrepancy highlighted. The agent never changes the vendor's bank details.

What moves. AP hours per invoice, duplicate payments, discounts captured, and month-end close time. Systems: email, the accounting system, Bill.com or the vendor portals, your PO source.

2. Container and last-free-day monitoring

The job today. A trucking company moving import containers has to know, for every box, when it is available and when free time ends. That information lives in the port's portal, the steamship line's portal, and the tracking provider, and the three do not always agree. Dispatchers check by hand, several times a day, and a missed date becomes a demurrage or per-diem bill.

What the agent does. It registers each inbound container with the tracking provider, pulls status and the last-free-date from the port and the line, and compares them. It sends dispatch a daily digest of every container's status and an alert in Teams the moment a date moves, a hold appears, or a container needs attention. This is the workflow we run for a Houston trucking company: an automated monitoring workflow with alerting, running across their TMS (TruckMate), the tracking provider (Vizion), and Microsoft Teams. The trucking version in detail →

Where a person stays. Dispatch decides what to do about an alert. The agent reports. It does not book, reschedule, or dispute a charge.

What moves. Demurrage and per-diem avoided, dispatcher hours spent on portals, and fewer surprises on the freight bill. Systems: TMS, the tracking provider, port and line portals, Teams or Slack.

3. Intake and scheduling

The job today. Requests come in through the website form, email, and voicemail. Someone reads each one, works out what the customer needs, checks the calendar and the crew, creates the job in the field-service or scheduling system, proposes a time, and follows up until it is confirmed. Requests that arrive after hours wait until morning, and some are lost.

What the agent does. It reads each request, pulls what it needs from the customer record and the job history, creates the job in ServiceTitan, Jobber, Housecall Pro, or your TMS with the right job type and notes, proposes slots from the real calendar, and confirms with the customer by email or text. If a phone receptionist agent is in front of it, the call becomes a job the same way, not a voicemail.

Where a person stays. The agent books inside the rules you give it: which job types, which crews, which hours, which customers. Anything outside them, a request it cannot classify, an urgent job, a customer with an open balance, goes to the office with the request summarized and a suggested next step.

What moves. Booked jobs per inquiry, response time, front-desk and office hours, and after-hours requests that no longer wait. Systems: web forms, email, phone system, the field-service or scheduling system, calendar, CRM.

The pattern is the same each time: one workflow, the systems it touches, the decisions that stay with a person, and the number it should move. That is how we scope, quote, and test every agent.

Each one names the systems it touches and the P&L line it moves. That is how we rank them in the assessment and how you should judge them.

We rarely build all twelve. Most companies get three or four that matter, in the order the P&L says. When the constraint is expertise held by a few people rather than a handoff, the answer is usually an AI knowledge system instead, and the two often work together.

An agent is only useful if it can read and write your real systems. That is the part most AI agent development services skip.

We connect agents to your TMS, FSM, accounting system, ERP, and email through their APIs, with least-privilege access: the AP agent can read invoices and create bills, and cannot touch payroll. Reads and writes are logged for the covered workflows, with what the agent saw and why it acted. If a system has no API, we work from its exports and tell you what that limits.

The agent runs in accounts you own: your cloud project, your database, your API keys. We build it; you hold the keys. How integrations work →

Agents earn trust in stages. A shadow period first, where the agent runs and a person keeps doing the job. Then a parallel run with review before anything leaves the building. Then production, with approval steps on the decisions you name: anything that moves money, anything customer-facing with a problem in it, anything the agent is unsure about.

Every action is in an audit trail. We document each agent's purpose, data access, limits, and review steps in a form aligned to the NIST AI Risk Management Framework, one of the frameworks Texas's AI law (TRAIGA) recognizes. If a customer, insurer, or regulator asks how your AI makes decisions, you have the answer written down.

Sometimes the AI feature inside software you already pay for is enough. If your TMS or FSM vendor ships an agent that does exactly the job, inside the one system, take it.

Custom AI agent development pays back when the work crosses systems, when the vendor's feature does not know your accounting system or your customer's portal, when nobody is accountable for it breaking, or when you want to own what you run. Off-the-shelf automation tools sit in between and are the right answer for some workflows. Zapier vs. Make vs. n8n vs. custom: when each one wins →

It starts with a conversation about what the business needs to achieve. If agents look like part of the answer, the next step is a two-day AI Readiness Assessment at your operation. Day one, we walk the workflows with the people who do them. Day two, we score every candidate on value, risk, effort, and readiness and hand you a ranked roadmap you keep, with a business case for the first initiative. Then we quote the first agent build: one eligible workflow, with acceptance tests in writing. If it misses them, the build fee is waived.

The assessment is $10,000 and is credited once against your first build.

• AI Readiness Assessment: $10,000, two days on site, credited once against the first build.
• Agent build workstreams: fixed fee, from $10,000 each, sized by systems touched, data quality, and number of integrations. Quoted before work starts.
• Managed AI: from $1,200 per month. Monitoring, fixes when a vendor changes an API, engineering hours included. Priority-one issues responded to within four business hours on the Managed plan.

Prices verified September 2026. Full detail on the pricing page.

Business goals first. Every agent is mapped to a P&L line and ranked by value and risk before we write code.

Built into what you already run. Your systems stay the systems of record. The agent works inside them.

Yours to keep, ours to run. You own the code, data, and accounts. We carry the pager.

Founder-built by Jonathan Klein and Chip Ray, based in Houston. Insured: cyber liability, technology E&O, and general liability.

Agents depend on vendor APIs and portals that change without notice. Our managed plan monitors every agent, responds when a vendor changes something, and includes engineering hours each month for the next improvement. How managed AI works →