AI agents that do the work, inside guardrails you set.

An agent is software that reads a situation, decides on a next step, and takes it in your real systems. We build agents with narrow jobs, scoped permissions, and a human on the decisions that carry risk.

01why now

Language models stopped being only text generators once they learned to call tools reliably: look up a record, fill a form, send a message, open a ticket, query a database. That is the step from a chatbot that answers questions to an agent that finishes tasks. Standards like the Model Context Protocol have also made it far cheaper to connect a model to the systems a business already runs, so the integration work that used to sink these projects is now a fraction of the effort.

What has not changed is that an agent is only as good as the job it is given. Most failed agent projects tried to build a general assistant for everything. The ones that pay back do one well defined job, end to end, measured against how a person does it today.

02what we build

• Inbound handling agents. Read incoming email, forms, or messages, classify them, pull the context from your CRM or ticketing system, draft or send the response, and route the exceptions to a person.
• Back office agents. Reconcile records between systems, chase missing documents, prepare reports, and update the system of record, the repetitive multi-step work that eats an operations team's week.
• Research and preparation agents. Assemble a brief before a sales call, a summary before a review, or a first draft of a proposal from your own past work.
• Customer-facing assistants. Grounded in your own knowledge base and able to act, such as booking, rescheduling, or checking status, with a clean hand-off to a human. Supreme Dental's patient apps carry an LLM assistant alongside scheduling and patient records. See the case study.
• Engineering agents. Agents that pick up tickets, write tests, and review code. That is a service of its own: AI coding orchestration.

agent or automation? the honest answer

If every step is predictable, you do not need an agent. A deterministic workflow automation with a model handling one or two judgment calls is cheaper, faster, and easier to audit. Agents earn their cost when the path varies case by case: when the next step depends on what the last one found. We will tell you which one your process needs before we price it.

03who this is for

Companies with a team doing high volume, rules-heavy, multi-system work: operations, customer service, finance, sales support, and intake. You are a fit if you can point to a queue, describe how a good employee works it, and give the team access to real examples. Typical sponsors run a department of five to fifty people and want capacity without the matching headcount.

You are not a fit if the process is undocumented and changes weekly, or if no one is willing to own the agent's results after launch. Agents need an owner the same way a new hire needs a manager.

04how it runs

1. Pick one job. We map the queue, the systems it touches, and the decisions in it, then choose the job where an agent's return is clearest. We collect a set of real past cases as the yardstick.
2. Design the permissions. Every tool the agent can use is listed, with read or write scope, and every action that moves money, changes a customer record, or speaks for the company gets an approval step or a hard limit.
3. Build and evaluate. Working software every two weeks, run against the real case set. We report how often the agent gets it right, how often it escalates, and what it costs per task.
4. Shadow, then supervise, then run. The agent first drafts while your team acts, then acts with approval, then acts alone on the cases it has proven it handles. Each stage is a decision you make on the numbers.
5. Operate. Logs of every action, alerts on drift, and a team that stays past launch to widen the agent's job as it earns trust.

Pricing is the same as all our work: a fixed number agreed before work starts, with change requests priced before they begin. If you want the agent connected to Claude or another MCP client, our MCP server development work covers the integration layer.

05common questions about AI agent development

What is an AI agent?

Software built on a language model that reads a situation, decides on the next step, and takes it using tools: looking up records, filling forms, sending messages, or updating systems. Unlike a chatbot, it finishes tasks instead of only answering questions.

What is the difference between an AI agent and workflow automation?

Workflow automation follows a fixed path with a model making one or two judgment calls along the way. An agent chooses its own path case by case. If every step is predictable, automation is cheaper and easier to audit; agents earn their cost when the next step depends on what the last one found.

How do you keep an AI agent from doing something harmful?

Scoped permissions and approval steps. Every tool the agent can call is listed with read or write scope, and any action that moves money, changes a customer record, or speaks for the company either needs a human approval or sits behind a hard limit. Every action is logged.

How do you measure whether an agent works?

Against a set of real past cases collected before the build. We report accuracy, escalation rate, and cost per task every two weeks, and the agent moves from shadow mode to supervised to autonomous only when the numbers support it.

Which systems can an agent connect to?

Anything with an API or a structured export: CRMs, ticketing systems, email, calendars, databases, document stores, and internal tools. Where a system has no API, we look for the safest workable path before building anything brittle.

How much does AI agent development cost?

A fixed number agreed before work starts, based on the job the agent does and the systems it touches. Change requests are priced before they are started, never after.

Will an AI agent replace our staff?

It replaces the repetitive part of their week. The pattern that works is a team that handles more volume without matching headcount growth, with people moving to the exceptions and judgment calls the agent escalates.