Workflow Agents
Agents that triage, summarize, route, research, draft, update records or initiate actions inside a defined business process.
We design and implement AI agents for businesses across the United States. They connect to the systems your team already uses, take repetitive work off people's plates, and keep a human in control where judgment matters.
We design agents as durable operational components—not novelty chatbots. That means clear inputs and outputs, permissions, exception handling, monitoring, auditability and human review when appropriate.
Agents that triage, summarize, route, research, draft, update records or initiate actions inside a defined business process.
Secure assistants grounded in internal procedures, documents, policies and operational knowledge.
Connect APIs, SaaS platforms, databases and internal software to reduce manual handoffs and repetitive work.
Require review or approval where actions have material business, financial, legal or customer impact.
Create clear ownership, logging, access controls and operational boundaries for AI-enabled workflows.
Embed agents within custom applications so AI becomes part of the product experience rather than another separate tool.
It isn't usually because the AI isn't good enough. It's because the company picked the wrong first job for it.
The typical first request is huge: "an agent that runs sales," or "an agent that handles customer service." Those goals are too big, too vague, and have too many ways to be wrong. Nobody can say what "done" looks like, so nobody can tell whether the agent is working. The project turns into a demo that impresses once and then gets quietly shelved.
The agents that actually work start with one narrow, repetitive job. For example:
Each of these has one job, clear inputs and a clear definition of "done." A person checks the output until the agent earns trust. Then you give it the next job.
That's how you end up with an agent you rely on instead of a demo you show once.
Before we build any agent, we put a rough number on the work it would take over:
Hours per week spent on the task × loaded hourly cost × 52 weeks
Example only: a task that takes 10 hours a week at a loaded cost of $40 an hour comes to 10 × $40 × 52 = $20,800 a year, for one person doing one repetitive process. Your numbers will differ.
Then we ask three questions:
Three yeses, and you've probably found a good agent candidate. Two or more nos, and you should fix the process first. An agent will only automate the mess.
This isn't about replacing the person. It's about giving them those hours back for work that needs judgment. Results depend on the process, the data quality, the integrations and how much review the work requires.
If you can't write down what the agent does, you can't build it. Every agent we implement starts with a seven-line job description:
| Line | What It Answers |
|---|---|
| Role | What is this agent responsible for? |
| Inputs | Where does its information come from? |
| Tools | Which systems can it read? Which can it change? |
| Rules | What must it never do without a human? |
| Done | What does a finished task look like? |
| Escalation | When does it stop and ask a person? |
| Scorecard | How do we know it's working? |
Most companies skip these seven lines and go straight to picking a tool. Then they wonder why the agent "hallucinates," goes off script, or quietly does nothing. The agent usually wasn't the problem. The missing job description was.
Here's what the seven lines can look like for an invoice-matching agent (illustrative):
We don't publish a separate price list for AI agents, because the cost depends on the job, the systems involved and how much review the work needs. See our published pricing for custom software, or book a scoping call and we'll give you a fixed price for your first agent. Every engagement is fixed price under its own statement of work.
| Good First Agent | Bad First Agent |
|---|---|
| One defined task with a clear "done" | "Run sales" or "handle customer service" |
| Inputs already digital and in one or two systems | Information lives in people's heads |
| Output a person can check in seconds | Output nobody can verify |
| Mistakes are cheap and caught in review | Mistakes go straight to customers or money |
| Happens daily or weekly | Happens twice a year |
These are running inside real businesses, not sitting in a demo environment.
An AI bidding agent built and integrated directly into the Zoho environment the business already runs on.
A production AI portal giving students and administrators controlled access to AI. All 500 users have since been onboarded.
A client-facing portal built and delivered for a financial services firm with a 350-client base.
An AI agent solution designed and delivered against HIPAA requirements.
Selected recent engagements. Client names withheld. Timelines reflect these specific projects and are not a guarantee for every scope.
Ordinary automation follows fixed rules: if this, then that. An AI agent can handle inputs that vary, like reading an invoice in any layout or understanding an email request, and then take defined actions in your systems. We often combine the two. Rules handle the predictable steps, and the agent handles the parts that need reading and judgment.
One repetitive job, with inputs that are already digital and output a person can check quickly. If you can write the seven-line job description (role, inputs, tools, rules, done, escalation, scorecard), it's probably a good candidate.
We give it the narrowest permissions that get the job done, write down what it must never do without a human, log its actions, and have a person review its output until the scorecard shows it can be trusted. High-impact actions keep a human approval step permanently.
That's not the goal. The goal is to take the repetitive part of a job off someone's plate so they can spend those hours on work that needs judgment.
Usually, yes. We build agents into existing tools such as CRMs, inboxes and databases, or into a custom app. For example, the paving contractor's bidding agent is a button inside their Zoho CRM.
Multiply the hours per week the task takes by the loaded hourly cost and by 52. As an example, 10 hours a week at $40 an hour is $20,800 a year. Then check whether the task is repeatable, digital and easy to verify.
Tell us the process: what comes in, what your team does with it, and what "done" looks like. We'll tell you whether it's a good first job for an agent, what the math looks like, and what we'd build.