Agent Build
Purpose-built AI agents that handle specific tasks autonomously: intake processing, document generation, lead qualification. No hype, no shortcuts.
The myth
The pitch you hear is that you can spin up an agent and let it run wild on your workload. The reality is that an AI agent is an intricate web of code, platform authorizations, and carefully mapped logic that needs to be built with precision.
Workflows have to be recorded, mapped to specific functions, and tracked for valid execution at every step. It is time-consuming and detailed work, even with modern assisted software development. Most people who try to build agents themselves hit a wall fast and abandon the project.
The reality
A properly built agent takes a defined category of work off your plate entirely. Intake processing. Document generation. Lead qualification. Routing decisions. Follow-up sequences. The tasks that follow predictable logic but eat hours every week because a person is still doing them manually.
To see what a fully deployed AI agent looks like in practice, check out My Digital Employee and my appearance on the AI DIY podcast where I walk through the build in detail.
The approach
Every agent I build starts with the workflow, not the technology. What is the task? What are the inputs? What does a correct output look like? What are the edge cases, and what does failure look like? Only once that is fully mapped does the build begin. The result is an agent that operates within a defined scope, executes reliably, and does not require babysitting.
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No subscriptions. No platform fees. No vendor lock-in.
Every system BottBott builds is yours outright. You own the code, the data, and the deployment. After setup, most systems run for a few dollars a month in API costs. That's it!
