Blog
Field notes on building AI agents that run in production: what they automate, how they connect to a CRM, where they break, and what it takes to keep one reliable.
An AI agent is not a chatbot with a better prompt. It is a system that holds context, decides what to do next, calls your tools, and finishes a job. Here is what that means in practice, where it pays, and where it quietly fails.
11 min read
A voice agent that sounds good in a demo and one that survives a real inbound queue are different builds. A look inside the stack — latency budget, interruptions, tool calls mid-call, and the handoff that keeps customers.
10 min read
Speed to lead is the metric everyone cites and almost nobody hits. An AI sales agent responds in seconds, qualifies against real criteria, books the meeting and writes the record — and knows which leads to leave alone.
9 min read
Deflection is a metric that flatters the company and irritates the customer. The agents worth building take the action the ticket was asking for — and know precisely when to stop and fetch a person.
10 min read
Most of what a company calls AI work is really integration work. Where agents beat traditional automation, where they lose to it, and how to connect one to your systems without handing it the keys to everything.
9 min read
Retrieval is where most AI projects quietly fail. Not because the idea is hard, but because company knowledge is messy, contradictory and out of date — and no amount of embedding fixes a document nobody has updated since 2023.
11 min read
Reading about it only goes so far. Bring us a workflow and we will tell you whether an agent is the right tool for it — including when the honest answer is no.