AI agent development: beyond if-then, automation that can decide.

Agentic automation uses AI agents — software that can reason about a goal — instead of fixed if-then rules. A rule-based workflow breaks when reality deviates from the diagram; an agentic AI system reads the context, decides the next step, and escalates to a human when it is unsure. We build agentic automation on your existing stack, with guardrails, logging, and approval gates around anything irreversible.

Workflow tools follow rigid if-then rules and chatbots follow scripts. Agents reason: they handle unstructured inputs, plan multi-step work, recover from errors, and decide when to escalate. Use workflows for pure data routing; use agents where judgment matters.

Your chosen model — Claude, GPT, Gemini, or a local model you self-host — chosen per use case. BYOK pricing means you create your own API accounts and pay providers directly at published rates. We do not mark up AI costs.

A typical build starts at $7,500 one time, with ongoing costs limited to your model provider — usually $30 to $150 a month. Compare that against the 60 to 80 percent cost reduction most clients see in the processes the agent takes over.

Guardrails, allowed-action lists, and approval gates. V1 agents typically get read-and-create permissions only; destructive or high-stakes actions route through human approval. Every action is logged and auditable.

Live in 30 days for most builds, including integrations, guardrails, and escalation rules. We follow Discover, Design, Build, Launch, Optimize — and you own the system from day one.

Broadly: reactive agents that respond to a trigger with a decision, task agents that pursue a defined goal through several steps, and multi-agent systems where narrow specialists hand work to each other. For a small business the useful distinction is narrower — an intake agent that reads and routes, a research agent that gathers, a drafting agent that writes, and a human who approves before anything leaves the building.

The main one is an agent doing exactly what it was told by something it read — an email, a document, a web page — and treating that text as an instruction. So: give it the narrowest access that does the job, put an approval gate before anything irreversible, log every action, and treat everything it reads as data rather than commands. Design for the day it is wrong.

They handle the steps that need judgment across changing inputs, which rules cannot — reading a messy request and deciding where it goes, comparing a document against a scope and flagging what is missing. That is the work that used to require a person in the loop for every instance. The benefit is not replacing the person; it is that the person now sees only the exceptions.