---
title: "AI Agent Development: Hand Repetitive Workflows to AI | TauX"
description: "TauX builds AI agents for SMEs: repetitive, rule-based workflows run by AI that calls tools, follows the steps, keeps a record, and stops where a person must decide."
url: "https://taux.io/en-US/ai-agents"
locale: "en-US"
alternates:
  ja-JP: "https://taux.io/ja-JP/ai-agents"
  ko-KR: "https://taux.io/ko-KR/ai-agents"
  zh-Hans-CN: "https://taux.io/zh-Hans-CN/ai-agents"
  zh-Hant-TW: "https://taux.io/zh-Hant-TW/ai-agents"
---

# AI agent development

Hand a repetitive, rule-based workflow to an AI agent that calls tools, works through the steps and keeps a record. Where a person needs to make the call, it stops.

01

## Who it’s for

*   Your processes are fixed but have many steps, such as compiling quotes, checking orders or assembling reports
*   You’ve tried a chatbot, but what you need is something that does the work, not just answers
*   Work involves copying and pasting back and forth between several systems
*   You want to try one workflow first and expand once it works

Trigger

Such as a new order or quote request

AI agent

Follows the steps, calls tools, logs

When judgment is needed

Stops and hands off to a person

Result

A document, system update or notice

Rule-based steps go to the AI agent; a person decides wherever judgment is needed, and every action is logged.

02

## What we do

### Process analysis

We break down each step of the current process and find the points that need human judgment and can’t be handed to AI.

### Agent design

We define its role, the tools it may use, when it stops, and when it hands back to a person.

### Development and tool integration

Built on models such as Claude and Gemini, with tools provided over MCP, so switching models later doesn’t mean rewriting the integrations.

### Testing and evaluation

We test accuracy on real cases and list the known failure modes and how each is handled.

### Launch and monitoring

Every action is logged, the person responsible is notified when something goes wrong, and results are reviewed regularly.

03

## How it works

### 1\. Process analysis

Pick one workflow and record how it’s done today, how long it takes and where mistakes happen most.

### 2\. Design and spec

Write down what the agent does, what it doesn’t, and when it stops.

### 3\. Build and test

Build it and test it repeatedly against real cases.

### 4\. Pilot and launch

Let a few staff use it first, then launch it and hand over how it is monitored.

04

## Deliverables

*   Process analysis and agent design document
*   Agent code, prompts and tool configuration
*   Test cases and evaluation results
*   Launch, logging and alert configuration
*   Maintenance documentation

05

## Engagement cycle

Each cycle runs three months, six months or a year, depending on scope. At the end of each cycle we sit down with you and compare the results against the goals set at the start, then decide what the next cycle should cover, or whether to stop there.

Every cycle: audit → design → implement → check against the goals, then decide what's next

06

## Pricing

Each engagement is estimated on its own: how many systems and how much data are involved, the people and time needed, and how long the cycle runs. Talk to us first and we’ll give you a number based on the actual scope, rather than quoting a price and then fitting the scope to it.

07

## Common questions

### How is an AI agent different from a chatbot?

A chatbot answers questions. An AI agent carries out work step by step, such as looking up data, calling a system or producing a document, and stops where a person needs to decide.

### What if the AI agent gets something wrong?

We decide at design time which steps must be confirmed by a person, and every action is logged. Before launch we test on real cases and list the known failure modes and how each is handled.

### Are we locked into one AI company’s models?

No. We choose the model based on the task, cost and your data rules. Tools are provided over MCP, so switching models later doesn’t mean rewriting the integrations.

08

## Further reading

*   [An agent development workflow](https://taux.io/en-US/agent-dev-workflow)
*   [A practical guide to building Claude Skills](https://taux.io/en-US/claude-skills-guide)
*   [ADK Skill Patterns](https://taux.io/en-US/adk-skill-patterns)
*   [Prompting Guide](https://taux.io/en-US/agent-prompting-guide)

## Tell us where you are

Email us about where you’re stuck, and we’ll reply with what could work and the next step.

[Email us](mailto:hello@taux.io) 

AI agent development

Engagement cycle

3 months, 6 months or a year

Pricing

Estimated per engagement

[Email us](mailto:hello@taux.io)