AI agents with a human hand-off, MCP servers, workflow automation and browser extensions
01 / The problem
What it looks like from your side
Your team spends hours on repetitive replies and data entry, and the queue grows faster than you can hire.
A good AI agent is mostly plumbing and rules. The model writes the reply; the work is in what it's allowed to do, what it can see, and when it hands the conversation to a person.
What I build:
Customer-support agents. Agents that answer questions, look up orders, place orders as pending for staff review, and hand off to a human with a ticket when they should.
MCP servers. Connectors that let AI tools read and act on your own systems (orders, stock, CRM) with the permissions you choose.
Workflow automation. n8n or custom workers that move data between systems and trigger the next step without a person in the middle.
Browser extensions. Small Chrome extensions that put the right data and actions inside the tools your team already uses.
Every agent starts with a hand-off rule. Anything about money, a complaint or an unclear request goes to a person, with the full conversation attached.
02 / Signs
Signs you need this
03 / Method
What I do, step by step
01List the repetitive work and pick the jobs worth automating
02Decide what the agent may do alone and what always goes to a person
03Connect it to your systems through APIs or an MCP server
04Test it on real past conversations before it talks to customers
05Launch with logging and a weekly review of what it handled
04 / Deliverables
What you get
A working agent or automation in your accounts
Clear hand-off rules and an escalation path
Logs and a short weekly report
The number I report at the end
First response time, share of conversations handled, hours saved per week
Typical timeline: 2–4 weeks for a first agent or workflow