AI Agents and Autonomous Work
Part 3 of The Broker's Guide to AI — the shift from AI that answers to AI that does, and what “autonomous” really means.
From answering to doing
Most people's first experience with AI is a chatbot: you ask, it answers. An agent is the next step — it can actually carry out work: look up a client in your management system, update an address, pull a document, create a renewal activity, draft and queue the follow-up email.
The difference in one line: a chatbot tells you how to process the change. An agent processes the change.
How an agent actually works
Behind the scenes, an agent runs a simple loop, over and over:
- Understand the goal. “Update Mrs. Chen's mailing address and confirm with her by text.”
- Make a plan. Find the client record, update the address field, generate the confirmation, send it.
- Use tools. Tools are the agent's hands — connections into your broker management system, your email, your document storage. Without tools, an agent is just a chatbot with ambition.
- Check the result. Did the update succeed? Did the system return an error? Adjust and retry if needed.
- Report back. Log what was done and tell the human what happened.
It's the same loop a good CSR runs in their head a hundred times a day. The agent just runs it in software.
Tools and connections: where MCP fits
For an agent to do real work, it has to connect to your actual systems. Historically, every connection was a custom build, which made everything slow and expensive.
MCP — the Model Context Protocol — is a new industry standard that fixes this. Picture a universal power adapter: instead of every AI tool needing a custom cable for every system, MCP is one standard plug that lets AI connect to many tools the same way.
What “autonomous” actually means
“Autonomous” is one of the most abused words in AI sales pitches. It does not mean the AI does whatever it wants. It means the AI can complete a defined task from start to finish without a human touching each step. A useful way to think about it is levels of autonomy, similar to how the auto industry talks about self-driving:
- Level 0 — manual: a human does everything; AI isn't involved.
- Level 1 — assisted: AI drafts, suggests, or summarizes; a human does the task. (AI drafts a renewal email, the broker edits and sends it.)
- Level 2 — supervised: AI does the task, a human approves before anything takes effect. (AI prepares the full address change, the CSR clicks approve.)
- Level 3 — autonomous with oversight: AI completes routine tasks on its own, humans review logs and handle exceptions. (AI texts a client their proof of insurance when they ask, and flags anything unusual.)
- Level 4 — fully autonomous: AI handles an entire workflow with no human review. In insurance, reserve this for the lowest-risk, most repetitive tasks, if used at all.
Which tasks should run autonomously — and which never should
A simple rule of thumb: autonomy should rise as risk falls. Good candidates for high autonomy include retrieving documents a client requested, sending payment reminders, updating contact details the client provided in writing, logging activities, and preparing renewal summaries.
Tasks that should keep a human in control: anything involving coverage advice, binding or amending coverage, declining a request, handling a complaint, anything touching a claim, and any conversation where the client is upset or confused. The pattern: AI runs the routine, repetitive, low-judgment work; humans own anything involving advice, judgment, money, or emotion.