Why insurance agencies need a secure AI foundation before they put agents to work.
For years, insurance agencies have looked at automation as the answer to repetitive work. That led many agencies toward bots, workflow automation, and robotic process automation, or RPA.
Those tools have a place. But they are not the future operating model for agencies. A revolution in agentic AI has already begun, and it is about to accelerate quickly. The important question now is what these new agents can do, how they differ from the automation tools agencies have used in the past, and what agencies should be doing to prepare.
In this article, I am going to use one simple distinction. When I say bots, I am grouping together traditional RPA bots and step-based workflow automations. When I say agents, I mean agentic AI systems that can follow a goal and decide how to move the work forward.
- Bots automate steps for defined processes.
- Agents follow goals and coordinate outcomes.
- Humans oversee, approve, and advise.
That is the model agencies should be preparing for.
Bots Still Matter, But They Are Limited
Bots can include workflow and RPA-style tools such as Zapier, Make, n8n, Microsoft Power Automate, UiPath, and other systems that connect applications, move data, trigger actions, or repeat a predefined process.
They can send notifications, update records, transfer information, trigger follow-ups, or carry out a stable series of steps. Used selectively, they can save real time.
But they are usually technical to set up. They take planning, testing, tweaking, and ongoing maintenance. They work best when the process is clear, the rules are stable, and the systems do not change.
That is not always how insurance works.
One small change to a carrier portal, form, field name, button location, login process, user interface, or internal workflow can break the automation. Now imagine an agency with dozens of these bots running across service, sales, claims, renewals, downloads, certificates, and follow-ups. That can create a lot of care and feeding.
I do not recommend that the average agency try to automate itself with RPA.
Bots will still have a role. They should be a limited execution layer inside a broader operating model, not the agency’s entire automation strategy.
Agentic AI Is Different
Agentic AI is not just another automation step. An agent can review context, understand a goal, decide what should happen next, call the right tool, monitor progress, check its work, and escalate when a human needs to review, approve, or advise.
That tool might be a bot. It might be a workflow automation. It might be a virtual employee. It might be an email draft, a browser action, a task in the agency management system, or a human review step.
The agent becomes the coordinator.
In other words, the bot is not the brain. The bot is a tool the agent can use.
That distinction matters in insurance because agency work is not always clean or predictable. A renewal is not just a reminder. A claim is not just a status check. A quote is not just a form. Each can involve missing information, carrier differences, timing, client nuance, compliance, and coverage judgment.
That is where agentic AI becomes more valuable than traditional automation.
What Agentic AI Looks Like Today
Agentic AI is already showing up in several forms, and the lines between the categories are moving quickly.
Microsoft Copilot Cowork goes beyond chat by taking a requested outcome, building a plan, working across Microsoft 365, and presenting checkpoints so the user can review or approve actions. Microsoft developed it with technology from Anthropic’s Claude Cowork.
Anthropic Claude Cowork also lets users delegate complete, multi-step work across files, connected tools, and the browser, then review the finished documents, spreadsheets, presentations, or other results.
OpenAI Codex has moved well beyond the old idea of a coding helper. It can operate a computer, work across apps and files, use plugins, schedule continuing work, retain useful context, and run multiple agents in parallel. Coding remains a major strength, but its operating model increasingly applies to broader knowledge work.
Microsoft Scout is one of the clearest signals of where this is heading. Scout is an always-on personal agent built on OpenClaw technology. It can act across local files, the browser, Microsoft 365, and other approved tools, with granular permissions and human approval before sensitive actions. Scout remains a Frontier preview, so it is not broadly available to everyone yet.
There are also persistent agent platforms such as OpenClaw, Hermes, and other custom or third-party systems. They can run on a workstation, server, virtual machine, hosted machine, or cloud environment. There can be many of them, each assigned to a role, workflow, set of tools, security boundary, and escalation path.
Many mainstream agent tools still enter the business through an individual user’s account or workspace, even as they become more autonomous and cloud-based. Persistent, self-hosted platforms make it easier to deploy a fleet of always-available agents across different machines and functions. That distinction is not permanent, but it matters today.
The future is not simply one person using one AI chat window. The future is a structured AI operating layer across the agency.
A Word of Caution
These new agent systems are powerful, but they are still new.
Whether we are talking about Scout, OpenClaw, Hermes, Codex, Claude Cowork, Copilot Cowork, or another emerging platform, this category is still developing. In many ways, these may be the least mature versions we will ever see. Reliability, performance, capability, integrations, administration, and security are improving rapidly.
That is not a reason to ignore them. It is a reason to prepare correctly.
Agencies should expect rapid improvement, but they should also expect mistakes, integration limits, changing features, and occasional failures. Agents may misunderstand instructions, use the wrong tool, need more context, or require human intervention before they can safely continue.
The right approach is not to hand an agent broad control of the agency. Give it controlled access, limited authority, clear instructions, defined workflows, strong security, and explicit human approval points.
Start small. Test one workflow. Measure the results. Improve the process. Then expand.
Onboard Agents Like New Employees
Agencies should think about agentic AI the same way they would think about bringing a new employee into the business.
You would not hire a new person on Monday, hand them keys to every system, and let them make coverage decisions by Friday. The same rule should apply to AI agents.
Start with zero trust. The agent should earn additional trust and authority over time.
Give it one job. Give it limited access. Give it clear instructions. Train it on documented workflows. Show it examples of good work and bad work. Watch how it performs. Correct it. Improve the instructions. Then slowly expand what it is allowed to do.
Documentation matters. If the agency’s workflows only live in someone’s head, an agent cannot reliably learn them. Tools such as Scribe and Loom can record a process, capture the steps, and turn the work into training material, standard operating procedures, and examples that both people and agents can follow.
A good agent should know:
- What task it is responsible for.
- What data it can access.
- What systems and tools it can use.
- What it is allowed to change.
- What examples and standards it should follow.
- What it should never do.
- When it must stop and ask for help.
- When it must escalate.
- Who reviews and approves the work.
That is how trust should be built: one workflow at a time, one permission at a time, and one successful result at a time.
How do you teach an agent all of this – and make sure the same knowledge, rules, and guardrails are shared across all of your agents? That is a subject worthy of its own article, and I will cover it soon.
The goal is not to let agents run wild. Train them inside a controlled operating system with good guardrails and humans in the loop supervising, correcting, improving, approving, and advising.
The Roadmap Comes First
Agencies should not start by buying agents. They should start by preparing the agency.
That is why we created the AI Foundation Roadmap. It defines five levels of agency AI maturity and provides a practical path from early experimentation to secure, structured implementation.
AI maturity is a useful term because it covers both readiness and actual implementation. An agency may be ready in some areas but still have very little AI deployed. Another agency may have several tools in use but lack the security, documentation, training, and governance needed to use them well.
The foundation matters regardless of which technology the agency adopts. It applies whether the agency starts with a chatbot, Microsoft Copilot, workflow automation, bots, virtual employees, or fully agentic AI systems.
The Roadmap addresses the pieces that must come together:
- Organized, usable data and clearly defined data silos.
- Correct user rights, security controls, and compliance safeguards.
- Documented workflows, task categories, and escalation rules.
- AI policies, governance, and human approval requirements.
- Staff education and training for working safely with AI.
- Prioritized use cases, implementation planning, measurement, and phased expansion.
Without that structure, AI will not fix the agency. It will only move the confusion faster.
Agencies need to know what an agent can see, what it can touch, what it can change, what it can recommend, and when it must stop. Agents should not have broad access to everything. They should have controlled access to the right information, for the right task, under the right permissions, with a clear record of what happened.
The winning agencies will not be the ones with the most bots or the flashiest AI tools. They will be the ones with the cleanest structure and the most disciplined implementation.
How Agencies Should Prepare
The work can be organized into three practical phases.
First, stabilize. Identify recurring tasks. Review how work enters the agency. Clean up obvious data problems. Review user rights and security. Document the work that is currently hidden in inboxes, spreadsheets, sticky notes, and individual memory.
Next, structure. Build task buckets and routing rules. Decide which work belongs with licensed staff, virtual employees, bots, or agents. Establish approval points. Train the staff. Create the policies and documentation agents will need.
Then, scale. Add AI tools, bots, and agents in controlled phases. Start with one or two workflows. Measure the impact. Watch the exceptions. Improve the process. Then expand.
Good starting points include renewal preparation, claims-status tracking, quote follow-up, service intake, certificate workflows, onboarding, and cross-sell identification.
A renewal-preparation agent can identify upcoming renewals, gather needed information, draft outreach, and create a review task.
A claims-status agent can monitor open claims, detect stale updates, prepare follow-up, and escalate when a human needs to get involved.
A sales-follow-up agent can watch quote activity, identify prospects who have gone quiet, draft the next message, and alert the producer when the conversation needs a human touch.
An intake agent can classify incoming requests and route them to the right handler before they sit in an inbox.
That is the real shift. The future agency is not built around people doing every task or bots trying to automate every click. It is built around orchestration.
The Agency Workforce Is Becoming Human + Agent Teams
I believe we are moving into an age in which many knowledge workers will have an agentic AI assistant working beside them. Some employees may eventually direct several specialized agents.
That does not automatically mean the agent replaces the employee. It means the employee becomes the leader of a small human-agent team.
An account manager might work with a renewal-preparation agent, a claims-status agent, and a service-intake agent. A producer might direct agents that help with prospect research, quote follow-up, proposal preparation, and cross-sell opportunities.
The agents can gather information, monitor activity, prepare drafts, complete repetitive work, and keep processes moving. The human provides judgment, establishes priorities, corrects mistakes, handles exceptions, approves sensitive actions, and remains accountable for the result.
Employees will need to learn a new management skill: how to direct, supervise, correct, and improve the agents working with them. At first, the human should closely watch everything the agent is doing. Over time, oversight can become more risk-based, but accountability must remain human.
The future agency employee may not work alone. They may lead one or more AI assistants that help them manage more work while preserving the human judgment, coverage knowledge, and client relationships that matter most.
Humans Still Own the Judgment
Bots will still matter for narrow execution. Workflow tools will still matter for connecting systems. Virtual employees will still matter for monitoring and workflow management. Agents will matter because they can follow goals and coordinate outcomes.
But licensed professionals still matter most where judgment, advice, coverage, relationships, and client trust are involved.
That is the line agencies should hold.
Agents can initiate. They can monitor. They can summarize. They can route. They can call tools and prepare recommendations.
But humans oversee, approve, advise, and remain accountable.
That is the human-in-the-loop model agencies should prepare for now.
Use the free Agency AI ROI Impact Calculator to estimate what the next level of AI maturity could mean for your agency.
Start With the Roadmap. Then Measure the Opportunity.
The next step is not buying another AI tool. It is figuring out where your agency is today, where you want it to go, and what needs to be in place to get there.
The AI Foundation Roadmap defines five levels of AI maturity and gives agencies a phased path forward – including data, security, staff training, documented workflows, governance, human oversight, and implementation planning – so AI can be adopted securely, compliantly, and successfully.
Agency owners can then use the free Agency AI ROI Impact Calculator to see what moving to a higher maturity level could mean for the agency. Enter your current level, desired level, and basic agency information, and the calculator estimates added capacity, how far the agency may be able to grow before adding employees, and the additional annual premium that capacity could support.
These are planning estimates, not guarantees, but they are based on substantial insurance-industry data and documented assumptions explained in the calculator report.
Agentic AI is still new and improving rapidly. Agencies do not need to wait for it to become perfect. They need to prepare now: get the foundation in order, train their people, establish guardrails, and begin with carefully supervised use cases.
Your next agency employee might be an AI agent.
The question is whether your agency will be ready to onboard it, train it, supervise it, and put it to work successfully.
If you’d like to talk through your agency’s AI use – or lack of it – or have questions about where to start, what to secure, or what to implement next, feel free to schedule a quick call with me. I’m happy to help.
Source Links and Supporting Material
All external links referenced in the article are collected here. Product availability and capabilities can change quickly, especially for preview agent systems.
- Agency AI ROI Impact Calculator — Free planning calculator for agency AI maturity, capacity, staffing, premium growth, and projected impact.
- Microsoft Learn — Microsoft Scout overview — Scout capabilities, permissions, autonomous modes, sensitive-action approvals, and Frontier preview status.
- Microsoft Command Line — OpenClaw goes enterprise with Microsoft Scout — Microsoft account of Scout being powered by OpenClaw technology with enterprise identity, governance, and security.
- Microsoft — Copilot Cowork: A new way of getting work done — Copilot Cowork planning, background execution, checkpoints, Microsoft 365 context, and Claude Cowork technology.
- Anthropic — Claude Cowork — Anthropic’s agentic workspace for delegating multi-step tasks across files, tools, and devices.
- OpenAI — Codex for (almost) everything — Codex computer use, apps, plugins, multiple agents, memory, automations, and work beyond coding.
- OpenClaw — Personal AI Assistant — Open-source agent platform that can run on infrastructure selected by the user.
- OpenClaw Documentation — Self-hosted gateway, supported channels, deployment, and operating model.
- Nous Research — Hermes Agent — Open-source, self-hosted agent with persistent memory, skills, messaging gateways, and deployment options.
- Scribe — Workflow context for teams and AI agents — Process capture, SOPs, onboarding, and workflow context for people and AI agents.
- Loom AI — Video-based process capture and AI workflows that can produce SOPs and step-by-step documentation.
- Redpanda — Introducing the Agentic Data Plane — Governed access, context, security controls, observability, and auditability for agentic systems.
- Zapier Agents — Workflow automation and AI-agent platform example.
- Make — Visual workflow automation and integration platform example.
- n8n AI — Workflow automation platform with AI agents, integrations, controls, and human approvals.
- Microsoft Power Automate — Microsoft workflow automation and RPA platform example.
- UiPath — Robotic Process Automation — traditional enterprise RPA platform and RPA overview.