Independent agencies don’t need “better chat.” They need work handled: prep this renewal, compare the expiring policy to the renewal and flag changes, issue a certificate, process an endorsement, update activities, and keep the agency management system (AMS) clean—without your team living in five systems all day.
Recently, the touted glue has been RPA and workflow automations.
RPA (Robotic Process Automation) is software that follows a predefined script to automate repetitive, rule-based steps—often by mimicking what a human does on-screen (click, copy/paste, enter data) across apps. (UiPath)
Agentic AI is different. You give it an outcome (“get this renewal ready”), and it can plan the steps, choose tools, handle messy inputs, and increasingly operate software through the screen—not just through perfect APIs. OpenAI’s Operator is explicitly built on a “Computer-Using Agent” trained to interact with GUIs (buttons, menus, text fields). (OpenAI)
One point I want to be very clear about: agentic agents aren’t here to replace your employees. In well-run agencies, they become a force multiplier—drafting, triaging, logging, and teeing up decisions so producers and CSRs spend time on judgment calls and client relationships, not copy/paste work. OpenAI’s own positioning of Frontier leans into “AI coworkers” operating with shared context and boundaries. (OpenAI). Keeping humans in the loop.
Why the timing is different now
Gartner predicts up to 40% of enterprise applications will include integrated, task-specific AI agents by end of 2026. (Gartner) But Gartner also predicts over 40% of agentic AI projects will be canceled by end of 2027 due to cost, unclear business value, or weak risk controls. (Gartner)
Translation for agency owners: this is real, it’s moving fast, and the winners will be the agencies that deploy it with permissions, auditability, and guardrails—not the ones that “turn it on everywhere.”
The biggest practical shift: agents can live inside your environment
This is the second major difference vs classic RPA and it’s exactly what you’ve been getting at:
RPA usually lives as a scripted workflow that breaks when screens change.
Agentic AI is increasingly capable of living inside your environment—inside your network and even on user workstations—and helping staff do real processes by reading the screen, taking actions, and learning the way your team works.
You see this direction across the major players:
OpenAI Frontier (Feb 5, 2026): enterprise platform to build, deploy, and manage agents with shared context plus “permissions and boundaries.” (OpenAI)
OpenAI Operator / CUA (Jan 23, 2025): model trained to interact with GUIs. (OpenAI)
Anthropic Cowork: agentic capability inside Claude Desktop (file access and multi-step tasks). (Claude)
Important: Cowork activity is not captured in Audit Logs/Compliance API/Data Exports and Anthropic explicitly says do not use Cowork for regulated workloads right now. (Claude Help Center)
Google Gemini 2.5 Computer Use: a model released for UI interaction (screenshot → action loop) via API. (blog.google)
Project Mariner (DeepMind): Google’s research prototype for “computer use,” with plans to bring those capabilities into the Gemini API. (Google DeepMind)
And then there’s OpenClaw, which matters because it pushed the “self-hosted agent that actually does things” concept into the mainstream. Its creator announced he’s joining OpenAI and that OpenClaw will move to a foundation while staying open. (steipete.me) OpenClaw’s docs also describe multi-agent routing—running multiple isolated agents in one gateway and routing work to the right agent. (OpenClaw)
The flip side (and agencies can’t ignore this): Microsoft’s security team warns that self-hosted agent runtimes combine untrusted inputs with code execution and durable credentials—meaning you need isolation, tightly scoped identity, and monitoring. (Microsoft)
What this means for independent agencies (practical examples)
The near-term win isn’t a “fully autonomous robot agency.” It’s an agent that does the first 70–90% of the work and keeps your system of record clean—logging activities, creating tasks, and (where allowed) updating the AMS so your team isn’t chasing details across inboxes and portals.
Renewal prep: pulls the key account facts, flags premium jumps and notable changes, builds a checklist, drafts insured outreach, and creates AMS activities/tasks (calls, follow-ups, remarket decision, due dates, owner assignments).
Policy comparison: compares expiring vs renewal and highlights material changes (limits, deductibles, endorsements/forms), then posts a clean summary note in the AMS and creates action tasks (discuss changes, obtain acceptance, update schedules).
Endorsement intake: reads the email and attachments, extracts what changed, identifies missing info, drafts the CSR’s questions, and logs the endorsement request + follow-up trail in the AMS. If write access is allowed, it can prefill the endorsement workflow for quick review.
Certificates: extracts holder/wording/dates, drafts the response, logs the request in the AMS, routes for issuance, and then records completion (what was issued, when, and any special wording) so you have a defensible record later.
New business lead handling: normalizes lead data, routes it to the right producer, drafts follow-up, schedules the next step, and creates the prospect/account + activity record (or stages it for approval) so “lead came in” instantly becomes AMS/CRM record + tasks + next action.
2026 Agency Playbook: Get Agent-Ready, Not RPA-Heavy
If you’re an agency owner and you’re being pushed into heavy, custom RPA buildouts, pause. Most of that work is expensive to maintain and gets brittle fast—while agentic AI is rapidly making “goal-driven automation” more practical and more powerful.
The smarter move right now is to get your agency agent-ready: clean data structure, least-privilege access, approval gates, and auditability—then deploy agents with the right security guardrails so they can work alongside your team without creating compliance or E&O exposure. If you want a practical roadmap for that, SMART Services can help.
Author Bio
Jerry Fetty is the Founder of SMART Services and has spent 35+ years helping independent insurance agencies modernize their technology, strengthen cybersecurity, and operate more efficiently. Today, his focus is helping agencies adopt AI the right way, with a secure foundation, clean data structure, clear policies, and real-world training that produces measurable ROI.
References
Gartner press releases on adoption (40% by 2026) and cancellation risk (40% by 2027). (Gartner)
OpenAI Frontier and Operator / Computer-Using Agent (CUA). (OpenAI)
Anthropic Cowork + audit/compliance limitation guidance. (Claude Help Center)
Google Gemini 2.5 Computer Use + Project Mariner. (blog.google)
OpenClaw foundation announcement + multi-agent routing docs; Microsoft guidance for running agent runtimes safely. (steipete.me)