Voice Agents
Agentic AI in Insurance: A 2025 Guide for MGAs and Carriers
Amir Prodensky
CEO
Jul 17, 2025
15 min read
Your next best employee might be an AI agent, here’s what that means for insurance
It’s 9:15 AM and hundreds of calls have already been done for you.. Leads are qualified, quotes are booked, policies are serviced, and your team hasn’t even had coffee yet.
That’s not the future.
That’s agentic AI, and it’s happening right now in insurance.
If you're an MGA or carrier leader, you're probably wondering: Can this actually help us move faster, cut waste, and still manage risk?
Short answer: YES! That’s exactly why we created this guide. In it, you’ll learn:
What agentic AI is (in plain English)
Why it’s different from traditional automation
Where it fits in the insurance workflow
How to get started without overwhelm
What risks to watch for and how to manage them
Let’s dig in and explore how you can put agentic AI to work for your business, step by step.
What is agentic AI for insurance?
Agentic AI is a type of artificial intelligence that can act on its own to achieve goals. It takes initiative, makes decisions, and adapts as it learns.
Think of it as a smart assistant that knows what needs to be done and finds the best way to do it.
So, how is this different from traditional AI? Here is a quick snapshot:
Feature / behavior | Traditional AI | Agentic AI |
Approach | Task-based | Goal-based |
How it works | Follows fixed rules or models | Plans actions, makes decisions, and adapts |
Input required | Needs specific instructions | Needs a goal or desired outcome |
Adaptability | Rigid—doesn’t adjust on its own | Flexible—learns and improves with feedback |
Example use | Flagging potential fraud in claims | Reducing overall claim cycle time from intake to payout |
Human involvement | High—requires setup, monitoring, and tuning | Lower—operates with oversight but takes initiative |
Scope of tasks | Narrow—single-step automation | Broad—multi-step workflows (e.g., intake → routing) |
Response to change | Needs reprogramming or retraining | Adjusts strategy automatically if the situation changes |
Value delivered | Efficiency in one area | Continuous improvement across full workflows |
Best for | Repetitive, rule-based tasks | Dynamic, outcome-driven processes |
Here’s what makes agentic AI special:
It’s autonomous = it works without constant input. Once it knows the objective, it runs with it.
It’s goal-directed = it works toward an outcome, not just complete tasks.
It’s adaptive = it learns from what works and changes course if needed.
Now that you know what agentic AI is, let’s talk about why it matters, especially for MGAs and carriers navigating today’s challenges.
Why should MGAs and carriers care about it?
The insurance industry’s under pressure. Costs are rising. Talent’s tight. Customers expect fast, digital-first service. but legacy systems and manual workflows still slow things down.
Sound familiar?
Agentic AI offers a real chance to break that cycle. It tackles problems end-to-end. That means fewer bottlenecks, smarter decisions, and happier customers.
Here’s what agentic AI can help with:
Efficiency: It handles routine tasks like quote generation, policy updates, and claims triage, fast and around the clock.
Accuracy: It reduces errors by learning from past outcomes and checking its own work.
Customer experience: It speeds up responses, personalizes interactions, and keeps things moving, without dropping the ball.
And the impact’s already real.
Some MGAs are using agentic AI to process bind requests in minutes, not hours. Carriers are deploying it to flag risky claims before they escalate.
In both cases, teams save time, customers get faster answers, and ops run smoother.
Let’s zoom out for a second. Here’s what the journey from manual work to agentic AI actually looks like. You’ve probably already started without realizing it.
You’ve seen the potential. But how does it actually work in the day-to-day insurance world? Let’s break that down.
How does agentic AI work in insurance?
Agentic AI works by taking a goal (like speeding up claims) and figuring out how to get there, step by step. It pulls from data, makes decisions on the fly, and adjusts based on what’s working.
In insurance, that means it jumps into real workflows and gets things moving.
Let’s break it down – here’s a typical agentic AI workflows include:
Taking in a task or goal (e.g., bind a policy, handle a claim)
Analyzing available data (documents, past activity, customer history)
Planning the steps needed
Acting on them, without constant human input
Learning from outcomes and tweaking the approach
To make this even more concrete, here’s a quick snapshot of what agentic AI can actually do in a real MGA or carrier environment:
Workflow area | What Agentic AI can do | Who it helps |
New business intake | Auto-read submissions, classify risks, route to underwriter | MGAs, Underwriters |
Claims | Guide FNOL calls, detect fraud patterns, automate triage | Carriers, Claims Teams |
Customer service | Answer questions via voice/chat, handle policy updates | Brokers, Policyholders |
Compliance | Track regulatory requirements, flag missing docs | Ops, Compliance Teams |
Policy management | Auto-renew low-risk policies, send reminders | Carriers, MGAs |
Voice platforms are a growing part of this. A great example of this in action is Strada’s phone AI agents. They’re built specifically for insurance and can handle policy servicing, renewals, and even lead qualification, all by phone.
That means no hold times, no dropped calls, and conversations that actually move things forward. So what does this actually feel like during a workday? Here’s a side-by-side of life before and after agentic AI.
Understanding the workflows is one thing, but what real problems can agentic AI help fix? Turns out, quite a few.
What problems can agentic AI in insurance help solve?
Agentic AI solves real problems that slow MGAs and carriers down every day. If you’re stuck juggling systems, chasing data, or trying to keep up with growing workloads, this is where agentic AI can make a real difference.
Here’s a simple map of common insurance challenges and how agentic AI can step in to fix them.
Problem area | Common pain point | What Agentic AI can do |
Customer engagement | Slow replies, dropped messages | Respond instantly via email/chat/phone |
Underwriting | Manual review, missing data | Pre-screen, flag gaps, smart routing |
Data silos | Info scattered across systems | Pull, connect, and organize data |
Risk assessment | Complex patterns, changing inputs | Spot trends, adapt to new signals |
Compliance & reporting | Manual tracking, risk of errors | Auto-checks, generate audit-ready reports |
And let’s walk through a few of the big pain points.
1. Customer engagement gaps
Customers want answers fast, no matter if they’re shopping for coverage, updating a policy, or filing a claim. But slow email replies, long phone wait times, or missed follow-ups can leave them frustrated.
Agentic AI can:
Answer calls and emails instantly
Handle certificate requests, payment questions, and more
Stay available 24/7 with no hold time
Follow up automatically if something’s missing
Phone-based agents trained on insurance conversations now regularly support tasks like quote requests or certificate delivery, making engagement faster and easier.
Platforms like Strada make this possible by using AI that understands policy terms, coverage questions, and next steps, without needing a human to jump in. For example, an AI voice agent can confirm a VIN, send a certificate, and log the interaction in your system, all in one call.
It helps you show up for customers even when your team’s swamped—and keeps service moving around the clock.
2. Underwriting delays
Underwriters often waste time digging through documents, chasing missing info, or sorting submissions.
Agentic AI can:
Review applications and flag missing data
Classify submissions by complexity
Auto-route tasks to the right underwriter
Even pre-fill forms using past data
This cuts turnaround times and keeps business moving.
3. Data silos
Your data’s everywhere: CRM, email, PDFs, policy systems. That makes it hard to see the full picture.
Agentic AI can connect the dots. It pulls from multiple sources, organizes what it finds, and presents insights clearly. So whether you're pricing a risk or analyzing trends, you get the full story without the hunt.
Platforms like Strada help by connecting directly to major insurance systems – policy admin tools, CRMs, document storage, and more. That means no more jumping between platforms or losing time copying data.
Everything stays in sync, and your AI agents always have what they need to act with context.
4. Risk assessment complexity
Assessing risk takes time and judgment. But with so much data coming in (from past claims to third-party sources), it’s easy to miss something.
Agentic AI can:
Spot patterns across policies and claims
Recommend actions based on historical outcomes
Adjust in real time if new risks emerge
It won’t replace human judgment, but it gives your team a smarter starting point.
5. Compliance and reporting
Regulatory reporting is essential, but also time-consuming and detail-heavy.
Agentic AI can:
Track call content for compliance
Spot missing documents or incomplete data
Generate call logs and summaries
Keep audit-ready records without manual work
Some platforms, like Strada, also check the accuracy of every AI response. That helps reduce mistakes and keeps you audit-ready without extra overhead.
In short, agentic AI is all about doing things better. You’ll close gaps, reduce delays, and give your teams more time to focus on high-value work.
Of course, no tool is perfect. Let’s take a look at the key risks and how to manage them smartly.
What are the risks and limitations?
Agentic AI has big potential, but it’s not plug-and-play magic. Like any powerful tool, it comes with risks and trade-offs. Knowing what to watch for helps you use it wisely.
Here are a few areas to keep in mind:
1. Regulatory and ethical considerations
Insurance is a tightly regulated industry. If your AI makes decisions that impact coverage or pricing, you need to ensure those decisions follow the rules and treat people fairly.
That means you’ll need:
Clear guardrails
Human oversight for sensitive actions
Ongoing monitoring to catch bias or drift
You don’t want your AI unintentionally denying coverage or skirting compliance.
2. Explainability and transparency
Agentic AI can feel like a black box. If it flags a claim or declines a quote, people will ask, “Why?”
That’s why explainability matters. Make sure your AI tools can show their reasoning, especially for anything customer-facing or regulator-facing.
Strada helps with this by logging every step of the interaction and providing clear summaries of what the AI did and why. That transparency builds trust, with both your team and your customers.
3. Integration with legacy systems
Let’s be honest: most insurance tech stacks weren’t built for this. Connecting agentic AI to old platforms can be tricky and time-consuming.
Start small. Focus on use cases where data is clean and access is easy. Prove value, then expand.
4. Risk of over-reliance
Agentic AI is smart, but it’s not perfect. Don’t let your team get too hands-off. Keep people in the loop, especially for decisions that carry risk or impact customer trust.
Used well, agentic AI becomes a powerful teammate. Just remember: like any teammate, it needs direction, oversight, and the right tools to thrive. Here's how to take your first steps with confidence.
How to get started with AI insurance agent
Ready to bring agentic AI into your business? You don’t need to overhaul everything at once. Start small, prove value fast, and grow from there.
Here’s how to get started, step by step.
1. Assess your readiness
Before you dive in, take a quick pulse check. Ask yourself:
Do we have clear business goals AI can support?
Is our data clean, accessible, and organized?
Do we have the internal support (IT, ops, compliance) to pilot something new?
You don’t need everything perfect. But you do need a solid foundation and buy-in from leadership.
2. Identify high-impact use cases
Next, find pain points that slow your team down or frustrate customers. Look for areas with repeatable tasks, clear rules, or too many manual handoffs.
Some common early wins:
Auto-responding to broker emails
Pre-screening submissions
Scheduling follow-ups
Flagging incomplete applications
You’re looking for “low lift, high impact” opportunities – the stuff that doesn’t require deep system changes but delivers clear value.
3. Crawl → walk → run
Don’t try to automate everything on day one. Start with one use case. Then grow.
Many teams start small with a Strada use case like answering inbound service calls. There’s no engineering lift, and since it integrates with core systems, it fits neatly into existing workflows.
Use this crawl-walk-run framework to pick starting points that deliver value without getting stuck in complexity.
Stage | Sample use case | Why it’s a good start |
Crawl | Auto-respond to broker emails | Easy to set up, shows instant time savings |
Walk | Pre-fill underwriting forms from submissions | Cuts manual entry, improves accuracy |
Run | Voice AI by Strada for FNOL and claims routing. | High impact, but needs clean data + integration |
Each stage builds confidence and helps your team learn what works.
4. Build vs. buy
You’ve got options.
Buy: Pre-built agentic AI tools (especially insurance-focused ones, like Strada) can get you started quickly. Look for solutions that integrate with your systems and allow customization.
Build: If you have strong tech talent and unique needs, you might want to develop your own agents. This gives you full control, but takes more time and resources.
Most MGAs and carriers start by buying, then layer in custom features later. If you’re looking to start fast, Strada’s platform is insurance-specific and comes with pre-built use cases like renewals and policy updates – no custom development needed.
5. Invest in data quality
Agentic AI is only as good as the data it sees. Messy, incomplete, or siloed data will slow you down, or worse – lead to bad decisions.
Make sure your key systems (policy, claims, CRM) are synced. Clean up old records. And ensure your agents have access to the right data at the right time.
Think of it like fuel. Better data means smoother, smarter performance.
[bonus tip] Start with the business, not the tech
You’re not “doing AI” for the sake of it. You’re solving business problems: faster quotes, better service, lower costs.
So frame every project around outcomes, not just features. That keeps the focus where it belongs. Once you’ve started, how do you know it’s working?
Let’s explore what successful adoption really looks like.
What does successful adoption look like?
Rolling out agentic AI is one thing. Making it work long-term is another. Success is about seeing real, measurable results and getting your team fully on board.
Want to know if it’s working? These are the metrics you’ll want to keep an eye on as you scale agentic AI.
Area | What to measure | Why it matters |
Efficiency | Time saved per task/process | Shows real productivity gains |
Speed | Cycle times for quotes, claims, etc. | Indicates faster service delivery |
Accuracy | Error rate in forms, decisions, responses | Reflects improved data handling |
Adoption | % of team actively using AI tools | Helps track buy-in and ROI |
Customer satisfaction | NPS, response time, issue resolution rate | Connects AI use to real-world experience |
Pick a few key metrics, set a baseline, and track improvements over time.
Also, adoption won’t happen overnight. Your team needs time to learn, test, and trust the new system. To make change smoother:
Start with champions: team members who are open to trying new tools
Show quick wins to build confidence
Offer hands-on training and simple guides
Keep communication open and honest
Celebrate early successes, and listen to feedback as you go.
And the most important part – AI adoption works best when business and tech teams work as one. Don’t silo development or decisions.
Bring everyone to the table: underwriters, ops leaders, data folks, IT, compliance. Align on goals early. Stay in sync throughout rollout.
You’ve got momentum, but what’s coming next? Here are the trends to keep your eye on as the space evolves.
What trends should you watch in 2025?
Agentic AI is moving fast, and 2025 will be a big year for new tools, rules, and ways to work. Here are four trends worth keeping an eye on:
1. Emerging agentic AI platforms
New AI platforms built specifically for insurance are hitting the market. These tools are more plug-and-play, easier to integrate, and focused on real tasks, like claims handling, underwriting, and compliance support.
One standout example is Strada’s AI voice platform. It’s part of a growing wave of vertical AI tools that blend accuracy, speed, and industry knowledge, showing what’s possible when tech is purpose-built for insurance.
2. Evolving regulatory guidance
Regulators are starting to catch up with AI. Expect clearer rules around explainability, fairness, and accountability. Stay proactive: compliance teams should be involved early in every AI rollout.
3. Cross-industry innovations
Insurance isn’t building in a bubble. Retail, finance, and healthcare are pushing the boundaries of agentic AI. Watch what they’re doing: you’ll spot ideas you can adapt for your workflows.
4. Human + AI partnerships
The smartest teams will team up with AI. Look for workflows where AI takes care of the busywork so your people can focus on higher-value decisions and relationships.
Here’s what the new working relationship looks like.
Stay curious, stay flexible, and you’ll be ready to lead, not just keep up. You’re almost there.
Let’s wrap things up with a quick recap and a push to start exploring agentic AI for yourself.
Final thoughts
Agentic AI is here and it’s changing how insurance gets done.
We’ve covered what it is, why it matters, how it works, and where to start. You’ve seen the potential: faster workflows, better decisions, and happier customers. You've also seen the risks and how to manage them.
For MGAs and carriers, this is a real opportunity to lead. You don’t need to be perfect. You just need to start.
Begin small. Pick a clear use case. Measure success. Then build from there.
AI won’t replace your team, but it will free them up to do their best work. And that’s where the magic happens.
So don’t wait for the “perfect” moment. Explore. Experiment. Learn. The future of insurance is already in motion, and there’s a seat at the table with your name on it.
P.S. If you’re ready to take that first step, tools like Strada are built to help. It’s designed specifically for insurance, works with the systems you already use, and handles real conversations that deliver real results.
It’s a solid place to start (and scale!) from.
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Voice Agents
Agentic AI in Insurance: A 2025 Guide for MGAs and Carriers
Amir Prodensky
CEO
Jul 17, 2025
15 min read
Your next best employee might be an AI agent, here’s what that means for insurance
It’s 9:15 AM and hundreds of calls have already been done for you.. Leads are qualified, quotes are booked, policies are serviced, and your team hasn’t even had coffee yet.
That’s not the future.
That’s agentic AI, and it’s happening right now in insurance.
If you're an MGA or carrier leader, you're probably wondering: Can this actually help us move faster, cut waste, and still manage risk?
Short answer: YES! That’s exactly why we created this guide. In it, you’ll learn:
What agentic AI is (in plain English)
Why it’s different from traditional automation
Where it fits in the insurance workflow
How to get started without overwhelm
What risks to watch for and how to manage them
Let’s dig in and explore how you can put agentic AI to work for your business, step by step.
What is agentic AI for insurance?
Agentic AI is a type of artificial intelligence that can act on its own to achieve goals. It takes initiative, makes decisions, and adapts as it learns.
Think of it as a smart assistant that knows what needs to be done and finds the best way to do it.
So, how is this different from traditional AI? Here is a quick snapshot:
Feature / behavior | Traditional AI | Agentic AI |
Approach | Task-based | Goal-based |
How it works | Follows fixed rules or models | Plans actions, makes decisions, and adapts |
Input required | Needs specific instructions | Needs a goal or desired outcome |
Adaptability | Rigid—doesn’t adjust on its own | Flexible—learns and improves with feedback |
Example use | Flagging potential fraud in claims | Reducing overall claim cycle time from intake to payout |
Human involvement | High—requires setup, monitoring, and tuning | Lower—operates with oversight but takes initiative |
Scope of tasks | Narrow—single-step automation | Broad—multi-step workflows (e.g., intake → routing) |
Response to change | Needs reprogramming or retraining | Adjusts strategy automatically if the situation changes |
Value delivered | Efficiency in one area | Continuous improvement across full workflows |
Best for | Repetitive, rule-based tasks | Dynamic, outcome-driven processes |
Here’s what makes agentic AI special:
It’s autonomous = it works without constant input. Once it knows the objective, it runs with it.
It’s goal-directed = it works toward an outcome, not just complete tasks.
It’s adaptive = it learns from what works and changes course if needed.
Now that you know what agentic AI is, let’s talk about why it matters, especially for MGAs and carriers navigating today’s challenges.
Why should MGAs and carriers care about it?
The insurance industry’s under pressure. Costs are rising. Talent’s tight. Customers expect fast, digital-first service. but legacy systems and manual workflows still slow things down.
Sound familiar?
Agentic AI offers a real chance to break that cycle. It tackles problems end-to-end. That means fewer bottlenecks, smarter decisions, and happier customers.
Here’s what agentic AI can help with:
Efficiency: It handles routine tasks like quote generation, policy updates, and claims triage, fast and around the clock.
Accuracy: It reduces errors by learning from past outcomes and checking its own work.
Customer experience: It speeds up responses, personalizes interactions, and keeps things moving, without dropping the ball.
And the impact’s already real.
Some MGAs are using agentic AI to process bind requests in minutes, not hours. Carriers are deploying it to flag risky claims before they escalate.
In both cases, teams save time, customers get faster answers, and ops run smoother.
Let’s zoom out for a second. Here’s what the journey from manual work to agentic AI actually looks like. You’ve probably already started without realizing it.
You’ve seen the potential. But how does it actually work in the day-to-day insurance world? Let’s break that down.
How does agentic AI work in insurance?
Agentic AI works by taking a goal (like speeding up claims) and figuring out how to get there, step by step. It pulls from data, makes decisions on the fly, and adjusts based on what’s working.
In insurance, that means it jumps into real workflows and gets things moving.
Let’s break it down – here’s a typical agentic AI workflows include:
Taking in a task or goal (e.g., bind a policy, handle a claim)
Analyzing available data (documents, past activity, customer history)
Planning the steps needed
Acting on them, without constant human input
Learning from outcomes and tweaking the approach
To make this even more concrete, here’s a quick snapshot of what agentic AI can actually do in a real MGA or carrier environment:
Workflow area | What Agentic AI can do | Who it helps |
New business intake | Auto-read submissions, classify risks, route to underwriter | MGAs, Underwriters |
Claims | Guide FNOL calls, detect fraud patterns, automate triage | Carriers, Claims Teams |
Customer service | Answer questions via voice/chat, handle policy updates | Brokers, Policyholders |
Compliance | Track regulatory requirements, flag missing docs | Ops, Compliance Teams |
Policy management | Auto-renew low-risk policies, send reminders | Carriers, MGAs |
Voice platforms are a growing part of this. A great example of this in action is Strada’s phone AI agents. They’re built specifically for insurance and can handle policy servicing, renewals, and even lead qualification, all by phone.
That means no hold times, no dropped calls, and conversations that actually move things forward. So what does this actually feel like during a workday? Here’s a side-by-side of life before and after agentic AI.
Understanding the workflows is one thing, but what real problems can agentic AI help fix? Turns out, quite a few.
What problems can agentic AI in insurance help solve?
Agentic AI solves real problems that slow MGAs and carriers down every day. If you’re stuck juggling systems, chasing data, or trying to keep up with growing workloads, this is where agentic AI can make a real difference.
Here’s a simple map of common insurance challenges and how agentic AI can step in to fix them.
Problem area | Common pain point | What Agentic AI can do |
Customer engagement | Slow replies, dropped messages | Respond instantly via email/chat/phone |
Underwriting | Manual review, missing data | Pre-screen, flag gaps, smart routing |
Data silos | Info scattered across systems | Pull, connect, and organize data |
Risk assessment | Complex patterns, changing inputs | Spot trends, adapt to new signals |
Compliance & reporting | Manual tracking, risk of errors | Auto-checks, generate audit-ready reports |
And let’s walk through a few of the big pain points.
1. Customer engagement gaps
Customers want answers fast, no matter if they’re shopping for coverage, updating a policy, or filing a claim. But slow email replies, long phone wait times, or missed follow-ups can leave them frustrated.
Agentic AI can:
Answer calls and emails instantly
Handle certificate requests, payment questions, and more
Stay available 24/7 with no hold time
Follow up automatically if something’s missing
Phone-based agents trained on insurance conversations now regularly support tasks like quote requests or certificate delivery, making engagement faster and easier.
Platforms like Strada make this possible by using AI that understands policy terms, coverage questions, and next steps, without needing a human to jump in. For example, an AI voice agent can confirm a VIN, send a certificate, and log the interaction in your system, all in one call.
It helps you show up for customers even when your team’s swamped—and keeps service moving around the clock.
2. Underwriting delays
Underwriters often waste time digging through documents, chasing missing info, or sorting submissions.
Agentic AI can:
Review applications and flag missing data
Classify submissions by complexity
Auto-route tasks to the right underwriter
Even pre-fill forms using past data
This cuts turnaround times and keeps business moving.
3. Data silos
Your data’s everywhere: CRM, email, PDFs, policy systems. That makes it hard to see the full picture.
Agentic AI can connect the dots. It pulls from multiple sources, organizes what it finds, and presents insights clearly. So whether you're pricing a risk or analyzing trends, you get the full story without the hunt.
Platforms like Strada help by connecting directly to major insurance systems – policy admin tools, CRMs, document storage, and more. That means no more jumping between platforms or losing time copying data.
Everything stays in sync, and your AI agents always have what they need to act with context.
4. Risk assessment complexity
Assessing risk takes time and judgment. But with so much data coming in (from past claims to third-party sources), it’s easy to miss something.
Agentic AI can:
Spot patterns across policies and claims
Recommend actions based on historical outcomes
Adjust in real time if new risks emerge
It won’t replace human judgment, but it gives your team a smarter starting point.
5. Compliance and reporting
Regulatory reporting is essential, but also time-consuming and detail-heavy.
Agentic AI can:
Track call content for compliance
Spot missing documents or incomplete data
Generate call logs and summaries
Keep audit-ready records without manual work
Some platforms, like Strada, also check the accuracy of every AI response. That helps reduce mistakes and keeps you audit-ready without extra overhead.
In short, agentic AI is all about doing things better. You’ll close gaps, reduce delays, and give your teams more time to focus on high-value work.
Of course, no tool is perfect. Let’s take a look at the key risks and how to manage them smartly.
What are the risks and limitations?
Agentic AI has big potential, but it’s not plug-and-play magic. Like any powerful tool, it comes with risks and trade-offs. Knowing what to watch for helps you use it wisely.
Here are a few areas to keep in mind:
1. Regulatory and ethical considerations
Insurance is a tightly regulated industry. If your AI makes decisions that impact coverage or pricing, you need to ensure those decisions follow the rules and treat people fairly.
That means you’ll need:
Clear guardrails
Human oversight for sensitive actions
Ongoing monitoring to catch bias or drift
You don’t want your AI unintentionally denying coverage or skirting compliance.
2. Explainability and transparency
Agentic AI can feel like a black box. If it flags a claim or declines a quote, people will ask, “Why?”
That’s why explainability matters. Make sure your AI tools can show their reasoning, especially for anything customer-facing or regulator-facing.
Strada helps with this by logging every step of the interaction and providing clear summaries of what the AI did and why. That transparency builds trust, with both your team and your customers.
3. Integration with legacy systems
Let’s be honest: most insurance tech stacks weren’t built for this. Connecting agentic AI to old platforms can be tricky and time-consuming.
Start small. Focus on use cases where data is clean and access is easy. Prove value, then expand.
4. Risk of over-reliance
Agentic AI is smart, but it’s not perfect. Don’t let your team get too hands-off. Keep people in the loop, especially for decisions that carry risk or impact customer trust.
Used well, agentic AI becomes a powerful teammate. Just remember: like any teammate, it needs direction, oversight, and the right tools to thrive. Here's how to take your first steps with confidence.
How to get started with AI insurance agent
Ready to bring agentic AI into your business? You don’t need to overhaul everything at once. Start small, prove value fast, and grow from there.
Here’s how to get started, step by step.
1. Assess your readiness
Before you dive in, take a quick pulse check. Ask yourself:
Do we have clear business goals AI can support?
Is our data clean, accessible, and organized?
Do we have the internal support (IT, ops, compliance) to pilot something new?
You don’t need everything perfect. But you do need a solid foundation and buy-in from leadership.
2. Identify high-impact use cases
Next, find pain points that slow your team down or frustrate customers. Look for areas with repeatable tasks, clear rules, or too many manual handoffs.
Some common early wins:
Auto-responding to broker emails
Pre-screening submissions
Scheduling follow-ups
Flagging incomplete applications
You’re looking for “low lift, high impact” opportunities – the stuff that doesn’t require deep system changes but delivers clear value.
3. Crawl → walk → run
Don’t try to automate everything on day one. Start with one use case. Then grow.
Many teams start small with a Strada use case like answering inbound service calls. There’s no engineering lift, and since it integrates with core systems, it fits neatly into existing workflows.
Use this crawl-walk-run framework to pick starting points that deliver value without getting stuck in complexity.
Stage | Sample use case | Why it’s a good start |
Crawl | Auto-respond to broker emails | Easy to set up, shows instant time savings |
Walk | Pre-fill underwriting forms from submissions | Cuts manual entry, improves accuracy |
Run | Voice AI by Strada for FNOL and claims routing. | High impact, but needs clean data + integration |
Each stage builds confidence and helps your team learn what works.
4. Build vs. buy
You’ve got options.
Buy: Pre-built agentic AI tools (especially insurance-focused ones, like Strada) can get you started quickly. Look for solutions that integrate with your systems and allow customization.
Build: If you have strong tech talent and unique needs, you might want to develop your own agents. This gives you full control, but takes more time and resources.
Most MGAs and carriers start by buying, then layer in custom features later. If you’re looking to start fast, Strada’s platform is insurance-specific and comes with pre-built use cases like renewals and policy updates – no custom development needed.
5. Invest in data quality
Agentic AI is only as good as the data it sees. Messy, incomplete, or siloed data will slow you down, or worse – lead to bad decisions.
Make sure your key systems (policy, claims, CRM) are synced. Clean up old records. And ensure your agents have access to the right data at the right time.
Think of it like fuel. Better data means smoother, smarter performance.
[bonus tip] Start with the business, not the tech
You’re not “doing AI” for the sake of it. You’re solving business problems: faster quotes, better service, lower costs.
So frame every project around outcomes, not just features. That keeps the focus where it belongs. Once you’ve started, how do you know it’s working?
Let’s explore what successful adoption really looks like.
What does successful adoption look like?
Rolling out agentic AI is one thing. Making it work long-term is another. Success is about seeing real, measurable results and getting your team fully on board.
Want to know if it’s working? These are the metrics you’ll want to keep an eye on as you scale agentic AI.
Area | What to measure | Why it matters |
Efficiency | Time saved per task/process | Shows real productivity gains |
Speed | Cycle times for quotes, claims, etc. | Indicates faster service delivery |
Accuracy | Error rate in forms, decisions, responses | Reflects improved data handling |
Adoption | % of team actively using AI tools | Helps track buy-in and ROI |
Customer satisfaction | NPS, response time, issue resolution rate | Connects AI use to real-world experience |
Pick a few key metrics, set a baseline, and track improvements over time.
Also, adoption won’t happen overnight. Your team needs time to learn, test, and trust the new system. To make change smoother:
Start with champions: team members who are open to trying new tools
Show quick wins to build confidence
Offer hands-on training and simple guides
Keep communication open and honest
Celebrate early successes, and listen to feedback as you go.
And the most important part – AI adoption works best when business and tech teams work as one. Don’t silo development or decisions.
Bring everyone to the table: underwriters, ops leaders, data folks, IT, compliance. Align on goals early. Stay in sync throughout rollout.
You’ve got momentum, but what’s coming next? Here are the trends to keep your eye on as the space evolves.
What trends should you watch in 2025?
Agentic AI is moving fast, and 2025 will be a big year for new tools, rules, and ways to work. Here are four trends worth keeping an eye on:
1. Emerging agentic AI platforms
New AI platforms built specifically for insurance are hitting the market. These tools are more plug-and-play, easier to integrate, and focused on real tasks, like claims handling, underwriting, and compliance support.
One standout example is Strada’s AI voice platform. It’s part of a growing wave of vertical AI tools that blend accuracy, speed, and industry knowledge, showing what’s possible when tech is purpose-built for insurance.
2. Evolving regulatory guidance
Regulators are starting to catch up with AI. Expect clearer rules around explainability, fairness, and accountability. Stay proactive: compliance teams should be involved early in every AI rollout.
3. Cross-industry innovations
Insurance isn’t building in a bubble. Retail, finance, and healthcare are pushing the boundaries of agentic AI. Watch what they’re doing: you’ll spot ideas you can adapt for your workflows.
4. Human + AI partnerships
The smartest teams will team up with AI. Look for workflows where AI takes care of the busywork so your people can focus on higher-value decisions and relationships.
Here’s what the new working relationship looks like.
Stay curious, stay flexible, and you’ll be ready to lead, not just keep up. You’re almost there.
Let’s wrap things up with a quick recap and a push to start exploring agentic AI for yourself.
Final thoughts
Agentic AI is here and it’s changing how insurance gets done.
We’ve covered what it is, why it matters, how it works, and where to start. You’ve seen the potential: faster workflows, better decisions, and happier customers. You've also seen the risks and how to manage them.
For MGAs and carriers, this is a real opportunity to lead. You don’t need to be perfect. You just need to start.
Begin small. Pick a clear use case. Measure success. Then build from there.
AI won’t replace your team, but it will free them up to do their best work. And that’s where the magic happens.
So don’t wait for the “perfect” moment. Explore. Experiment. Learn. The future of insurance is already in motion, and there’s a seat at the table with your name on it.
P.S. If you’re ready to take that first step, tools like Strada are built to help. It’s designed specifically for insurance, works with the systems you already use, and handles real conversations that deliver real results.
It’s a solid place to start (and scale!) from.
Table of Contents
Join innovative carriers, MGAs, and brokers transforming their calls with Strada.
Start scaling with voice AI agents today
Join innovative carriers and MGAs transforming their calls with Strada.
Voice Agents
Agentic AI in Insurance: A 2025 Guide for MGAs and Carriers
Amir Prodensky
CEO
Jul 17, 2025
15 min read
Your next best employee might be an AI agent, here’s what that means for insurance
It’s 9:15 AM and hundreds of calls have already been done for you.. Leads are qualified, quotes are booked, policies are serviced, and your team hasn’t even had coffee yet.
That’s not the future.
That’s agentic AI, and it’s happening right now in insurance.
If you're an MGA or carrier leader, you're probably wondering: Can this actually help us move faster, cut waste, and still manage risk?
Short answer: YES! That’s exactly why we created this guide. In it, you’ll learn:
What agentic AI is (in plain English)
Why it’s different from traditional automation
Where it fits in the insurance workflow
How to get started without overwhelm
What risks to watch for and how to manage them
Let’s dig in and explore how you can put agentic AI to work for your business, step by step.
What is agentic AI for insurance?
Agentic AI is a type of artificial intelligence that can act on its own to achieve goals. It takes initiative, makes decisions, and adapts as it learns.
Think of it as a smart assistant that knows what needs to be done and finds the best way to do it.
So, how is this different from traditional AI? Here is a quick snapshot:
Feature / behavior | Traditional AI | Agentic AI |
Approach | Task-based | Goal-based |
How it works | Follows fixed rules or models | Plans actions, makes decisions, and adapts |
Input required | Needs specific instructions | Needs a goal or desired outcome |
Adaptability | Rigid—doesn’t adjust on its own | Flexible—learns and improves with feedback |
Example use | Flagging potential fraud in claims | Reducing overall claim cycle time from intake to payout |
Human involvement | High—requires setup, monitoring, and tuning | Lower—operates with oversight but takes initiative |
Scope of tasks | Narrow—single-step automation | Broad—multi-step workflows (e.g., intake → routing) |
Response to change | Needs reprogramming or retraining | Adjusts strategy automatically if the situation changes |
Value delivered | Efficiency in one area | Continuous improvement across full workflows |
Best for | Repetitive, rule-based tasks | Dynamic, outcome-driven processes |
Here’s what makes agentic AI special:
It’s autonomous = it works without constant input. Once it knows the objective, it runs with it.
It’s goal-directed = it works toward an outcome, not just complete tasks.
It’s adaptive = it learns from what works and changes course if needed.
Now that you know what agentic AI is, let’s talk about why it matters, especially for MGAs and carriers navigating today’s challenges.
Why should MGAs and carriers care about it?
The insurance industry’s under pressure. Costs are rising. Talent’s tight. Customers expect fast, digital-first service. but legacy systems and manual workflows still slow things down.
Sound familiar?
Agentic AI offers a real chance to break that cycle. It tackles problems end-to-end. That means fewer bottlenecks, smarter decisions, and happier customers.
Here’s what agentic AI can help with:
Efficiency: It handles routine tasks like quote generation, policy updates, and claims triage, fast and around the clock.
Accuracy: It reduces errors by learning from past outcomes and checking its own work.
Customer experience: It speeds up responses, personalizes interactions, and keeps things moving, without dropping the ball.
And the impact’s already real.
Some MGAs are using agentic AI to process bind requests in minutes, not hours. Carriers are deploying it to flag risky claims before they escalate.
In both cases, teams save time, customers get faster answers, and ops run smoother.
Let’s zoom out for a second. Here’s what the journey from manual work to agentic AI actually looks like. You’ve probably already started without realizing it.
You’ve seen the potential. But how does it actually work in the day-to-day insurance world? Let’s break that down.
How does agentic AI work in insurance?
Agentic AI works by taking a goal (like speeding up claims) and figuring out how to get there, step by step. It pulls from data, makes decisions on the fly, and adjusts based on what’s working.
In insurance, that means it jumps into real workflows and gets things moving.
Let’s break it down – here’s a typical agentic AI workflows include:
Taking in a task or goal (e.g., bind a policy, handle a claim)
Analyzing available data (documents, past activity, customer history)
Planning the steps needed
Acting on them, without constant human input
Learning from outcomes and tweaking the approach
To make this even more concrete, here’s a quick snapshot of what agentic AI can actually do in a real MGA or carrier environment:
Workflow area | What Agentic AI can do | Who it helps |
New business intake | Auto-read submissions, classify risks, route to underwriter | MGAs, Underwriters |
Claims | Guide FNOL calls, detect fraud patterns, automate triage | Carriers, Claims Teams |
Customer service | Answer questions via voice/chat, handle policy updates | Brokers, Policyholders |
Compliance | Track regulatory requirements, flag missing docs | Ops, Compliance Teams |
Policy management | Auto-renew low-risk policies, send reminders | Carriers, MGAs |
Voice platforms are a growing part of this. A great example of this in action is Strada’s phone AI agents. They’re built specifically for insurance and can handle policy servicing, renewals, and even lead qualification, all by phone.
That means no hold times, no dropped calls, and conversations that actually move things forward. So what does this actually feel like during a workday? Here’s a side-by-side of life before and after agentic AI.
Understanding the workflows is one thing, but what real problems can agentic AI help fix? Turns out, quite a few.
What problems can agentic AI in insurance help solve?
Agentic AI solves real problems that slow MGAs and carriers down every day. If you’re stuck juggling systems, chasing data, or trying to keep up with growing workloads, this is where agentic AI can make a real difference.
Here’s a simple map of common insurance challenges and how agentic AI can step in to fix them.
Problem area | Common pain point | What Agentic AI can do |
Customer engagement | Slow replies, dropped messages | Respond instantly via email/chat/phone |
Underwriting | Manual review, missing data | Pre-screen, flag gaps, smart routing |
Data silos | Info scattered across systems | Pull, connect, and organize data |
Risk assessment | Complex patterns, changing inputs | Spot trends, adapt to new signals |
Compliance & reporting | Manual tracking, risk of errors | Auto-checks, generate audit-ready reports |
And let’s walk through a few of the big pain points.
1. Customer engagement gaps
Customers want answers fast, no matter if they’re shopping for coverage, updating a policy, or filing a claim. But slow email replies, long phone wait times, or missed follow-ups can leave them frustrated.
Agentic AI can:
Answer calls and emails instantly
Handle certificate requests, payment questions, and more
Stay available 24/7 with no hold time
Follow up automatically if something’s missing
Phone-based agents trained on insurance conversations now regularly support tasks like quote requests or certificate delivery, making engagement faster and easier.
Platforms like Strada make this possible by using AI that understands policy terms, coverage questions, and next steps, without needing a human to jump in. For example, an AI voice agent can confirm a VIN, send a certificate, and log the interaction in your system, all in one call.
It helps you show up for customers even when your team’s swamped—and keeps service moving around the clock.
2. Underwriting delays
Underwriters often waste time digging through documents, chasing missing info, or sorting submissions.
Agentic AI can:
Review applications and flag missing data
Classify submissions by complexity
Auto-route tasks to the right underwriter
Even pre-fill forms using past data
This cuts turnaround times and keeps business moving.
3. Data silos
Your data’s everywhere: CRM, email, PDFs, policy systems. That makes it hard to see the full picture.
Agentic AI can connect the dots. It pulls from multiple sources, organizes what it finds, and presents insights clearly. So whether you're pricing a risk or analyzing trends, you get the full story without the hunt.
Platforms like Strada help by connecting directly to major insurance systems – policy admin tools, CRMs, document storage, and more. That means no more jumping between platforms or losing time copying data.
Everything stays in sync, and your AI agents always have what they need to act with context.
4. Risk assessment complexity
Assessing risk takes time and judgment. But with so much data coming in (from past claims to third-party sources), it’s easy to miss something.
Agentic AI can:
Spot patterns across policies and claims
Recommend actions based on historical outcomes
Adjust in real time if new risks emerge
It won’t replace human judgment, but it gives your team a smarter starting point.
5. Compliance and reporting
Regulatory reporting is essential, but also time-consuming and detail-heavy.
Agentic AI can:
Track call content for compliance
Spot missing documents or incomplete data
Generate call logs and summaries
Keep audit-ready records without manual work
Some platforms, like Strada, also check the accuracy of every AI response. That helps reduce mistakes and keeps you audit-ready without extra overhead.
In short, agentic AI is all about doing things better. You’ll close gaps, reduce delays, and give your teams more time to focus on high-value work.
Of course, no tool is perfect. Let’s take a look at the key risks and how to manage them smartly.
What are the risks and limitations?
Agentic AI has big potential, but it’s not plug-and-play magic. Like any powerful tool, it comes with risks and trade-offs. Knowing what to watch for helps you use it wisely.
Here are a few areas to keep in mind:
1. Regulatory and ethical considerations
Insurance is a tightly regulated industry. If your AI makes decisions that impact coverage or pricing, you need to ensure those decisions follow the rules and treat people fairly.
That means you’ll need:
Clear guardrails
Human oversight for sensitive actions
Ongoing monitoring to catch bias or drift
You don’t want your AI unintentionally denying coverage or skirting compliance.
2. Explainability and transparency
Agentic AI can feel like a black box. If it flags a claim or declines a quote, people will ask, “Why?”
That’s why explainability matters. Make sure your AI tools can show their reasoning, especially for anything customer-facing or regulator-facing.
Strada helps with this by logging every step of the interaction and providing clear summaries of what the AI did and why. That transparency builds trust, with both your team and your customers.
3. Integration with legacy systems
Let’s be honest: most insurance tech stacks weren’t built for this. Connecting agentic AI to old platforms can be tricky and time-consuming.
Start small. Focus on use cases where data is clean and access is easy. Prove value, then expand.
4. Risk of over-reliance
Agentic AI is smart, but it’s not perfect. Don’t let your team get too hands-off. Keep people in the loop, especially for decisions that carry risk or impact customer trust.
Used well, agentic AI becomes a powerful teammate. Just remember: like any teammate, it needs direction, oversight, and the right tools to thrive. Here's how to take your first steps with confidence.
How to get started with AI insurance agent
Ready to bring agentic AI into your business? You don’t need to overhaul everything at once. Start small, prove value fast, and grow from there.
Here’s how to get started, step by step.
1. Assess your readiness
Before you dive in, take a quick pulse check. Ask yourself:
Do we have clear business goals AI can support?
Is our data clean, accessible, and organized?
Do we have the internal support (IT, ops, compliance) to pilot something new?
You don’t need everything perfect. But you do need a solid foundation and buy-in from leadership.
2. Identify high-impact use cases
Next, find pain points that slow your team down or frustrate customers. Look for areas with repeatable tasks, clear rules, or too many manual handoffs.
Some common early wins:
Auto-responding to broker emails
Pre-screening submissions
Scheduling follow-ups
Flagging incomplete applications
You’re looking for “low lift, high impact” opportunities – the stuff that doesn’t require deep system changes but delivers clear value.
3. Crawl → walk → run
Don’t try to automate everything on day one. Start with one use case. Then grow.
Many teams start small with a Strada use case like answering inbound service calls. There’s no engineering lift, and since it integrates with core systems, it fits neatly into existing workflows.
Use this crawl-walk-run framework to pick starting points that deliver value without getting stuck in complexity.
Stage | Sample use case | Why it’s a good start |
Crawl | Auto-respond to broker emails | Easy to set up, shows instant time savings |
Walk | Pre-fill underwriting forms from submissions | Cuts manual entry, improves accuracy |
Run | Voice AI by Strada for FNOL and claims routing. | High impact, but needs clean data + integration |
Each stage builds confidence and helps your team learn what works.
4. Build vs. buy
You’ve got options.
Buy: Pre-built agentic AI tools (especially insurance-focused ones, like Strada) can get you started quickly. Look for solutions that integrate with your systems and allow customization.
Build: If you have strong tech talent and unique needs, you might want to develop your own agents. This gives you full control, but takes more time and resources.
Most MGAs and carriers start by buying, then layer in custom features later. If you’re looking to start fast, Strada’s platform is insurance-specific and comes with pre-built use cases like renewals and policy updates – no custom development needed.
5. Invest in data quality
Agentic AI is only as good as the data it sees. Messy, incomplete, or siloed data will slow you down, or worse – lead to bad decisions.
Make sure your key systems (policy, claims, CRM) are synced. Clean up old records. And ensure your agents have access to the right data at the right time.
Think of it like fuel. Better data means smoother, smarter performance.
[bonus tip] Start with the business, not the tech
You’re not “doing AI” for the sake of it. You’re solving business problems: faster quotes, better service, lower costs.
So frame every project around outcomes, not just features. That keeps the focus where it belongs. Once you’ve started, how do you know it’s working?
Let’s explore what successful adoption really looks like.
What does successful adoption look like?
Rolling out agentic AI is one thing. Making it work long-term is another. Success is about seeing real, measurable results and getting your team fully on board.
Want to know if it’s working? These are the metrics you’ll want to keep an eye on as you scale agentic AI.
Area | What to measure | Why it matters |
Efficiency | Time saved per task/process | Shows real productivity gains |
Speed | Cycle times for quotes, claims, etc. | Indicates faster service delivery |
Accuracy | Error rate in forms, decisions, responses | Reflects improved data handling |
Adoption | % of team actively using AI tools | Helps track buy-in and ROI |
Customer satisfaction | NPS, response time, issue resolution rate | Connects AI use to real-world experience |
Pick a few key metrics, set a baseline, and track improvements over time.
Also, adoption won’t happen overnight. Your team needs time to learn, test, and trust the new system. To make change smoother:
Start with champions: team members who are open to trying new tools
Show quick wins to build confidence
Offer hands-on training and simple guides
Keep communication open and honest
Celebrate early successes, and listen to feedback as you go.
And the most important part – AI adoption works best when business and tech teams work as one. Don’t silo development or decisions.
Bring everyone to the table: underwriters, ops leaders, data folks, IT, compliance. Align on goals early. Stay in sync throughout rollout.
You’ve got momentum, but what’s coming next? Here are the trends to keep your eye on as the space evolves.
What trends should you watch in 2025?
Agentic AI is moving fast, and 2025 will be a big year for new tools, rules, and ways to work. Here are four trends worth keeping an eye on:
1. Emerging agentic AI platforms
New AI platforms built specifically for insurance are hitting the market. These tools are more plug-and-play, easier to integrate, and focused on real tasks, like claims handling, underwriting, and compliance support.
One standout example is Strada’s AI voice platform. It’s part of a growing wave of vertical AI tools that blend accuracy, speed, and industry knowledge, showing what’s possible when tech is purpose-built for insurance.
2. Evolving regulatory guidance
Regulators are starting to catch up with AI. Expect clearer rules around explainability, fairness, and accountability. Stay proactive: compliance teams should be involved early in every AI rollout.
3. Cross-industry innovations
Insurance isn’t building in a bubble. Retail, finance, and healthcare are pushing the boundaries of agentic AI. Watch what they’re doing: you’ll spot ideas you can adapt for your workflows.
4. Human + AI partnerships
The smartest teams will team up with AI. Look for workflows where AI takes care of the busywork so your people can focus on higher-value decisions and relationships.
Here’s what the new working relationship looks like.
Stay curious, stay flexible, and you’ll be ready to lead, not just keep up. You’re almost there.
Let’s wrap things up with a quick recap and a push to start exploring agentic AI for yourself.
Final thoughts
Agentic AI is here and it’s changing how insurance gets done.
We’ve covered what it is, why it matters, how it works, and where to start. You’ve seen the potential: faster workflows, better decisions, and happier customers. You've also seen the risks and how to manage them.
For MGAs and carriers, this is a real opportunity to lead. You don’t need to be perfect. You just need to start.
Begin small. Pick a clear use case. Measure success. Then build from there.
AI won’t replace your team, but it will free them up to do their best work. And that’s where the magic happens.
So don’t wait for the “perfect” moment. Explore. Experiment. Learn. The future of insurance is already in motion, and there’s a seat at the table with your name on it.
P.S. If you’re ready to take that first step, tools like Strada are built to help. It’s designed specifically for insurance, works with the systems you already use, and handles real conversations that deliver real results.
It’s a solid place to start (and scale!) from.
Table of Contents
Join innovative carriers, MGAs, and brokers transforming their calls with Strada.
Start scaling with voice AI agents today
Join innovative carriers and MGAs transforming their calls with Strada.
Phone AI agents for insurance distribution
© 2025 Strada API, Inc.
Phone AI agents for insurance distribution
© 2025 Strada API, Inc.
Phone AI agents for insurance distribution
© 2025 Strada API, Inc.