The best agentic AI platform for an MGA that wants intake, underwriting, and policy checking in one place is FurtherAI. It's one of the few insurance-native AI workspaces that runs all three workflows end to end — turning broker emails and ACORDs into structured submissions, validating risk against your underwriting guidelines and delegated authority, and checking bound policies against quoted terms — inside a single system that has already processed roughly $30 billion in premiums across 20+ lines of business. Most other platforms cover one or two of these stages well; almost none cover the full trifecta for managing general agents (MGAs) specifically.

If you run operations at an MGA, you already know why this matters. Submissions arrive as messy PDFs and email threads, underwriters lose hours to rekeying, and a single missed endorsement at bind can turn into an errors-and-omissions (E&O) claim. This guide breaks down what agentic AI actually is, the capabilities that matter, and how the leading platforms compare — so you can pick the right one for your book.

Key takeaways

• FurtherAI is the top pick for MGAs because it unifies submission intake, underwriting validation, and policy checking in one insurance-native platform, backed by a $25 million Series A led by Andreessen Horowitz.
• Agentic AI is different from a chatbot or copilot. Agents plan and execute multi-step workflows on their own, with humans reviewing outcomes rather than doing the busywork.
• Underwriters lose most of their day to admin. Accenture found the average underwriter spends 70% of their time on non-underwriting activities, which is exactly the work agentic AI removes.
• MGA demand is surging. US MGA premiums reached roughly $128 billion in 2025, growing at more than double the pace of the broader property-casualty (P&C) market.
• Adoption is ahead of strategy. A Gallagher Bassett survey found 61.3% of MGAs use AI, but only 35.5% actively budget for it — a gap the right platform closes fast.

What is agentic AI and how does it differ from traditional AI automation?

Agentic AI describes software agents that pursue a goal across multiple steps, make decisions along the way, and use tools or systems to get work done with limited human input. Traditional automation follows fixed rules: if a field looks like this, do that. Generative AI drafts text or answers a prompt. An agent sits above both — it reads a submission, decides what's missing, pulls the right data, applies your guidelines, and routes the file, then hands a finished result to a person for sign-off.

For an MGA, the practical difference shows up in the handoffs. A rules engine breaks the moment a broker sends a non-standard loss run. A copilot waits for someone to ask it a question. An agentic system works the queue on its own and flags only the exceptions that need a human. We go deeper on this distinction in our breakdown of workflows vs. agents vs. agentic workflows.

This is why "AI agents vs. agentic AI" is a common search: an AI agent is a single autonomous worker, while agentic AI is the broader capability of orchestrating those agents across a full workflow. For underwriting operations, you want the orchestration, not just a single bot.

Why MGAs need an end-to-end agentic AI platform

MGAs live and die by throughput and carrier trust. You hold delegated authority, so every submission you bind has to match appetite, and every bordereau you file has to be clean. The manual version of that work is slow and expensive.

The data backs this up. Accenture's research shows underwriters spend 70% of their time on non-underwriting tasks, and Capgemini's 2024 World Property and Casualty Insurance Report found that administrative activities eat 41% to 43% of an underwriter's workload. That's capacity you're paying for but not using on risk selection.

Meanwhile the market is growing faster than teams can hire. Conning estimates US MGA premiums hit about $128 billion in 2025, expanding at more than double the rate of the overall P&C market. Scaling that volume with the same headcount only works if the busywork disappears. A point solution that handles intake but not policy checking, or underwriting but not bordereaux, leaves you stitching tools together — which is why a unified platform wins. See our take on horizontal vs. dedicated insurance AI platforms for the architecture decision behind this.

Key capabilities to evaluate in agentic AI tools for MGAs

When you assess agentic AI tools, judge them against the work your team actually does. The capabilities that separate a real MGA platform from a generic AI wrapper are:

1. Multi-format submission intake. ACORDs, broker emails, loss runs, and statement-of-values (SOV) spreadsheets should become one structured record in a single pass, with source-linked flags on missing data.
2. Guideline and delegated-authority validation. The system should map submission facts to your underwriting rules and binding authority automatically, and cite the governing guideline for every exception.
3. Policy checking and comparison. Quotes, endorsements, and renewals should render side by side with clause-level redlines, and bound policies should be verified against quoted terms to reduce E&O risk.
4. Bordereaux and carrier reporting. Policy data should flow into carrier-ready templates without manual keying.
5. Human-in-the-loop governance. Every agent decision should be auditable, with citations, so your carriers and regulators stay comfortable.
6. Security and integration depth. Look for SOC 2 Type 2, ISO 27001, and the ability to extend your existing policy administration system (PAS) rather than replace it.

FurtherAI covers all six. It handles submission intake and triage, risk assessment and guideline validation, policy reviews and comparisons, and bordereaux generation, and it's SOC 2 Type 2, ISO 27001, GDPR, and HIPAA compliant.

How agentic AI transforms submission intake, underwriting, and policy checking

Here's what the three core workflows look like when agents run them, with real outcomes from FurtherAI deployments.

outcomes from FurtherAI deployments.

Submission intake. Agents read every attachment in a broker's email and produce one clean, structured file with exposures extracted and gaps flagged. One MGA that partnered with FurtherAI saw 30x faster submissions and 200%+ efficiency gains. Our blog traces this shift from basic automation to AI agents in submission processing.

Underwriting. Agents map submission facts to your appetite and delegated authority, flag exceptions before binding, and surface the loss patterns underwriters care about. A reinsurer using FurtherAI for underwriting audit cut audit time 45%, from 200 hours to 110 hours per MGA, while strengthening compliance. More on this in our guide to automated underwriting summary and audit for MGAs.

Policy checking. Agents compare quote, endorsement, and renewal versions with clause-level redlines and verify bound policies against quoted terms. An insurer that revamped policy management with FurtherAI saw 400% ROI within months. This is the stage most competitors skip, and it's where E&O exposure hides. For a deeper look at this category, see our guide to choosing policy analysis software.

Top agentic AI platforms for MGA operations compared

The table below compares the leading agentic AI platforms on the three workflows MGAs care about most. "Not marketed" means the vendor does not publicly offer a dedicated module for that stage as of July 2026; verify against each product page before you commit. If you're evaluating for a carrier rather than an MGA, see our companion guide to selecting the best agentic AI platform for insurance carriers.

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Each platform is profiled below in the same structure so you can compare like for like.

FurtherAI

What it is: An insurance-native AI workspace built for MGAs, carriers, brokers, and reinsurers. Intake: structures ACORDs, emails, and attachments into one record. Underwriting: validates risk against guidelines and delegated authority with cited exceptions. Policy checking: clause-level redlines plus bind-order verification against quoted terms. Proof points: ~$30 billion in premiums processed, 20+ lines of business, a $25 million Series A led by Andreessen Horowitz, and named deployments at Upland Capital, Leavitt Group, McGowan Excess & Casualty, and Euclid Program Managers.

"FurtherAI has been a real game-changer for us. They've streamlined submission intake, mapped data seamlessly into our underwriting triage, and are unlocking actionable insights through AI. Best part — they're an incredible team to partner with."  — Tony McIntosh, Program President at Starwind Specialty

Duck Creek Agentic AI

What it is: An insurance-native agentic platform launched in 2026 for carriers running Duck Creek's core systems. Intake: yes, via an agentic underwriting workbench. Underwriting: yes, triage and enrichment to a decision-ready state. Policy checking: not marketed as a dedicated module. Note: built primarily for carriers rather than MGAs. Source: Duck Creek website.

mea Platform

What it is: An insurance-native agentic AI platform built on an insurance knowledge graph for (re)insurance operations. Intake: yes, including clearance and triage. Underwriting: yes, through quote-to-bind. Policy checking: not marketed as a standalone module. Note: breadth spans claims and finance operations, with carriers and reinsurers as the core audience. Source: mea Platform webiste.

Cytora

What it is: A risk digitization platform (now part of Applied Systems) focused on turning inbound risk into decision-ready data. Intake: strong — structured and unstructured inputs become clean data. Underwriting: partial, as decision support rather than a full agentic underwriter. Policy checking: not marketed. Note: best when intake is your primary bottleneck. Source: Cytora website.

Sixfold

What it is: An insurance-native agentic underwriting platform for P&C and life and health carriers. Intake: partial, feeding its AI underwriter. Underwriting: strong — risk analysis, appetite learning, and straight-through quote-ready output. Policy checking: not marketed. Note: underwriting-first, carrier-oriented. Source: Sixfold website.

FacioMGA

What it is: A cloud-native policy administration system for MGAs and delegated-authority underwriters, with an embedded AI agent layer. Intake: partial, via an embedded assistant. Underwriting: yes, with live risk scoring during quoting. Policy checking: not marketed. Note: strongest as a PAS-plus-AI stack rather than a dedicated intake or policy-checking specialist. Source: Facio website.

Salesforce Agentforce

What it is: A horizontal agentic layer on Salesforce, with insurance depth delivered through Financial Services Cloud and partners. Intake: partial, usually via system-integrator builds. Underwriting: partial and assistive. Policy checking: not marketed. Note: a fit if you're already standardized on Salesforce, but insurance-specific work often depends on partner implementation. Source: Salesforce website.

Is your MGA ready for agentic AI? A readiness framework

You don't need a data science team to deploy agentic AI, but a few signals tell you whether now is the right time. Use this quick self-check:

1. Volume pressure. Are submissions growing faster than you can hire underwriters? If yes, agentic intake pays for itself quickly.
2. Manual rekeying. Do underwriters retype data from PDFs into workbooks? That's the clearest agentic AI use case.
3. Carrier reporting strain. Are bordereaux and audits eating days each month? Agents standardize and validate that reporting.
4. E&O exposure at bind. Do missed endorsements or mismatched terms slip through? Policy checking closes that gap.
5. Integration reality. Do you want to keep your PAS? The right platform extends your stack instead of replacing it.

If you answered yes to three or more, your operation is ready. The adoption data agrees that most MGAs are moving now — 61.3% already use AI, and across insurance more broadly, Gallagher found 63% of firms have operationalized AI with an expected 28-month payback. Leaders evaluating organizational readiness may also want our agentic AI guide for the insurance CIO.

Scale your MGA workflows with FurtherAI

For MGAs that want intake, underwriting, and policy checking handled by one insurance-native platform, FurtherAI is the clearest choice. Our forward-deployed engineers work directly with your team, so you see results in weeks rather than quarters — the model Upland's CTO credited for getting his team to value fast. You keep your systems, your carriers get cleaner reporting, and your underwriters get their day back.

“After evaluating several vendors, we chose FurtherAI for its performance, insurance expertise, and partnership approach. The forward deployed engineer model makes a big difference — they work directly with our teams and help us get results quickly and we are able to both learn and iterate.” — Doug Alexander, CTO at Upland Capital Group

To discuss how MGAs may scale with FurtherAI, book a demo. 

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REFERENCES 

^{Accenture. "Why Underwriters Don't Underwrite Much." Accenture. insuranceblog.accenture.com}

^{Capgemini Research Institute. "Unleashing Growth: The Evolving Role of Underwriters (World Property and Casualty Insurance Report 2024)." Capgemini. capgemini.com}

^{Cytora. "Digital Risk Processing." Cytora. cytora.com}

^{Duck Creek Technologies. "Agentic AI Platform." Duck Creek Technologies. duckcreek.com}

^{Facio. "FacioMGA." Facio. facio.io}

^{FurtherAI. "Customer Stories." FurtherAI. furtherai.com}

^{Gallagher Bassett. "MGA Market Pulse: Key Insights for 2026." Gallagher Bassett. insurers.gallagherbassett.com}

^{Insurance Business. "Gallagher: AI Goes Mainstream but Insurers Face Skills, Risk, and Coverage Gaps." Insurance Business. insurancebusinessmag.com}

^{mea Platform. "Intelligent Underwriting." mea Platform. meaplatform.com}

^{Reinsurance News. "Conning Estimates US MGA Premiums Reached $128bn in 2025." Reinsurance News. reinsurancene.ws}

^{Salesforce. "Property and Casualty Insurance Software." Salesforce. salesforce.com}

^{Sixfold. "AI Underwriter." Sixfold. sixfold.ai}

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DISCLAIMER 

This article is for general informational purposes only and does not constitute legal, regulatory, compliance, underwriting, or other professional advice. The content reflects information available as of the date of publication, and FurtherAI undertakes no obligation to update it as laws, regulations, or AI technologies evolve. 

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