How to Vet Agentic AI Vendors for Regulated Industries [Checklist]

1. Deployment Model: Does It Run Inside Your Walls?

What to vet:

Make sure the platform can be deployed within your organization’s secure environment, not run in someone else’s cloud, where you lose visibility and control. For industries with strict compliance rules, like finance or insurance, this isn’t optional.

You need to own your data, your models, and your logs to meet audit demands and avoid regulatory risk.

Green flags:

• SOC2 Type II
• VPN-gated access
• Client-managed encryption keys

Red flags:

• Shared multi-tenant clouds
• Vendor-managed logs
• Promises of “trust us” security

Our agentic AI platform, AgentFlow, operates in private VPCs or fully isolated on-prem environments for 100% of deployments.

2. Workflow Coverage: Does It Automate End-to-End Processes?

What to vet:

Look for systems that handle entire workflows from intake to decision, not just isolated tasks. For example, a true agentic system should be able to take in claims documents, assess them, escalate edge cases, and generate the final approval memo. If it stops halfway, you’re stuck stitching tools together.

Green flags:

• Agent orchestration across ingestion, reasoning, and execution
• Prebuilt flows for finance and insurance operations

Red flags:

• Chatbot UIs posing as agentic systems
• Point solutions that can’t interoperate

AgentFlow’s orchestration spans Unstructured AI, Document AI, Decision AI, and Report AI agents in live deployments.

3. Auditability: Can You Trace Every AI Action?

What to vet:

You need to know exactly how a decision was made, by whom, and when. Whether it’s a declined loan or a denied claim, every step should be logged, reviewable, and explainable. If the AI goes rogue or gets something wrong, you must be able to prove what happened and why.

Green flags:

• Role-based access controls
• Nested JSON logs for decision traces
• Configurable confidence thresholds with automatic human escalation

Red flags:

• Black-box decisions
• No support for audit workflows or compliance dashboards

AgentFlow provides GPG-signed model commits, confidence-based thresholds, and full audit logs by default.

4. SME Control: Can Business Users Tune and Supervise Agents?

What to vet:

Ask whether your subject matter experts, not just engineers, can monitor, adjust, and improve how the AI works. The people who know your business best should be able to supervise edge cases, provide feedback, and guide the system without needing to code.

Green flags:

• Schema builders and per-decision override tools
• Agent coaching workflows for non-technical users

Red flags:

• Engineering bottlenecks for simple tuning
• Tools gated by Python skills or CLI-only configs

AgentFlow embeds business configuration tools that keep domain experts in the loop every day, not just during setup.

5. Domain Fit: Has the Vendor Pre-Built for Your Workflows?

What to vet:

Generic platforms often need months of configuration just to understand your terminology or document formats. A better option?

Systems built with your domain in mind, pretrained on insurance policies, credit reports, or underwriting rules. That’s the difference between a prototype and production.

Green flags:

• Loan origination, claims adjudication, KYB/KYC, and reinsurance treaty workflows pre-modeled
• Templates aligned to CECL, NAIC, and GDPR guidance

Red flags:

• General-purpose agent builders
• “Train it yourself” frameworks with no domain guardrails

AgentFlow includes 100+ domain-specific templates and vertical playbooks out of the box.

6. Governance & Lifecycle: Is the AI Being Maintained?

What to vet:

AI isn’t “set it and forget it.” It needs ongoing oversight, like regular updates, error checks, and version control. If the vendor can’t show you how the system gets smarter over time, or how they manage change, you’ll end up with a black box that drifts off course.

Green flags:

• Immutable logs
• A/B testing with statistical significance
• Confidence-score thresholds for escalation

Red flags:

• Static models
• No visibility into long-term accuracy or audit history

AgentFlow logs every execution, signs model versions, and supports full rollback.

7. Support & Setup: Who’s Actually Standing Up the Solution?

What to vet:

The real question is: who’s doing the work? A solid vendor doesn’t just hand you a product and walk away.

They partner with your team, guide setup, provide fast support, and get you from pilot to production on a clear timeline. Anything less is a liability.

Green flags:

• 6–8 week VPC or on-prem setup
• 24/7 MLOps support with SLAs under 2 hours for critical issues

Red flags:

• No structured onboarding
• Self-serve setup with vague timelines

AgentFlow pairs each deployment with implementation engineers and defined rollout milestones.

8. Feedback Integration: Does the System Improve Over Time?

What to vet:

Ask how the system learns. Can your teams provide feedback when they get something wrong?

Does it adapt to new scenarios or changing business rules? Without a feedback loop, even the best model will go stale, and fast.

Green flags:

• Feedback dashboards by agent
• Integrated labeling and retraining workflows

Red flags:

• Static wrappers on foundation models
• No pathway for iterative tuning

AgentFlow routes feedback into retraining pipelines with quarterly performance reviews baked in.

9. Implementation Model: Do They Embed With Your Teams or Just Hand You a Tool?

What to vet:

A checklist won’t get the job done. You need a team that embeds with your claims, credit, or underwriting teams, learning how your business runs so the system can reflect real workflows, not generic ones.

True partners act more like consultants than software resellers.

Green flags:

• Forward-deployed engineers (FDEs)
• On-site configuration sessions
• Embedded workshops

Red flags:

• “Tool not a service” ethos
• Support via ticket portals only

At Multimodal, we run Palantir-style FDE deployments until the agent outpaces a human peer.

10. End-User Focus: Is It Built for Business Teams or Just IT?

What to vet:

Can your frontline teams actually use the product, or does everything have to go through engineering?

AI that only works in the hands of developers won’t scale. Your supervisors, analysts, and operators should be able to understand decisions and make changes themselves.

Green flags:

• No-code config, agent dashboards, and SME-friendly review workflows

Red flags:

• CLI-first setups
• Engineer-only tuning paths
• No visibility for business owners

AgentFlow provides role-based portals: Monitor (engineering), Review (SMEs), and Command (execs).

Choose Only AI You Can Trust in Production

Vendor selection isn’t about potential, it’s about production proof.

Ask for:

• Real audit logs
• Documented confidence thresholds
• Workflow execution traceability

If a vendor can’t pass a compliance test, it doesn’t belong in your stack.

Ask every vendor: “Can your regulator trace this workflow from input to decision?”

Ready to Evaluate Real Production-Ready Agents?

AgentFlow is already powering mission-critical workflows across leading financial and insurance organizations.

Book a demo to see how AgentFlow helps business and IT teams move from pilot to production in under 90 days, and how it fits directly into your existing systems.