“Vibe coding”, describing what you want in plain English and letting an AI assistant generate the code, has captured the imagination of the software world for good reason. A single person can stand up a prototype in an afternoon that would have taken a team a few months just a few years ago. For consumer apps, internal dashboards, and throwaway tools, it’s a huge unlock.
But for financial service enterprises, such as insurance carriers, brokers, and wealth managers, the honeymoon ends fast. The same properties that make vibe coding feel magical in a greenfield project turn into liabilities the moment you try to use it for new client account openings, advisor transitions, submission intake for underwriting, and other complex workflows.
The answer isn’t to retreat to waterfall IT projects. It’s to move up a level of abstraction. Instead of vibe coding the implementation, you let AI agents define the workflow and execute it on hardened, purpose-built building blocks. That’s the distinction between vibe coding and AI-defined workflows, and it’s the difference between a demo and a system you can actually run a business on.
Where vibe coding breaks in financial services
Vibe coding optimizes for getting from idea to running code as fast as possible. In an unregulated context, the cost of a hallucinated API call, a forgotten edge case, or a brittle integration is an afternoon of debugging. In financial services, the cost is a failed audit, insurance risk exposure, NIGO’ed submissions, or a client relationship you can’t get back.
A few specific failure modes show up again and again:
- It’s not consistent where consistency is required. Underwriting decisions, suitability checks, RMD calculations, and beneficiary routing aren’t creative tasks. They have correct answers, and those answers must be reproducible on the same inputs a year from now. LLM-generated code can silently drift between runs or regenerations, and “it worked when I demoed it” is not a control a chief compliance officer will accept.
- Vertical integrations require context & relationships. Connecting to a custodian, policy management system, or agency management system isn’t just a matter of pointing an agent at OpenAPI documentation. Each system has its own file formats, sandbox quirks, reconciliation expectations, and certification processes. An agent can draft plausible-looking code against any of them. Whether that code will actually clear a NIGO check or pass a custodian’s security requirements is a different question entirely. Beyond the integration itself, even getting access requires demonstrated success within the industry.
- Security and data handling are not defaults. PII, PHI, and non-public material information need to live inside controlled boundaries with encryption, tokenization, least-privilege access, and documented data flows. Vibe-coded prototypes routinely log PII to the console, stash API keys in client-side code, send full SSNs into third-party LLMs, and treat “works on my machine” as a deployment strategy. For a broker-dealer subject to SEC Rules 17a-3 and 17a-4, or a carrier under state DOI examination, these aren’t bugs. They’re reportable events.
- There’s no audit trail of the reasoning. When a regulator asks why a specific decision was made on a submission or why a specific document was generated with specific language, “the AI wrote the code that did it” is not a defensible answer. Vibe coding produces artifacts (the code) but not evidence (what decisions the system made, when, why, and on whose behalf).
- Business users can’t maintain it. The prototype that a product manager vibe-coded on a Friday becomes tech debt on Monday. The moment a field needs to change, a new state’s disclosures need to be added, or a carrier updates its appetite, you’re back in a codebase that only the AI assistant (hopefully) can read. In an industry where ops and compliance teams own the business logic, code as the source of truth is a staffing and continuity problem, not a feature.
- Documents are messy. A large share of financial services workflows centers around documents - insurance policies, quotes, investment statements, new-account paperwork, disclosures, beneficiary designations. These have to be filled accurately, signed in the right order, stored with WORM guarantees, versioned, and retrievable on demand.
What AI-defined workflows do differently
AI-defined workflows keep the part of vibe coding that’s genuinely valuable — natural-language expression of intent, fast iteration, the ability for a business user to describe a process and see it come to life — and move the execution onto infrastructure that was built for regulated work.
The shift is conceptual. Instead of asking an agent to write code that implements a workflow, you ask it to plan and configure a workflow. The agent’s output isn’t a codebase; it’s a specification:
- What information needs to be collected, from whom, in what order, and under what conditional logic
- What the end-user-facing layout should look like at each step, including disclosures, consent language, and branding
- Which upstream and downstream systems the workflow needs to talk to and what data flows in each direction
- Which inbound documents need to be intelligently extracted (driver’s licenses, trust documents, statements from prior firms, ACORD submissions, loss runs) and how the extracted values map into the workflow’s data model
- Which outbound documents need to be generated, with which template, populated from which fields, routed for signature in which order, and stored under which retention policy
- What data mappings translate internal field names to the naming conventions each integrated system expects
That plan then executes on building blocks that don’t care about vibes. They care about throughput, error handling, observability, compliance, and maintainability:
- Vertical integrations with custodians, policy management platforms, agency management systems, and carrier APIs that have been built, tested, and certified once - and are reused across every workflow, rather than regenerated per project.
- Intelligent document processing (IDP) with confidence scores, reviewer approval workflows, and human-in-the-loop correction paths, so extraction accuracy is a managed metric.
- Workflow management with deterministic branching, retries, SLAs, and full audit logging.
- Data mapping that is declarative, versioned, and visible to business users, not buried in generated code.
- Document automation with autofill into Word, PDF, Excel, and PowerPoint templates, e-signature routing, WORM storage aligned to SEC 17a-4, and granular permission and versioning controls.
The critical property is that the agent defines the workflow, but it does not define the building blocks . Every workflow is composed of the battle-tested building blocks. This is what makes the output reviewable, auditable, and — most importantly — maintainable by non-engineers.
Flexibility without the liability
The practical result is the best of both modes. An underwriting leader at a carrier can describe a new commercial lines submission intake in natural language and see a working version of it in the time it used to take to schedule the kickoff meeting with IT. An advisor ops lead at an RIA can stand up a new custodian transition workflow that pulls statements, extracts positions and cost basis, runs identity resolution against the existing household, and generates the ACATs and new account paperwork in the correct order.
But what they produce isn’t a vibe-coded artifact that lives in one person’s IDE. It’s a configured workflow on managed infrastructure. When a state changes a disclosure requirement, a compliance analyst edits the template. When a custodian updates a file spec, the integration is updated once, centrally, for every workflow that uses it. When an auditor asks for evidence, it’s already been captured. When the original builder leaves the company, the next person can read the workflow, understand it, and change it.
Vibe coding asked a good question: why should it take a team of engineers to express a business process? AI-defined workflows capture the balance. The expression can be simple, but the execution cannot.
For the insurance and wealth firms that move from the first idea to the second, the payoff isn’t just faster shipping. It’s the ability to keep shipping safely, auditably, and at enterprise scale long after the demo ends.
