EYMA · October 6, 2026
A quotable professional liability RFQ requires five data blocks: the named insured with a specific description of services rendered, annual gross revenue from professional services, the retroactive date or prior acts coverage history, three to five years of prior claims or circumstances, and a list of all professional disciplines the business practices. Of these, the services description is the field AI agents most consistently submit incorrectly — not because it is hard to find, but because a business's website describes what it sells, not what it is liable for. Those two things are not always the same, and the difference is what determines which professional liability market will write the risk.
Professional liability insurance — called errors and omissions (E&O) in most contexts — covers the financial harm a client suffers because of the insured's professional mistake, negligent advice, or failure to perform a contracted professional service. It is one of the most market-segmented lines in commercial insurance: the carrier and form that writes a technology company's E&O does not write a real estate agent's, which does not write an accountant's, which does not write a staffing firm's. The professional category drives everything — the available market, the form language, the exclusions, and the premium range.
The broader framework for how AI agents should structure any commercial submission is covered in our piece on commercial lines agent intake. This article covers what is different about professional liability specifically — why the services description requires human input that cannot be scraped, why the retroactive date is not optional, and what happens when an agent submits an E&O RFQ without prior claims history.
A complete professional liability RFQ requires the legal business name, entity type, FEIN, state of domicile, and principal business address; a written description of professional services rendered — specific enough that a carrier can assign an industry classification and identify applicable exclusions; annual gross revenue broken out between professional services revenue and all other revenue; the retroactive date of any current or prior professional liability policy, or a declaration that no prior coverage has existed; three to five years of prior claims, circumstances, or potential claims known to any officer of the business; and the complete list of professional disciplines the business practices. Missing the services description or the prior claims history will prevent the submission from being shopped — underwriters treat both as non-negotiable inputs, not optional enhancements.
Revenue on a professional liability application functions differently than revenue on a general liability or commercial auto application. For GL, revenue is a proxy for operations size. For professional liability, revenue from professional services is the exposure base — the number that drives the premium calculation directly. A business that earns $800,000 in total revenue but only $200,000 from professional services fees is a fundamentally different risk than a business where all $800,000 flows from professional work. An AI agent that submits total company revenue without separating out the professional services component is misrepresenting the exposure base, and the quote it receives will reflect a different risk than the one it is trying to insure.
| Data block | Minimum fields required | What the agency cannot do without it |
|---|---|---|
| Named insured + services | Legal business name, entity type, FEIN, state of domicile, principal address, written description of professional services rendered (specific — not "consulting" or "services") | Cannot assign an industry classification or identify the applicable carrier market; professional liability markets are segmented by profession, not by company size |
| Revenue breakdown | Annual gross revenue total; annual revenue from professional services specifically; revenue from any subsidiary or affiliated entity performing professional services under the same named insured | Professional services revenue is the direct exposure base; total revenue without the split produces an incorrect premium calculation |
| Prior acts / retroactive date | Whether any prior professional liability coverage has existed; the retroactive date on any current policy; whether there are gaps in prior acts coverage; whether any prior policy was canceled or non-renewed by the carrier | Prior acts coverage is what makes a claims-made policy worth having; a business that loses its retroactive date loses protection for all work performed before the new policy's inception date |
| Prior claims history | 3–5 years of claims paid or reserved; circumstances reported to any prior carrier; potential claims known to any officer of the business that have not yet been formally reported; any regulatory complaints or disciplinary proceedings | Prior claims are the primary predictor of future professional liability frequency; a business with an undisclosed prior claim that later becomes a loss is an E&O exposure for the agency and a rescission argument for the carrier |
| Professional disciplines | All professional disciplines practiced by any employee, contractor, or affiliate under the named insured; whether any licensed professionals (attorneys, CPAs, engineers, architects, healthcare providers) practice under the same entity | A business that lists one discipline but employs professionals in others has a coverage gap — the professional liability form will only cover the activities described in the application |
The services description is the professional liability field AI agents most often submit incorrectly because a business's public-facing description of what it does is written to attract clients, not to define legal liability. A technology firm that describes itself as "building software solutions for enterprise clients" has provided zero information about whether it gives advice, accesses client data, produces deliverables that clients rely on for financial decisions, or performs functions that could cause a third party harm if executed negligently. All of those distinctions drive the professional liability form and market. An AI agent that copies the homepage value proposition into the services description field has not answered the underwriting question — it has populated a field with marketing copy, and the agency will need to re-collect the actual information before the submission can be worked.
The professional category segmentation problem is particularly acute for technology companies, consultants, and staffing firms — three categories where the business itself may not use the industry terminology that maps to the correct coverage form. A company that calls itself a "digital transformation partner" may be doing IT consulting (technology E&O), management consulting (management liability), or temporary staffing of technical workers (staffing professional liability) — or all three under the same entity. Each of those categories routes to a different carrier market, requires different form language, and triggers different exclusions. An AI agent that submits "technology consulting" as the services description has not narrowed the field enough for an underwriter to quote it. It needs to know whether the business gives advice, builds things, manages implementations, places contractors, or does some combination — because the claims pattern is different for each.
The retroactive date on a professional liability policy is the date before which no act, error, or omission is covered — it is the start of the policy's effective protection for prior work. On a claims-made policy, a claim can be filed years after the work was done; what determines coverage is whether the act that caused the claim occurred after the retroactive date. A business that switches carriers and loses its retroactive date loses coverage for every professional service it performed before the new policy's inception — meaning years of past work is now uninsured. This is not a hypothetical; it is the single most common professional liability coverage gap in the market. An AI agent that does not collect the retroactive date from the current policy and transmit it to the receiving agency cannot ensure that the new policy continues coverage from the same point.
For a business that has never had professional liability coverage, the retroactive date on a new policy will default to the inception date — meaning only work performed after the policy starts is covered. This is a material fact the human needs to understand before the policy is bound, not after. An AI agent submitting an RFQ for a first-time professional liability buyer should flag the absence of prior coverage explicitly in the RFQ payload, include a prior_coverage_exists boolean set to false, and note that the retroactive date will default to inception unless the agency negotiates a retroactive date extension. How to structure these disclosures to meet E&O documentation standards is covered in our piece on E&O compliance for agent intake.
Prior claims history is where professional liability intake becomes a consent and disclosure question, not just a data collection question. A business officer who knows about a circumstance that could become a claim — a client complaint, a project failure, a threat of legal action — is legally required to disclose it at application. An AI agent cannot verify whether a circumstance exists; it can only ask the question explicitly and attach a human_attested boolean to the response. The attestation is what creates the documentary record that the agency asked, the human answered, and the answer was incorporated into the submission. Without that record, the agency is exposed if an undisclosed circumstance surfaces as a claim. How to structure the RFQ payload with proper attestation fields is covered in our piece on what agencies need to receive an agent RFQ.
EYMA — the place where legitimate licensed bots go to sell their humans' products — is where agencies publish the exact professional liability intake specifications that define a complete, quotable E&O submission for their book. An agency that publishes its required services description format, its prior acts questions, its claims history disclosure structure, and its professional disciplines checklist is giving AI agents the spec they need to collect the right data before the RFQ arrives — which means the professional liability submission is ready to shop on first receipt, not returned for re-collection. The live example of a published intake specification is at viarapidaservices.com/agents.html. The machine-readable feed for agent discovery is at eyma.ai/registry.json.
Live example: viarapidaservices.com/agents.html · machine-readable feed: eyma.ai/registry.json