Most no-code AI agent builders demo well. The mismatch shows up in production, when an agent answers a customer wrong, an auditor asks where a number came from, or a model price doubles overnight. Seven questions separate a builder that survives that from one that does not. Ask each vendor directly and test the answers on your own data.
1. Does every answer cite its source?
The single most important property. A production agent points each output to the document, section, and version it used, and says so when the answer is not in your data. Test it: feed the agent a question your documents do not answer and see whether it admits the mismatch or invents a confident reply. The ones that invent are the ones that embarrass you later.
2. Can you require named-owner sign-off before an action lands?
For anything that touches customers, contracts, regulators, or money, you want a person in the path: the agent drafts, a named owner signs off, then it sends or writes the record. Check whether approval is a firm guarantee on the outputs that matter or a setting a workflow can switch off under deadline pressure. A control that can be disabled is not a control.
3. Is there an audit trail you can export?
Every question, draft, model call, and approval click should be written to a log your compliance team can replay and export. Ask to see the export format. If the answer is “we can pull logs and get back to you,” that is not an audit trail; it is a support ticket.
4. Is your data isolated from other customers?
Your sources, your rules, and your outputs should be separated from every other tenant at the database layer, so nothing leaks across organizations. Ask how isolation is enforced and verified. For regulated buyers this is a procurement gate, not a nice-to-have.
5. Can you bring your own AI model and switch later?
The model is the part of your stack most likely to change. A builder that treats the model as configuration lets you move when pricing, capability, or regulation shifts, without rebuilding the agents. One tied to a single provider turns a model change into a migration project. Confirm which models are supported and what switching actually involves.
6. Who can actually build on it?
“No-code” should mean a non-technical operator can stand up a working agent once the data and guardrails are set. Watch a real operator build a workflow in the demo, not an engineer. If the person composing the agent needs to understand prompts, schemas, or APIs, the no-code claim is thin.
7. Does it fit your industry’s constraints?
A general-purpose builder may move fast and leave compliance to you. A regulated team needs source traceability, pre-approved guardrails, hosting options that satisfy data residency, and security documentation procurement will accept. Ask whether the vendor has shipped in your industry and what the deployment options are.
How to run the evaluation
Pick one narrow workflow you understand well. Connect a small, real slice of your data. Build the same workflow on each builder you are considering, and inspect the outputs against questions 1 through 3: sources cited, approval enforced, audit exportable. A four-to-six-week pilot on one workflow tells you more than any feature matrix. The builders that pass on your data are the short list.
Where Clarm fits
Clarm is built for the governed end of this list. Source citations, the approval gate, the audit trail, tenant isolation, and bring-your-own model are part of the substrate rather than features added on top, and it has shipped in banking, healthcare, and other high-trust teams. See the Atlas page for how it works, or book a pilot discussion to run the evaluation above on your own data.