COMPARISON

FlozentAi vs building it yourself

Every team that adds an agent to real software writes roughly the same layer. The demo takes a week. The layer underneath it — identity, scope, approval, isolation, audit — is the part that takes two quarters, and almost none of it is about the model.

See the demo Read the model

Side by side

Dimension	FlozentAi	In-house build
Getting a demo working	Import an API, publish an action, embed the widget.	Fast. A model, a function and a prompt gets you there in days.
Per-customer isolation	Row-level security in the database; a run is bound to one tenant.	Application-level filtering that every future query must remember. One miss is a breach.
Credential handling	Opaque references resolved in the execution layer; no credential is ever stored.	Your own vault integration, plus care that keys never reach prompts or logs.
Human approval	Built in, bound to a digest of the exact proposal, with expiry.	A queue, a UI, state machines for expiry, and the subtle part: binding approval to a specific proposal rather than a category.
Audit	Identity, context, policy decision, provider request and result per run.	Usually logs. Enough to prove something happened, rarely enough to explain why.
Changing the action list	Re-import produces new revisions for review; published versions stay pinned.	A migration, and a way to ensure in-flight runs do not change under you.
Where the effort goes	Deciding what agents should be allowed to do.	Building the machinery that makes that decision enforceable.

When in-house build is the right call

This section is not a formality

A comparison where the other option never wins tells you nothing except that the author is selling something. These are the cases where we would tell you not to use this.

You have one tenant and one internal user group. Most of this layer is solving multi-tenancy you do not have.
The agent is strictly read-only against non-sensitive data. Approval and credential isolation matter much less.
Your governance requirements are unusual enough that a general-purpose model of tenants and approvals would fight you.
You are exploring. Prototypes should not carry a platform.

Questions

Is this not just a wrapper around a model API?

The model call is a small part of it. What is between your application and the model — scoped identity, a reviewed action list, credential resolution, approval binding and the audit trail — is the product. You bring the model.

Can we migrate off it later?

Your API definitions, action reviews and configuration are yours and exportable. What you would rebuild is the enforcement layer, which is the part you would have had to build anyway.

Other comparisons

vs Direct model API

FlozentAi vs calling a model API directly

Where calling OpenAI, Anthropic or Gemini directly is exactly right, and the specific point at which it stops being enough.

Read it vs Agent framework

FlozentAi vs an agent framework

Orchestration libraries and governance layers solve different problems. Why you may well want both.

Read it

Build beyond the expected.

Create a workspace, register an application, and import your first API.

Create your workspace Read the docs

Side by side

Dimension

FlozentAi

In-house build

Getting a demo working

Import an API, publish an action, embed the widget.

Fast. A model, a function and a prompt gets you there in days.

Per-customer isolation

Row-level security in the database; a run is bound to one tenant.

Application-level filtering that every future query must remember. One miss is a breach.

Credential handling

Opaque references resolved in the execution layer; no credential is ever stored.

Your own vault integration, plus care that keys never reach prompts or logs.

Human approval

Built in, bound to a digest of the exact proposal, with expiry.

A queue, a UI, state machines for expiry, and the subtle part: binding approval to a specific proposal rather than a category.

Audit

Identity, context, policy decision, provider request and result per run.

Usually logs. Enough to prove something happened, rarely enough to explain why.

Changing the action list

Re-import produces new revisions for review; published versions stay pinned.

A migration, and a way to ensure in-flight runs do not change under you.

Where the effort goes

Deciding what agents should be allowed to do.

Building the machinery that makes that decision enforceable.