Logic Agents
Overview
Logic Agents are a suite of AI agents that read the logic you build with Flowon on Microsoft Dataverse and turn it into something you can act on: enterprise documentation, governance reports, process intelligence, and test suites. They read business processes, logic flows, logic blocks, schedules, events, configurations, and recipes, and each agent produces one specific, high-value deliverable.
Every agent connects to your environment through the Flowon MCP server, reads the project's artifacts on behalf of the signed-in user, and combines deterministic analysis with large-language-model reasoning. Each ships as a self-contained container with its own web application, and each is strictly read-only: the agents analyze what you have built, and they never change it. That makes them safe to point at production.
The four agents
| Agent | What it produces | Use it to |
|---|---|---|
| Logic Insights | Process intelligence: health scores, metrics, simulation, standards audits, and AI redesign | Understand how your processes really perform, and improve them with evidence |
| Logic Context | Enterprise Business Requirements Documents in plain business language | Document a project for handover, audit, or onboarding, on demand |
| Logic Guard | A governance report: per-artifact issues with severity and suggested fixes | Enforce a consistent quality bar before you publish |
| Logic Assurance | Schema-accurate test suites grounded in live Dataverse metadata | Protect your logic from regressions when it changes |
Each agent is licensed and shipped independently. Install only the ones you need. They do not depend on each other.
A shared foundation
All four agents are built on the same platform and follow the same shape, so once you know one, you know the pattern.
- Connect. Each agent authenticates the incoming user (Entra ID or ADFS) and calls the Flowon MCP server on behalf of that user, so it sees exactly the projects and data the user is allowed to see.
- Read. It reads the project's artifacts through the MCP read tools. Nothing is ever written back to your project.
- Analyze. A fleet of specialist workers does the job. Deterministic work such as metrics, rule checks, and metadata validation runs in code. Language work such as prose, findings, recommendations, and test cases runs through a configurable model.
- Stream. Results stream back to the browser over Server-Sent Events as each part completes, so long-running jobs render progressively instead of blocking.
- Persist. Results and per-project configuration are stored in Redis in production, or in memory for local evaluation.
What they have in common
- One image, one endpoint per agent. Each agent is a single container that serves both its REST API and its web application on port 8080, with a health probe at
/health. - Per-project configuration. Every agent has tunable, per-project settings stored as sparse overrides. You change only what you need, and everything else keeps tracking product defaults across releases.
- Provider-flexible reasoning. Claude on Azure AI Foundry, Azure OpenAI, OpenAI, or Anthropic, chosen by configuration.
- Secure by default. Incoming requests require authentication, and downstream calls to the MCP server use the on-behalf-of flow, so the agent acts as the user, not as a shared service identity.
- Read-only and safe for production. The agents call only the MCP read tools. They never create, update, or delete anything in your project.
Getting started
- Read the agent pages above to decide what you need.
- Follow the Installation guide to run the container images, connect them to your Flowon MCP server and a model, and provision Redis and an Entra ID app registration for production.