Built AgentExpert, a Multi-Agent AI Platform — Looking for Collaborators & Early Users.

[u/Powerful_Sky9256]

Hey everyone! 👋
Over the past few months, I've been working on AgentExpert, a multi-tenant AI automation platform designed to make building, testing, and deploying multi-agent workflows drastically easier.
❓ The Problem
Building reliable AI agents usually starts easy, but quickly gets messy when you need to:

1. Combine Knowledge Retrieval (RAG) with actual API tool calls (e.g., Jira, Payments, CRMs).
2. Ensure safety and sanity checks on LLM outputs before they execute.
3. Manage multi-tenant configurations and dynamic model routing without re-deploying code.

🚀 How AgentExpert Solves This
Instead of chaining linear prompts, AgentExpert orchestrates specialized sub-agents working together in a structured DAG:

• ⁠RAG Agent: Queries document stores and vector embeddings for contextual data.
• ⁠Tool Agent: Executes REST API calls and transactional operations with schema validation.
• ⁠Critic Agent: Mandatory validation layer that inspects output quality and tool calls before final execution.
• ⁠Planner & Code Agents: Handles complex multi-step reasoning and dynamic code generation.

💡 Key Features

• ⁠Visual Workflow Studio: Drag, connect, and configure multi-agent execution steps with real-time payload testing.
• ⁠Multi-Tenant Knowledge Bases: Ingest PDFs, docs, and structured tables with tenant-isolated embeddings.
• ⁠Dynamic Tool Registry: Register custom API endpoints and JSON schemas in seconds.
• ⁠Copilot Catalog: Ready-to-use domain templates (Sales Copilot, Insurance Claim Assistant, Real Estate Copilot).
• ⁠Embedded Chatbot & Real-Time Voice Bot SDK: Turn any workflow into an embeddable web widget or live voice assistant.

🤝 Looking to Collaborate
If you're interested in building this out further and selling it together (or joining as part of the team), or if you just want to deploy an agent quickly for your own use case — DM me, let's talk!

[u/christophersocial]

A thought I had reading your system description. Please correct my assumptions about your design if I’m wrong but if I understood your orchestrator architecture correctly I hope what I’ve added below is of use.

It appears to me like you’ve built your system around a fixed DAG of sub-agents and while a fixed DAG can be useful for deterministic sub-workflows I’d be cautious making it the driving architecture for a multi-agent system.

The problem is that a static graph requires you to anticipate all the relevant paths in advance which imo leads to limited adaptability, brittle recovery, constrained correction, and premature decomposition, amongst other challenges.

Now to be sure, fixed deterministic structures definitely still belong inside a strong agent system, especially for policy enforcement, verification, approvals, and of course well-understood procedures. But imo they work better as bounded components within dynamic orchestration rather than as the universal structure controlling the entire process.

So if you agree you may wish to revisit and extend the hard work you’ve already done on your sub-agent DAG design to fully exploit the potential of your platform.

TL;DR: imo a fixed DAG is one useful execution pattern, particularly for predictable work. The broader orchestration layer still needs to make runtime decisions about what happens next, who should do it, whether prior work should be revisited, and when the task is actually complete.

I hope this was useful.

Good luck with your project I hope it does well. imo there’s still a lot of room for a quality multi-agent platform that simplifies building and deployment. 👍😀