Conversational AI Agent Development

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Multi-Agent AI Business System for MIR Fine Jewelry ❇️ Hi My Friend, I hope you are doing well. I’ve reviewed your project requirements and see you are looking for a developer to create a multi-agent AI business system. You don’t need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects for AI systems. I will design a modular system using Python, FastAPI, and PostgreSQL, ensuring it meets all your specifications within budget. ➡️ Why Me? I can easily build your multi-agent AI business system as I have 5 years of experience in Python development, FastAPI, and database management. My expertise includes API integration, workflow automation, and system architecture. I also have a strong grip on relevant technologies like LangGraph, MCP, and A2A architecture, ensuring a robust solution. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I am looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Python Development ✅ FastAPI ✅ PostgreSQL ✅ API Integration ✅ Multi-Agent Workflows ✅ System Architecture ✅ Workflow Automation ✅ Database Management ✅ Persistent Memory ✅ Human Approval Processes ✅ Inventory Management ✅ Customer Management Waiting for your response! Best Regards, Zohaib

@corpmember29

HEY! I UNDERSTAND YOU NEED A TRUE MULTI-AGENT AI BUSINESS SYSTEM FOR MIR FINE JEWELRY, NOT A BASIC CHATBOT. I have 12+ years of experience in Python, FastAPI, PostgreSQL, LLM integrations and AI automation. I can build a modular, API-first architecture with a central Commander Agent coordinating specialized agents for sales/CRM, inventory/pricing, suppliers, quotes/orders, communications, invoices, lead prospecting and scheduling. Key implementation: • Commander/Orchestrator using LangGraph or Google ADK • MCP + A2A-based agent communication • Persistent business/customer/supplier memory • Real-time integration with existing business APIs/data • Human approval workflows for prices, discounts, POs, payments and messages • Role-based access, audit logs and secure API design • Modular agents that can be extended without rebuilding the core • PostgreSQL for structured data and agent state/history FLOW: Commander → Specialized Agent → Business Data/API → Validation → Human Approval → Action → Audit/Memory. Recommended stack: Python + FastAPI + PostgreSQL + LangGraph/Google ADK + MCP/A2A + REST/WebSocket APIs I’m ready to review your existing architecture/data structure and define milestones for implementation. Thanks CHIRSTIAN

@Hubready

Hello!, I am a US-based senior software engineer, and this project stands out because it is not a simple chatbot, it is a multi-agent AI business system. The real challenge is making the agents, APIs, workflows, and decision logic work together reliably and actually support business outcomes. I can help you build this in a practical way: 1. Define the agent roles, handoff logic, and success criteria 2. Design a clean Python/API architecture 3. Build the AI workflow automation and integrations 4. Test behavior, edge cases, and response quality before launch I pay close attention to prompt structure, tool routing, state handling, and failure recovery, because those are usually what make or break these systems. My goal is to give you something intelligent, stable, and ready for real use, not a demo that breaks under pressure. Relevant work includes: - An AI support assistant for a SaaS dashboard - A Python workflow automation system for internal ops - A multi-step lead qualification agent for a service business - An API-driven knowledge assistant for a private client portal Could you please clarify the following questions to help me better understand the project? 1. What exact business tasks should the multi-agent system handle first? 2. Do you already have preferred AI models, APIs, or tools to integrate? 3. What does success look like for phase one, and do you want an MVP first? If this sounds aligned, I’d be happy to discuss the best architecture and move quickly.

@ahmadayaz

I have extensive experience with Python, FastAPI, PostgreSQL, Google ADK, MCP, A2A architecture, APIs, and multi-agent workflows, aligning well with MIR Fine Jewelry's goal of implementing a comprehensive AI business system. I will ensure the system incorporates actual business data, detailed customer and supplier histories, and human oversight for critical actions. Using a modular, API-first design with persistent memory and multi-agent workflows, the system will be scalable, secure, and efficient. By prioritizing scalability and readiness for expansion, the system will support easy integration of new agents and functionalities. Through MCP and A2A architecture, seamless communication between system components will be established. I am enthusiastic about discussing your project details further and collaborating closely to deliver a cutting-edge solution that fosters continual innovation and growth for MIR Fine Jewelry.

@sufyanasmat38

Hi, I can build this as a modular, API-first multi-agent AI system with a central Commander/Orchestrator coordinating specialized agents for sales, inventory, suppliers, quotes, communications, invoices, leads, and scheduling. I have strong experience with Python, FastAPI, PostgreSQL, REST APIs, AI integrations, and workflow automation, and can structure the system using LangGraph/Google ADK with persistent memory, MCP/A2A patterns, and clear human approval gates for sensitive actions. I’ll focus on reliability, traceability, and clean architecture so the system can evolve with MIR Fine Jewelry’s real business data.

@toriquldev123

Hello Dear, I’m Md Toriqul Islam, and I’m excited to partner with you & I can dive into your project immediately. I have rich experience in Python, FastAPI, PostgreSQL, AI agents, APIs, automation, persistent memory, and complex business workflows. I understand you want a modular multi agent AI system for MIR Fine Jewelry, led by a central Orchestrator coordinating sales, inventory, suppliers, orders, communications, invoices, leads, and scheduling. I am skilled in LangGraph, Google ADK, MCP, A2A architecture, REST APIs, database design, memory systems, and human approval workflows. I can build an API first architecture with secure business data access, agent coordination, auditability, and approval controls for sensitive actions. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@ShaikhAneesa

Hi there, I understand you need to build a production-ready multi-agent AI business system for MIR Fine Jewelry, not simply add a chatbot. The critical part is creating a central Commander that can coordinate specialized agents while maintaining persistent customer, supplier, inventory and transaction history, with strict human approval before sensitive business actions. I’m confident I can architect this as a modular, API-first system that can scale as new agents and integrations are added. My approach is to first map MIR’s business workflows, data entities, permissions, tools and approval boundaries. Next, I’ll design the Commander/Orchestrator and specialized agents for sales/customer management, inventory/pricing, suppliers, quotes/orders, communications, invoicing, lead prospecting and scheduling using Python, FastAPI, PostgreSQL and LangGraph/Google ADK as appropriate. Then, I’ll implement persistent memory, MCP/A2A communication, API integrations, workflow state, audit trails and approval gates for price changes, discounts, POs, payments and outbound communications. Finally, I’ll connect the system to MIR’s real business data, validate end-to-end multi-agent workflows and establish the modular foundation for future agents and integrations. Can I review your current business systems/APIs and a sample of the existing data early in Milestone 1 so I can design the architecture around the actual MIR workflows rather than assumptions? Warm Regards, Aneesa.

@Hrolgar

Building a system like this, the orchestrator and its memory need to exist before any of the specialist agents do, because every one of them is going to lean on the same session state and approval flow. So that is where I would start, get the Commander running with persistent memory and the human approval gate wired into the message bus itself, not as a check bolted onto individual agents later. LangGraph fits this well since you can model each agent as a graph node and the approval gate as a literal interrupt point that halts execution until a human clears it, which is a cleaner guarantee than a flag checked inside application code. The two agents I'd put in that first phase are the ones that touch revenue directly, sales and customer handling, then quotes and orders, so from day one the system is doing something a jewelry business actually feels, not just infrastructure. Both would talk to the orchestrator over the same A2A protocol the later supplier, invoicing and prospecting agents will use, so nothing about this phase gets thrown away when you extend it. Postgres holds conversation state and business data behind FastAPI, MCP wraps whatever tools each agent needs so a supplier agent or invoicing agent later just registers new tools against the same server rather than needing its own integration layer. 1500 over 12 days for that scope is workable as a starting number, though the real cost driver is going to be how much your sales and quoting logic actually needs to reason about, pricing rules, inventory lookups, how strict the approval gate needs to be before something goes out to a customer. I'll firm that up once I can see what "quote" and "order" actually touch in your current process. M1: orchestrator skeleton, Postgres schema, persistent memory, message bus with the approval-gate interrupt built in, $375, 3 days. M2: sales/customer agent wired to the orchestrator, handles inbound conversation and hands off structured requests, $375, 3 days. M3: quotes/orders agent, pricing and inventory lookups via MCP tools, approval gate enforced before anything is sent or committed, $450, 4 days. M4: integration pass across both agents through the orchestrator, logging, and a clean extension point for supplier/invoicing/prospecting agents to plug into next, $300, 2 days. Send me whatever you've got on your current sales and quoting workflow and I'll have a tighter number tomorrow.

@mubeenm5026

Hi, You need a modular multi-agent AI business system with a central orchestrator for MIR Fine Jewelry. For your project, our team will: • Design a Python and FastAPI architecture for the Commander agent and specialist AI Agents • Build AI Workflow Automation for sales, inventory, suppliers, quotes, orders, and scheduling • Connect PostgreSQL-backed persistent memory so customer and supplier history stays available • Implement approval gates for discounts, price changes, POs, payments, and communications • Structure API-first services with LangGraph or Google ADK, MCP, and A2A-ready integration points Our background includes Python API Development and AI Development for workflow-driven systems that depend on real business data, modular services, and controlled automation. The approach will keep the system extensible and safe for sensitive actions. I'd be happy to discuss the details and answer any questions before we get started. Looking forward to working with you. Best regards, Mubeen Web Crest

@adeelpirzada

Hi, I am Adeel. I built Boostifai, a multi-tenant AI SaaS with a Python service layer and a C# backend on Postgres, coordinating several automated workflows behind one dashboard, so orchestrating specialized agents against a shared data store is familiar territory. I would start with the Commander agent and one or two specialized agents, sales and inventory first, wired through FastAPI with a clear approval gate before anything touches price or payment. LangGraph fits well for the handoffs and persistent memory. Given the scope, I would treat this as phased delivery rather than one drop, so you can review and adjust agents as they come online. Do you already have the business data in a Postgres schema, or does that need to be modeled from scratch?

@denisSolution

Hi, I'm Denis, a developer experienced with building multi-agent AI systems that integrate with real business data. I understand you're aiming for a centralized orchestrator coordinating specialized agents for sales, inventory, supplier management, quotes, orders, invoicing, lead prospecting, and scheduling. The system needs to handle sensitive actions with human approval while maintaining customer and supplier history. I recently worked on a similar orchestration system where agents communicated through APIs and persistent memory, with validation layers before executing critical operations. For this project, I'd start by mapping each agent's role, designing the orchestrator's workflow logic, then building the API layer with PostgreSQL for reliable data storage and retrieval. The main technical challenge will be designing the orchestrator to balance agent autonomy with required approvals. I'd implement state tracking for agent decisions, queue sensitive requests for approval, and ensure the system remains maintainable as new agents are added. One risk is unclear approval requirements for different action types. I'd recommend early workshops to define approval flows and edge cases before implementation. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@gotucat

Hi, I reviewed the project and understand you need a multi-agent conversational AI business system for MIR Fine Jewelry, with a central orchestrator coordinating sales, inventory/pricing, suppliers, quotes/orders, communications, invoice processing, lead prospecting, and scheduling. It must use your real business data, keep customer and supplier history in PostgreSQL, and support human approval before sensitive actions like price changes, discounts, POs, payments, or communications. I’ll build it as an API-first system in Python with FastAPI, implementing AI Integration and AI Agent Swarms via a LangGraph-style workflow, with persistent memory for auditability. I’ll connect specialized agents through clear APIs, add MCP/A2A architecture where needed, and ensure modular design for easy extension. Quality, clean integrations, and responsive delivery will be prioritized for smooth multi-agent orchestration. Let’s discuss here now.

@ImranAli01

Hi There, I’ve read your details and clearly understand that you are building a multi-agent AI business system for MIR Fine Jewelry, centered on an Orchestrator Agent coordinating sales, inventory, suppliers, quotes, orders, communications, invoices, prospecting, and scheduling with human approval for sensitive actions. This is absolutely doable for me, let's chat and take this forward. My approach is to build the backend with Python, FastAPI, and PostgreSQL, using LangGraph for stateful multi-agent orchestration, persistent memory, MCP for controlled tool access, and A2A patterns where agent-to-agent communication adds value. I’ll design the Commander as the central decision layer, with specialized agents operating through clear APIs, shared business context, audit trails, permissions, and approval gates before price changes, discounts, POs, payments, or outbound communications. Real business data will be integrated securely while keeping the architecture modular and extensible. As final deliverables you will receive the API-first multi-agent architecture, Commander and specialized agents, PostgreSQL data layer, memory and workflow management, MCP/A2A integrations, human approval mechanisms, business-data integrations, audit logging, documentation, and an extensible foundation for future agents. One thing I'd like to confirm before we start: which existing systems and APIs currently hold MIR’s customer, inventory, supplier, and financial data? Cheers, Imran

@DeveloperStation

Hi, The human-in-the-loop gate you described is the part I'd design first. For price changes, POs, and payments, I'd have the orchestrator pause and write a pending action to Postgres, then require an explicit approve or reject before any agent executes. That keeps the sensitive steps auditable and reversible. I've built an AI orchestration layer that encodes an agency's own business knowledge into automated workflows with OpenAI and Claude, plus a chatbot that answers from real business documents. BD Automation: evidence-backed BD workflows AIChatbot: https://mango... university portal assistant One question on memory: do you want per-customer and per-supplier history stored relationally in Postgres, or a vector store for retrieval, or both? Answer shapes the schema. I'd start with the orchestrator and one agent as the first milestone so you only release on working code. Adil

@jushuamurillo98

Hi, I am Jushua and I can build the multi-agent AI system for MIR Fine Jewelry with a central orchestrator that coordinates sales, inventory, suppliers, quotes, orders, communications, invoices, leads, and scheduling. First, I will design the system around a Commander Agent that routes tasks to specialized agents while keeping customer, supplier, product, and order history available across workflows. Next, I will build the API-first backend with Python, FastAPI, and PostgreSQL, using LangGraph or Google ADK based on the workflow requirements. After that, I will connect the required business APIs and MCP/A2A components and add persistent memory so agents can work with real business data instead of isolated conversations. Sensitive actions such as price changes, discounts, purchase orders, payments, and outgoing communications will require human approval before execution. I will keep each agent modular so new workflows can be added without rebuilding the system. Testing will cover agent communication, data accuracy, approvals, and failure handling. I have experience building AI agents, API integrations, automation workflows, and Python backends.

@sf264

Hello, I would love to develop a multi agent AI business system for MIR Fine Jewelry that will have all the given functionalities. I have a rich experience in all the mentioned technologies. Message me to discuss more details. I am excited to collaborate with you, Fahad.

@sarimshoaib1425

N8n. N8n. N8n. $20. That’s it. No need to go anywhere and you orchestrate everything there. n8n has native MCP client and server nodes now, so the MCP and A2A piece you’re asking for is built in, Persistent memory sits in Postgres, and every sensitive action, price changes, discounts, POs, payments, gets a human in loop approval step before it fires. Can walk you through the architecture before we lock scope. Let’s do it.

@Aaleen110

The hardest part on a job like this is making the Commander agent actually direct traffic instead of just being a chat interface, so I will focus on the LangGraph implementation for the multi-agent workflow, choosing LangGraph for its explicit graph-based state management which maps well to coordinating specialized agents. I will build the sales and customer management agent first, this one sets up the persistent memory structure needed by all other agents. Then I will build the inventory and pricing agent, so we can start populating the system with real business data. The Commander agent will use Python and FastAPI to route requests, and I will use PostgreSQL for persistent memory, also handling customer and supplier history. I will set up API endpoints for each agent so they can communicate. We will need a clear definition of the approval workflows for sensitive actions, so tell me, what is the exact escalation path and required data points for a human to approve a discount versus a payment? 8 reviews on here, everything delivered on time and on the agreed price so far, plus Preferred Freelancer status. Once you provide that approval workflow detail, I will send back a preliminary sequence diagram for the Commander agent's interactions.

@mayr81

Building a MIR Fine Jewelry multi-agent business system with a central Commander/Orchestrator plus specialized agents for sales & customer management, inventory & pricing, supplier management, quoting & orders, communications, invoice processing, lead prospecting, and scheduling. Core design: API-first modular services in Python with FastAPI, PostgreSQL for persistent customer/supplier history, and LangGraph-style multi-agent workflows (or Google ADK) to coordinate tool-using agents via A2A/MCP-capable architecture. Persistent memory will retain conversation context, entities, and business events so recommendations remain consistent across sessions. Safety and control: human-in-the-loop approvals before sensitive actions, price changes, discounts, PO creation, payments, and outbound communications, implemented as explicit approval gates in the workflow. Deliverables: orchestrated multi-agent runtime, well-defined agent interfaces, event-driven persistence, API endpoints for front-end/CRM integration, and extensible scaffolding to add new business capabilities without rewriting core coordination logic.