From tool calling to multi-agent orchestration—LLMs now interact via the Model Context Protocol (MCP) for scalable, modular workflows. Agents coordinate tasks through JSON-RPC, powering the next generation of AI systems.
#LLM #MCP #AgentSystems #AI #OpenAI #Anthropic

Modularize your LLM orchestration with the AI Agent Handler — supporting OpenAI, Gemini, Anthropic, and Ollama. Built on the serverless SilvaEngine framework.

🔗 github.com/ideabosque/a...
#LLM #AIInfrastructure #SilvaEngine #FunctionCalling #MultiAgentSystems

🔧 Introducing `ai_agent_core_engine`: A modular engine for orchestrating multi-agent AI workflows. Supports task routing, memory, and coordination across OpenAI, Claude, Gemini & more. Scalable, composable, and production-ready.
👉 github.com/ideabosque/a...
#AIAgents #LLMops #OpenAI #Claude #Gemini

🚀 Scalable Multi-Agent AI with Google A2A + Anthropic MCP: Modular AI mesh enabling agent interoperability, shared tools, and secure enterprise integration. Agents using OpenAI, Claude, Gemini, etc., can now collaborate via A2A & MCP.

#GoogleA2A #AnthropicMCP #AIAgents

AI RAG and Vector Database - Challenges and Fixes

- Speed Issues: Use approximate search to save time.
- Handling Big Data: Split and copy databases for large-scale tasks.
- Improving Accuracy: Regularly update embeddings.

#AI #RAG #VectorDatabase

AI RAG and Vector Database - Applications

- Knowledge Bases: Answer customer questions with accurate info.
- Document Search: Quickly find the most relevant documents.
- Custom AI Systems: Build tools for specific industries.

#AI #RAG #VectorDatabase

AI RAG and Vector Database - How It Works

1. Prepare Data: Turn raw info into embeddings.
2. Store: Save embeddings in a vector database.
3. Search: Find similar data points.
4. Generate: Use found data for smarter AI responses.

#AI #RAG #Embedding #VectorDatabase

AI RAG and Vector Database - Key Parts
Embedding Models:
- Turn words or data into high-dimensional numbers AI can understand.
- Fine-tune for better accuracy.

Vector Databases:
- Examples: Pinecone, Redis Stack, Milvus.
- Fast search methods like FAISS or HNSW.

#AI #RAG #Embedding #VectorDatabase

Conversation Tracking and Session Management: Log past messages for AI reference, linking them to specific users or sessions. Enable seamless session continuity for extended interactions. Support session pausing and resumption to enhance user experience and maintain context over time. (2/2) #AI

Conversation Tracking and Session Management: Keeping conversation history intact to ensure connected sessions. Why it matters: It enhances AI's ability to provide accurate, consistent responses, boosting user experience and reliability in interactions. (1/2)

#AI #AIThreading

Streamline Data Integration with DataWald!
The [DataWald Integration Framework](github.com/ideabosque/d...) is here! Built on AWS SilvaEngine, it dynamically maps, transforms, and syncs data across ERP, CRM, and eCommerce systems.
#DataIntegration #AWS #SilvaEngine #DataMapping

We built a scalable, serverless architecture using AWS and the OpenAI Assistant Engine for real-time communication. WebSocket enables instant responses, Lambda and SQS handle tasks, and DynamoDB ensures session continuity. github.com/ideabosque/o...
#AI #AWS #OpenAI #WebSocket #Serverless

The OpenAI Assistant Engine is an open-source toolkit that simplifies integrating with OpenAI's capabilities, supporting function calling. Recent updates include WSS support for real-time data streaming with AWS API Gateway.

#OpenSource #AI #OpenAI #AWS

www.linkedin.com/posts/bohung...

Orchestrating AI Coordination
Using the OpenAI Assistant API, we’re building an AI agent coordination structure to bridge communication between agents and multiple parties for complex tasks.

#AIAgent #AI

www.linkedin.com/posts/bohung...