In this conversation, Nicolay and Richmond Alake discuss various topics related to building AI agents and using MongoDB in the AI space. They cover the use of agents and multi-agents, the challenges of controlling agent behavior, and the importance of prompt compression. When you are building agents. Build them iteratively. Start with simple LLM calls before moving to multi-agent systems. Main Takeaways: Prompt Compression: Using techniques like prompt compression can significantly reduce the cost of running LLM-based applications by reducing the number of tokens sent to the model. This becomes crucial when scaling to production. Memory Management: Effective memory management is key for building reliable agents. Consider different memory components like long-term memory (knowledge base), short-term memory (conversation history), semantic cache, and operational data (system logs). Store each in separate collections for easy access and reference. Performance Optimization: Optimize performance across multiple dimensions - output quality (by tuning context and knowledge base), latency (using semantic caching), and scalability (using auto-scaling databases like MongoDB). Prompting Techniques: Leverage prompting techniques like ReAct (observe, plan, act) and structured prompts (JSON, pseudo-code) to improve agent predictability and output quality. Experimentation: Continuous experimentation is crucial in this rapidly evolving field. Try different frameworks (LangChain, Crew AI, Haystack), models (Claude, Anthropic, open-source), and techniques to find the best fit for your use case. Richmond Alake: LinkedIn Medium Find Richmond on MongoDB X (Twitter) YouTube GenAI Showcase MongoDB MongoDB AI Stack Nicolay Gerold: ⁠LinkedIn⁠ ⁠X (Twitter) 00:00 Reducing the Scope of AI Agents 01:55 Seamless Data Ingestion 03:20 Challenges and Considerations in Implementing Multi-Agents 06:05 Memory Modeling for Robust Agents with MongoDB 15:05 Performance Optimization in AI Agents 18:19 RAG Setup AI agents, multi-agents, prompt compression, MongoDB, data storage, data ingestion, performance optimization, tooling, generative AI