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#014 Building Predictable Agents through Prompting, Compression, and Memory Strategies
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:
- 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
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By Nicolay GeroldIn 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:
- 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