never been easier to swap models
@hwchase17
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Fireside Chat on LLM-Based Customer Support Agent Systems
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If you love diving deep into the technical details of highly complex, continually improving LLM-based systems… then boy do I have an event for you! Next Tuesday, in SF, fireside chat with Decagon – one of the leading customer support agent builders https://
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Base Models Commoditized: Building AI Flywheels Strategy
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It's increasingly clear that the base models will be commoditized (Deepseek) I would argue that that defining companies will invest not in models, but rather in flywheels around the model We're talking about how to build these flywheels next Tuesday: https://
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Human Annotations Critical for LLM Training Data Flywheel
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The importance of human annotations Human annotations on LLM outputs/trajectories are crucial for getting the data flywheel turning Decagon called this out as a key component of their AI agent platform. Learn more about it NEXT TUESDAY https://
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LangGraph State Management Primitives Now Accessible Beyond Framework
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excited to make low level primitives for state management accessible outside of LangGraph next up: higher level, off-the-shelf implementations for short and long term memory
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AI Agent Flywheel Architecture: Core Components Explained
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What does a flywheel for AI agents look like? According to Decagon (leading customer support agent) it involves:
Core AI Agent
Routing
Agent Assist
Admin Dashboard
QA Interface Want to learn more? We're doing a fireside chat next TUESDAY: https://
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COT Benchmarking Training in Modern AI Models
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these were easy to benchmark. COT would be easy as well but is trained into most models these days so i dont think it would change too much
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Framework-Agnostic AI Optimizers vs DSPy Dependency
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dspy more powerful, but requires using the dspy framework (in addition to their optimizers) i think framework-agnostic optimizers are more usable
