Matt
@mane
Anthropic released a great breakdown of common agent design patterns Feels like a great reference for a lay of the land and the variety of approaches you could take. Really good diagrams to illustrate these flows Starting Point - The Augmented LLM Agents are essentially LLMs enhanced with retrieval, tools and memory Design Patterns: Prompt Chaining Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one Routing Routing classifies an input and directs it to a specialized followup task
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Matt
@mane
Parallelization Sectioning: Breaking a task into independent subtasks run in parallel. Voting: Running the same task multiple times to get diverse outputs. Orchestrator-workers A central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results Evaluator-optimizer One LLM call generates a response while another provides evaluation and feedback in a loop
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1dolinski
@1dolinski
if anyone is working with ai agents and wants to discuss pls don't hesitate to reach out, i'd be happy to organize lightly scheduled calls for groups of 5 - 8 people
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