With OpenAI's GPT store, Microsoft's copilots, and the rise of coding agents like GPTEngineer, it's clear that LLM-powered agents are becoming instrumental to the wider adoption of AI. This primer includes main modules & frameworks to use for building AI agents, and methods for measuring their value to organisations.
LLMs are making headlines, but their impact comes once they become part of real-world applications.
With OpenAI's GPT store, Microsoft's copilots, and the rise of coding agents like GPTEngineer, it's clear that LLM-powered agents are becoming instrumental to the wider adoption of AI.
Why use agents?
Experiments carried out by Andrew Ng and his team show a dramatic performance increase when wrapping an LLM in an iterative agent loop. For example, GPT-3.5 accuracy for code-writing tasks jumped from 48.1% (zero-shot) to a remarkable 95.1% with an agent workflow (28% higher than GPT-4).
These tools are by no means even close to replacing humans, but they can automate and support us in carrying out a bunch of repetitive tasks.
Over the past few months, we've been exploring and developing our own AI agents, both independently and in collaboration with clients. We've prepared a quick primer, including main modules to use, frameworks for building them, and methods for measuring their value to organisations.
