Agentic Workflows & The Hack Framework

HACKS, FINAL ROUND

After two weeks of hacks to turn you into a Generative AI pro user, this week, we see the final and most advanced set of tips, mainly around our last pending point, agentic workflows.

We have covered this topic in the past, but today, we are looking at it from a new, more nuanced light, including updates based on the most impressive recent research, including agents who bribe other agents, fall in love, or have a tribal sense of belonging with each other.

Moreover, we will look at a final hack that I had not explained to you until now because, well, it was discovered this very week: reflection tuning. This new method is causing massive excitement as it has just created the best model in the world (beating Claude 3.5 Sonnet and GPT-4o) and is about to break all possible records with its new version coming next week.

Finally, we will define The Hack Framework. This guideline encapsulates all our learnings over the last three weeks and is a guide you can always follow: a guide to making excellence a reproducible process.

No Agentic Workflows, No Party

When something gets Andrew Ng as excited as it gets, you must pay attention.

He co-founded Google Brain (now merged with Google Deepmind) and is the former Chief Scientist at Baidu.

He is also a Stanford professor and co-founder of Coursera, which is known for popularizing AI education. And if that wasn’t enough, he is the source of some of the best free courses on Generative AI through Deeplearning.ai.

Over the last few months, Andrew has been obsessed with something he defined as ‘agentic workflows,’ a set of four techniques to enhance a Large Language Model’s (LLM) capacity to deal with more complex tasks that require one (or more) of the following:

Reflection

Tool use

Planning

Multi-agent collaborations

And the impact of such workflows is huge.

For instance, as you can see below, when wrapping a GPT-3.5 model (the model that saw the birth of ChatGPT back in November 2022) around an agentic workflow, its performance on HumanEval, arguably the most popular coding benchmark for LLMs, skyrockets to 95%, almost 30% more than its older brother GPT-4 released six months later and still, to this day, considered a frontier AI model.

And when GPT-4 is wrapped around a multi-agent agentic workflow (more on that later), it reaches even higher, almost topping the benchmark.

Source: Andrew Ng

And the best part is that this is not hard to build. Let’s look at every case.

Reflection

In simple terms, the model reflects over its responses. After generating an initial output (e.g., writing code), the model is asked to evaluate it for correctness, style, and efficiency, providing constructive feedback.

The model is then prompted to use that feedback to improve its response iteratively. Repeating this process can lead to further refinements, enhancing it’s ability to perform tasks like coding, writing, and answering questions more effectively.

For example, you may first ask Claude…

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