• AI Disruptor
  • Posts
  • Goodbye prompt engineering, hello agent engineering

Goodbye prompt engineering, hello agent engineering

The age of autonomous agents is upon us, and it's going to be crazy.

Welcome to AI Disruptor! if you want to join our growing community of readers, click the button below.

Have you noticed that the AI world has been absolutely buzzing about agents lately?

It's no longer enough to just be a master of prompts - nowadays, if you want to stay ahead of the curve, you've got to think like an agent engineer.

Designing effective AI agents requires a holistic framework that goes beyond just crafting clever prompts. It's about understanding the agent's purpose, breaking down the actions needed to fulfill that purpose, and identifying the capabilities and technologies required to make it all happen.

And that is where the Agent Engineering Framework comes in.

In this edition of AI Disruptor, you are about to learn:

The Agent Engineering Framework

Before we dive in, let's take a step back and look at the big picture. The Agent Engineering Framework is all about breaking down the process into manageable chunks, so you can tackle each aspect of your agent's design with clarity and purpose.

What's the agent's job? (purpose)

First things first: you've got to know what your agent is supposed to do. Are they a customer service or FAQ chatbot? A personal assistant? A content creator or writer? Defining your agent's job is the foundation upon which everything else is built.

Actions to get the job done (actions)

Once you've nailed down your agent's purpose, it's time to think about the actions they'll need to take to fulfill it. If they're a customer service agent, that might include things like answering questions, processing orders, and escalating issues to human representatives.

Capabilities behind the actions (capabilities)

Now, here's where things start to get interesting. To perform those actions, your agent will need certain capabilities - things like natural language processing, information retrieval, and decision-making. Figuring out which capabilities your agent needs is key to designing an effective system.

How good is good enough? Setting proficiency benchmarks (proficiency)

It's not just about having the right capabilities - it's about making sure those capabilities are working well. That's where proficiency benchmarks come in. You'll need to set clear metrics for how well your agent should perform each capability, so you can ensure they're meeting the mark.

Of course, capabilities don't just materialize out of thin air - they're enabled by the right technologies. From large language models to knowledge bases to machine learning algorithms, there's a whole world of tech out there waiting to be leveraged in your agent design.

Putting the pieces together - agent anatomy and orchestration (orchestration)

Finally, it's time to put all the pieces together. This is where you'll decide on your agent's anatomy - will they be a single, monolithic system or a swarm of specialized agents working together? You'll also need to orchestrate how all the different components interact with each other to create a cohesive, effective agent.

The tech behind agent workflows (technology)

Now that we've covered the key components of the Agent Engineering Framework, it's time to dive into the technologies that make it all possible. From language models to clever knowledge retrieval techniques, there's a whole world of tools and approaches to explore.

Let's take a look at some of the most promising ones:

Language models and knowledge: If you're building an agent that needs to understand and generate human language, then large language models, or LLMs, are your best friend. These AI powerhouses, like GPT-4o and Claude 3, have been trained on vast amounts of text data, allowing them to grasp the nuances of natural language and generate human-like responses. But LLMs aren't just about churning out words - they're also packed with knowledge. By leveraging the information stored in their training data, LLMs can provide your agent with a broad base of understanding to draw from.

Retrieving and generating: Of course, sometimes you need more than just general knowledge - you need specific, up-to-date information. That's where retrieval techniques like RAG (Retrieval-Augmented Generation) come in. RAG allows your agent to search through external knowledge bases and retrieve relevant information, which can then be fed into an LLM to generate a response. It's like giving your agent a personal research assistant to help them stay informed and accurate.

Function calling: But what if your agent needs to do more than just talk? That's where function calling comes in. By allowing your agent to call upon external APIs and services, you can expand its capabilities far beyond language. Imagine an agent that can not only discuss the weather but actually retrieve current weather data and provide personalized recommendations. Or an agent that can not only explain mathematical concepts but actually perform complex calculations.

Fine-tuning: While LLMs and retrieval techniques can provide a solid foundation, sometimes you need to give your agent a little extra training to really make it shine. That's where fine-tuning comes in. By training your agent on a smaller, more specialized dataset, you can adapt it to your specific use case and improve its performance on targeted tasks.

Guardrails: Of course, with great power comes great responsibility. As you're expanding your agent's capabilities, it's important to put guardrails in place to ensure it stays on track. Techniques like rule-based filters, content blocking, and adversarial training can help prevent your agent from going off the rails or producing harmful outputs.

The future of (prompt) agent engineering

Agent engineering is about to become a dominant force in the world of prompt engineering and business. While prompt engineering has largely focused on optimizing individual interactions with language models, agent engineering takes a more holistic approach, designing AI systems that can autonomously carry out complex tasks and make decisions based on their own understanding of the world.

Every company will be using AI agents within the next 12 months, and those that are not will fall severely behind.

This shift towards agent engineering is driven by a growing recognition that, as AI becomes more sophisticated and ubiquitous, we need to create systems that can operate with greater independence and adaptability. Rather than simply responding to prompts, these agents will be able to proactively seek out information, learn from their experiences, and adjust their behavior to better achieve their goals.

As the benefits of agent engineering become clear, enterprises are beginning to take notice. Forward-thinking companies are already starting to explore how they can leverage agent engineering to create more efficient, effective, and autonomous AI systems.

For example, check out what Klarna is doing. They replaced the work of 700 human agents with a single AI agent. You can see where this is going.

Of course, designing and building these sophisticated agents is no simple task. By providing a structured approach to agent design - from defining purpose and actions to selecting capabilities and technologies - these frameworks give enterprises a clear roadmap to follow as they embark on their agent engineering journeys.

As the field develops, we can expect to see even more frameworks and best practices emerge, helping to guide enterprises and individuals as they navigate this exciting but complex new world of prompt and agent engineering.

By designing agents that can learn, grow, and make decisions on their own, we open the door to a future where AI can take on even greater challenges and responsibilities. From tackling complex global problems to enhancing our daily lives and businesses in countless ways, the potential impact of agent engineering is staggering.

So get ready, prompt engineers, enterprises, entrepreneurs, and AI enthusiasts - the age of autonomous agents is upon us, and it's going to be crazy.

- Alex (Creator of AI Disruptor)

What did you think of this edition of AI Disruptor?

Your feedback helps me create a better newsletter for you!

Login or Subscribe to participate in polls.


or to participate.