2024: The year of AI Agents

In 2023, we saw the widespread adoption of retrieval-augmented generation (RAG) systems that combined large language models with external knowledge sources. This allowed AI applications to provide more factual, up-to-date and knowledgeable outputs across numerous domains. For example, e-commerce companies built chatbots powered by RAG models to assist online shoppers with product research, recommendations and order support - drawing on real-time inventory data, customer reviews and technical specifications.

RAG was also applied in enterprise search tools, medical diagnosis assistants, and financial advisory services to deliver more reliable and informative responses to user queries by seamlessly integrating relevant external information. The flexibility and knowledge-boosting capabilities of RAG systems fueled their rapid uptake across industries in 2023.

But 2024 is shaping up to be the year that AI takes a major leap forward with the rise of AI agents - systems that go beyond just asking something and getting an answer to actively perceive, reason and act upon their environments.

What makes an AI agent?

So what has enabled this shift towards more capable AI agents? A few key factors:

1. Limitations of simple RAG solutions: for many tasks, finding the right context is not a simple task, as we might need to require information that is not necessarily semantically close to the input prompt.

2. Real world problems cannot be solved by sending one context window to an LLM. Even if were able to find the right context, a problem may require some back and forth between context extraction and a LLM response.

Why is 2024 primed for AI Agents?

Your blog posts are a great way to communicate what happens behind the scenes of your business to people who follow your brand and buy your products. It's also a good space to share insights and expertise on how to get the most out of your products or to simply share tips and tricks on how to succeed more broadly in the niche that you're in.

Developing AI Agents solutions

At SpireLabs, we are heavily experimenting with AI agent architectures to create agent-based products and services. This involves:

Identity - Carefully defining an agent's goals, knowledge bases, permitted actions and decision-making framework. Do we want the agent to be an impartial information provider, a personalized task assistance, or something with more autonomy?

Reinforcement Learning - Setting up reinforcement learning environments to optimize an agent's behavior towards the desired goals through trial-and-error and reward modeling.

Safety Considerations - Ensuring agents behave reliably, with transparency into their reasoning, and hard-stops on unacceptable or harmful actions. Ethics must be accounted for.

Software Integration - Building tools and APIs to embed AI agents into existing software applications, workflows and products in a complementary manner.

Multi-Agent Systems - Exploring how multiple agents can collaborate, divide labor and be effectively orchestrated, including coordination with human teams.

2024 is shaping up to be an exciting year as AI agents go from research concepts to production-ready products and services. At SpireLabs, we are working diligently to be at the forefront of safe and innovative agent-based AI solutions across all industry verticals.

Contact us

Whether you have a request, a query, or want to work with us, use the form below to get in touch with our team.