Why AI Agents Make AI More Practical and Useful

Author

Duncan Trevithick

Duncan combines his creative background with technical skills and AI knowledge to innovate in digital marketing. As a videographer, he's worked on projects for Vevo, Channel 4, and The New York Times. Duncan has since developed programming skills, creating marketing automation tools. Recently, he's been exploring AI applications in marketing, focusing on improving efficiency and automating workflows.

AI agents are intelligent entities that can operate autonomously, interact with other agents, and adapt to changing environments. This definition captures the key characteristics that make AI agents powerful and versatile tools in various applications.

The autonomy of AI agents allows them to perform tasks and make decisions without constant human intervention. They can process information, analyze situations, and take appropriate actions based on their programming and learned behaviors. This autonomous operation is crucial for handling complex tasks efficiently, especially in dynamic environments where real-time decision-making is essential.

Interaction is another vital aspect of AI agents. They can communicate and collaborate with other agents, whether artificial or human, to achieve common goals or solve complex problems. This ability to interact enables the creation of multi-agent systems where different specialized agents work together, leveraging their individual strengths to tackle challenges that would be difficult for a single agent to handle alone.

Adaptability is perhaps one of the most important features of AI agents. They can learn from their experiences and adjust their behavior in response to changing circumstances though memory technologies including RAG. This flexibility allows them to improve their performance over time and handle unforeseen situations, making them valuable in environments where conditions are unpredictable or constantly evolving.

These characteristics – autonomy, interaction, and adaptability – make AI agents powerful tools in various fields, from robotics and automation to decision support systems and virtual assistants. As AI technology continues to advance, we can expect to see even more sophisticated and capable agents that can handle increasingly complex tasks and environments.

AI agents are demonstrating remarkable capabilities, often surpassing traditional large language model (LLM) approaches:

• Agentic workflows using GPT-3.5 can outperform zero-shot GPT-4 on coding tasks, achieving nearly 100% accuracy on benchmarks like human-eval.

Key design patterns in agent systems include:

• Reflection: Agents review and improve their own outputs.
• Tool use: Integrating external tools and APIs to expand capabilities.
• Planning: Breaking down complex tasks into manageable steps.
• Multi-agent collaboration: Multiple agents with different roles working together.

Andrew Ng, co-founder of Google Brain, former chief scientist at Baidu, and a leading figure in artificial intelligence, emphasizes the power of these patterns, stating, “Reflection is a tool that I think many of us should just use. It just works.” He also notes that while planning and multi-agent systems are still emerging, “When I use them, sometimes my mind’s blown for how well they work.”

However, AI agents are not without challenges:

• Performance can be inconsistent, with some tasks working brilliantly while others fail.
• Multi-agent systems can be finicky and don’t always produce reliable results.
• Users may need to adapt to longer wait times for complex tasks, as agent workflows often require minutes or even hours to complete.

AI developer David Ondej shared his insights on the burgeoning field of AI agents, predicting a surge in their popularity and application in 2024. Ondrej, who teaches other developers how to harness various AI technologies, believes that AI agents represent a significant shift in how we interact with artificial intelligence.

“AI agents are the next big thing,” Ondrej explains, pointing to exponential growth in search interest.

He compares the potential impact of AI agents to previous technological revolutions like the internet and social media, suggesting that those who master this technology early will have a significant advantage.

Ondrej highlights several business applications for AI agents that are already in use. These include continuous research, where agents monitor websites, social media, and academic papers for relevant information. In software engineering, agents like Devin are being employed to build, optimize, and debug code. Customer service is another major area of application, with companies like Klarna utilizing AI agents for 75% of their support team, handling over 2.3 million conversations.

My personal experience building with AI agent frameworks can be summed up as suprisingly easy yet at the same time frustrating.

It’s easy because with tools like CrewAI, it’s simple to create a team of agents that organize themselves and start generating results. You can see below how easy it is to define an agent within CrewAI

And creating a team of agents is also very simple:

But I have often found it frustrating because the agents sometimes display strange behaviour, seemingly ignoring detailed instructions and repeating themselves. Debugging this is a very different process to working with ‘normal’ (deterministic) code.

It can also end up being costly, as the prompts each agent uses typically require a large number of tokens. They need lots of tokens to be able to pass context around within the ‘team’ of agents

There are a number of products being developed to solve these frustrations, including AgentOps, which aims to help developers gain better visibility into the prompts that agents within CrewAI, Autogen, Multion, and other frameworks are using – as well as the token usage and costs.

In a recent interview, Mark Zuckerberg shared his vision for the future of AI agents and their potential impact on businesses, creators, and individuals.

Zuckerberg emphasizes the scale and diversity of this AI agent ecosystem, stating: “I think we’re gonna live in a world where there are gonna be hundreds of millions of billions of different AI agents, eventually, probably more AI agents than there are people in the world, and that people are just gonna interact with them in all these different ways.”

Harrison Chase, CEO and founder of Langchain, also emphasizes the importance of AI agents in his talk at a Sequoia event. He highlights three key areas of development for AI agents: context windows, tool usage, and text-to-action capabilities. Chase anticipates that infinite context windows will enable more complex chain-of-thought reasoning, allowing agents to tackle multi-step problems in fields like science, medicine, and climate change.

Chase states, “I think one way to think about this is you’re actually offloading the planning of what to do to the human engineers who are doing that at the beginning, and so you’re relying on that as a little bit of a crutch.” This underscores the importance of flow engineering in agent development.

The development of AI agents is closely tied to advancements in large language models (LLMs). As LLMs improve, so will the capabilities of AI agents, especially in terms of their reasoning abilities.

Andrew Ng, has also shared his insights on the future of AI agents.

Ng is optimistic about the future of AI agents, highlighting several key trends and potential developments:

• Expanded capabilities: AI agents are expected to dramatically increase the range of tasks they can perform effectively.
• Integration of faster inference models: Platforms offering rapid token generation could significantly speed up agent workflows, potentially making complex tasks near-instantaneous.
• Bridging the gap to more advanced models: Agentic workflows using current models may achieve performance levels close to upcoming advanced models.
• Emphasis on fast token generation: Ng suggests, “Generating more tokens really quickly from even a slightly lower quality LLM might give good results compared to slower tokens from a better LLM.”
• Improved reliability: As frameworks and techniques evolve, the current inconsistencies in agent performance are likely to decrease.
• Potential path to AGI: While not a direct solution, Ng views agentic reasoning as a step forward on the journey towards artificial general intelligence, stating, “The path to AGI feels like a journey rather than agent workflows could help us take a small step forward on this very long journey.”

As these technologies mature, we can expect to see more widespread adoption of AI agents in various industries, potentially revolutionizing how complex tasks are approached and solved. Ng’s insights suggest that AI agents will play a crucial role in advancing the field of artificial intelligence in the coming years.

High-quality training data is a crucial component for the success of AI agents, playing a fundamental role in their development, performance, and reliability. Here’s an explanation of why this is the case:

Improved Model Performance

High-quality training data directly impacts the performance of AI models. In specialized fields like healthcare, where accuracy is paramount, well-annotated and diverse datasets enable AI agents to learn more effectively. For example, in diabetic retinopathy classification, using public datasets with carefully adjudicated labels has led to the development of high-performing models. This demonstrates that the quality of input data significantly influences the output and decision-making capabilities of AI systems.

Generalization and Bias Reduction

Quality training data helps AI agents generalize better to new, unseen situations and reduces the risk of bias:

• Diverse Representation: A comprehensive dataset that covers a wide range of examples allows the AI to learn more robust and generalizable patterns.
• Bias Mitigation: High-quality data that is representative and well-balanced helps prevent the AI from learning and perpetuating biases present in limited or skewed datasets.

Addressing Data Limitations

In many real-world scenarios, especially in specialized domains, large amounts of high-quality data may not be readily available. To address this “small-data” issue, several techniques can be employed to enhance data quality:

• Data Augmentation: This technique artificially expands the dataset by creating modified versions of existing data points, helping the AI learn from a broader range of examples.
• Transfer Learning: By leveraging knowledge from pre-trained models on larger datasets, AI agents can perform better on specific tasks with limited data.
• Knowledge-Guided Methods: Incorporating domain expertise into the data generation process can significantly improve the quality and relevance of training data.

Impact on Training Efficiency

High-quality data not only improves the final performance of AI agents but can also enhance the efficiency of the training process:

• Faster Convergence: Clean, well-structured data allows models to learn more quickly, potentially reducing the number of training iterations required to reach a desired level of performance.
• Resource Optimization: With better data, AI systems may achieve high performance with smaller batch sizes or fewer parameters, leading to more efficient use of computational resources.

Ensuring Reliability and Trust

In critical applications such as healthcare diagnostics or autonomous driving, the reliability of AI agents is paramount. High-quality training data is essential for building trustworthy AI systems:

• Accurate Ground Truth: Carefully curated datasets with accurate labels or annotations provide a solid foundation for AI learning, ensuring that the model’s outputs align with expert knowledge.
• Regulatory Compliance: In regulated industries, high-quality data is crucial for meeting standards and gaining approval for AI-based solutions.

Challenges in Data Quality

It’s important to note that achieving high-quality training data is not without challenges:

• Resource Constraints: Collecting and annotating high-quality data can be time-consuming and expensive, especially in specialized domains.
• Edge Computing Limitations: The increasing adoption of edge devices for AI applications introduces new challenges in maintaining data quality due to resource limitations and decentralized data processing.
• Epistemic Differences: In fields like medicine, differences in expert opinions can complicate the creation of ground truth datasets.

Final Thoughts

AI Agents are an invetiable next step in the development of AI along the path to AGI. High-quality training data is the foundation upon which successful AI agents are built. It directly influences their performance, reliability, and ability to generalize across diverse scenarios. As AI continues to permeate various aspects of our lives, the importance of focusing on data quality cannot be overstated, particularly in critical domains where the consequences of AI decisions can have significant real-world impacts.