If you've been following the rapid developments in artificial intelligence (AI) lately, you've probably come across the term "AI Agent". But what does it actually mean? The truth is the definition is evolving as the technology advances. Let's try to cut through the hype and explain AI agents in simple terms.
Written by Mo Ahmed with assistance from ChatGPT 4o
The Basics: Software that can Act Autonomously
At its core, an AI agent is a software program that can perceive its environment, make decisions, and take actions to achieve goals, all without direct human control. Unlike a traditional software program that simply follows predefined instructions, an AI agent has the ability to operate autonomously to some degree.
For example, a chatbot that can engage in freeform conversation and help solve customer problems on its own would be considered an AI agent. So would a self-driving car that can navigate city streets while reacting to unexpected situations in real-time. The key is that the software can take in information, "think" for itself, and then act independently based on its goals.
Powered by Machine Learning
To achieve this level of autonomy, AI agents rely heavily on machine learning - the ability to automatically learn and improve from data and experiences without being explicitly programmed. By training on vast datasets and receiving feedback on their performance, AI agents can develop their own internal models for processing information and making decisions.
Over time, this allows the agent to get better and better at its designated tasks, whether that's engaging in natural conversations, detecting fraud in financial transactions, or optimizing supply chain logistics. Machine learning is what gives AI agents their intelligence and adaptability.
Different Types for Different Uses
AI agents come in many different forms depending on their intended use case. Some common types include:
Chatbots and virtual assistants for customer service and support
Autonomous vehicles and robots for transportation and manufacturing
Recommendation engines for e-commerce and content platforms
Fraud detection agents for banking and cybersecurity
Personal AI assistants for productivity and task management
While they share the core traits of autonomy and machine learning, the specific capabilities and level of sophistication can vary widely between different types of AI agents.
Our experience with AI Agents
In the quest for operational excellence, we at Intelligence Assist recently undertook the initiative to develop and implement of a set of AI agents. From automating data entry to enhancing customer relationship management, we explored the multifaceted ways in which this technology is reshaping our business processes and could do the same for you.
Our first experiment was streamlining our process for evaluating new AI tools and populating our database with objective data like pricing structure, discounts, and location etc so that the Intelligence Assist AI expert could add the subjective based on their own experience with the tool. We estimated that this would save up to 15 mins of mundane data entry per AI tool we review which is significant.
To achieve this, we started with Relevance AI which is a SaaS platform that provides low-code functionality to build AI agents. The attraction was the "chat option" to trigger the automation but the single workflow limitation meant that while it could easily gather data from the internet, transferring that data into a spreadsheet or the AI Tool database was not possible. Despite its potential for personal online tasks, such as extracting email sentiments or summarising YouTube videos, it fell short for this process.
To address these limitations, we shifted our approach and moved to Make.com. This proved to be an excellent choice for creating multi-conditional and complex automated workflows and AI agents. However, during testing, we noticed a significant difference between the quality of content generated by a custom GPT and an Assistant; an OpenAI function that can be used as AI agents and can be called via APIs. Queries processed by an OpenAI custom GPT were efficient and could be standardised via prompts, while its Assistants struggled with standardisation and consistency in their responses. Although connecting a custom GPT via API would have been ideal, current limitations restrict API connectivity to these GPTs.
Another AI agent we developed was designed to populate our CRM with potential client leads. This agent autonomously gathers information from emails and populates CRM records, including the context of the inquiries. We also implemented duplicate checks to maintain data integrity. In any business, manually filling CRM boards with potential clients from emails and LinkedIn messages can be time-consuming and prone to human error. This AI agent reduced our involvement in this process by approximately 80%, significantly saving time and improving efficiency. So, it's right to say that AI agents are headed in the right direction.
The Future: More Autonomous and Human-Like
As AI technology continues to advance at a rapid pace, the line between narrow, task-specific AI agents and more open-ended, human-like AI is starting to blur. Large language models like GPT-4 are enabling the creation of AI agents that can engage in freeform dialogue, answer follow-up questions, and even tackle open-ended tasks.
At the same time, the field of embodied AI is working to create robotic agents that can operate autonomously in the physical world, with the ability to perceive, reason, and interact with their environment in intelligent ways. As these research areas progress, we can expect AI agents to become more autonomous, flexible, and capable over time.
A Powerful but Complex Technology
While AI agents hold immense potential to automate tasks and enhance human capabilities, they also introduce new challenges around ethics, transparency, and control. As they become more autonomous and influential in shaping our lives, it will be crucial to develop frameworks for responsible development and deployment of these powerful systems.
Despite the complexity, one thing is clear - AI agents are here to stay and will play an increasingly important role across industries and domains. By understanding the basics of what they are and how they work, we can all be better prepared for the AI-powered future that is rapidly unfolding.
Signing off: My experience at Intelligence Assist
With the recent surge of interest in artificial intelligence (AI), I was eager to dive into a role where I could experience AI's practical applications beyond just the theoretical aspects of ChatGPT or large language models (LLMs). This drive led me to Intelligence Assist, a company at the forefront of implementing real AI solutions to streamline business processes and enhance efficiency.
At Intelligence Assist, we specialise in delivering actionable AI solutions to Australian businesses, helping them transform their operations and reduce inefficiencies. Joining this team was a thrilling opportunity to see AI in action and witness its impact firsthand.
One of the most exciting aspects of my internship was working on projects that automated tasks I previously thought were beyond the reach of AI. From streamlining mundane, daily tasks to automating complex research projects, I saw AI’s potential to revolutionize business processes in just a few weeks. The experience was eye-opening—AI tools that initially seemed like science fiction quickly became integral to our workflow.
This hands-on experience allowed me to see firsthand how AI can transform business operations, making processes faster, more accurate, and less labour-intensive. The ability to see and contribute to these tangible changes was both rewarding and inspiring, reinforcing my passion for AI and its potential to drive significant business improvements.
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