In my last post, I wrote about my experience working on a stealth startup that offers an AI agent on an e-commerce platform. In this article, I would like to dig a bit further into a broader analysis of how AI Agents perform in reality.
An AI or artificial intelligence agent is a system designed to perform tasks autonomously by perceiving its environment and taking action to achieve specific goals. So by definition, the key difference between an AI Agent vs a Human Agent lies in - Automation.
The next question would be - how does Automation help with the whole process, and with what specific outcome? Let's break down AI functions in detail:
- Architecture: This is the base from which the agent operates. It can be a physical structure, a software program, or a combination of both. For example, a robotic AI agent consists of sensors, actuators, and control systems, whereas a software AI agent may consist of algorithms, databases, and APIs.
- Agent Function: This defines how the agent uses the data it collects to perform actions. The agent function considers the type of information required, AI capabilities, the knowledge base, and feedback mechanisms.
- Agent Program: This is the implementation of the agent function. It involves developing, training, and deploying the AI agent to perform its designated tasks. The agent program aligns with the business logic, technical requirements, and performance goals.
- Goals and Task Planning: An AI agent starts by receiving a goal or instruction from a user. It then plans tasks to achieve this goal, breaking it down into smaller actionable steps.
- Information Acquisition: AI agents gather the necessary information to perform their tasks. This might involve accessing databases, interacting with other AI agents, or retrieving information from the internet.
- Task Implementation: The agent executes the planned tasks methodically, checking its progress and adjusting as needed based on feedback and new data.
- Improved Efficiency and Convenience: AI agents can provide quick responses, handle repetitive queries, and offer personalized recommendations, making processes like online shopping, customer service, and information retrieval faster and more convenient.
- Personalization and Engagement: For instance, AI-powered recommendation systems in eCommerce can suggest products based on past purchases and browsing history, which is more scientific, compared to human sales recommendations, unless the salesperson has already established a long-term merchant-customer relationship, which usually exists in luxury VIP shopping.
- Trust and Reliability: Trust is crucial in all kinds of relationship building. If accurate and reliable information or assistance is provided, users are generally happy and would become a returning customer possibly. The biggest challenge here is still around human behavior changes, which in my personal opinion, could be easily built with Gen Z and younger generations with their rapid adaption of all tech tools on the market now.
- Emotional Response: Human Agents, even if well trained, would possibly have a bad day in their life while offering customer service that might negatively impact consumer experience, while in the meantime, unreasonable customers might start a fight with human agents if things go wrong. AI Agents could be trained with the right tone of responding without having any human emotions, which adds much value especially when it comes to the customer service sector.
- Challenges and Frustrations: Despite every benefit mentioned above, users would for sure still experience challenges and frustrations when interacting with AI agents. Common issues include difficulties in understanding and interpreting user intent, especially with complex or ambiguous queries, and the perceived lack of empathy and human touch in automated interactions. Those are the areas of algorithm improvement opportunities that we could focus on when it comes to real-life applications, though I feel that Chatgpt has already done a great job.
Flashback to that Silicon Valley episode, which we could dig into further next time.
