AI-Agent
An artificial intelligence (AI) agent is a system that autonomously performs tasks by designing workflows with available tools.
IBM
When we are speaking about agents, almost everybody has at least one reference:
- Agent Smith from Matrix
- Agent K and Agent J from Men in Black
- And of course the one and only: 007, Her Majesty’s secret agent
From this list Agent Smith is a pretty great example for the AI agent:
- it is autonomous
- handles complex tasks
- able to use different tools
- has memory
- collaborates other agents or users
- goal oriented
- and it is digital (until it enters the real world)
In this article we are dealing with less lethal agents. Our subjects are more helpful, try to ease our daily life with automation.
This AI agent can solve complex problems that are too boring or too time consuming to deal with them ourselves.
The entry level could be an AI assistant. It can help to create your next vacation plan, suggests itineraries, accommodations, and sightseeing.
The most sophisticated AI agent – with necessary accesses – is able to solve these tasks in a more sophisticated way. It can book your flight ticket and hotels, buys museum tickets and hop-on hop-off bus tickets, according to your needs, and for example to the weather (museum visit in rainy days, sightseeing in sunny days).
With your feedback, AI agent can learn and improve its performance, and next time it will book a more suitable hotel for you.
What do you need to build such a helpful AI agent?
- Persona: this is a basic prompt focuses on the agent character, behavior, and role.
- Memory: like a human, AI agent needs memory for learning and improving. AI agent uses this memory for maintaining a meaningful conversation with you, remembering the tasks and the way, how to solve these tasks, and remembering the results, your feedback, adapting its future actions.
- Tools: can be anything from simple programs (calculator, or voice recorder) to complex internet search, like weather forecast, exchange rates or tasks-relevant databases. Even physical tools, like a welding machine, depends on the type of agent.
- Model: the brain of an agent, a Large Language Model which provides it with the ability to understand, reason, and act.
These four components work together to create different levels of intelligent behavior, ranging from simple automation to sophisticated learning systems.
To understand the different stage of automation
Here are some type of it:
- Reflex Agent – the entry level automation: every summer day at 9AM it opens the sunshade, regardless of whether the weather is cloudy or rainy.
- Model-based Reflex Agent – the next step in automation: a smart robot vacuum that maps your home, remembers furniture locations, and can navigate around new obstacles you’ve placed since its last cleaning cycle.
- Goal-based Agent – big jump towards intelligent suggestions: your intelligent navigation system needs your destination, and it handles the rest: offers the fastest route with some alternative ones, and during the travel it can adjust it based on actual traffic conditions
- Utility-based Agent – an intelligent advisory: it is an upgraded navigation system, it handles not only one parameter: fastest route, but considers other requirements like fuel economy, road tolls, avoiding risky junctions etc.
- Learning Agent – the autonomous entity: combination of the previous four types with learning capability. The best example is personalized recommendations on e-commerce sites. Your interactions – buying records, how long you stay in a certain page etc – are stored in the agent’s memory. From these interactions the agent improves itself and its recommendations for you are getting better and better.
Using AI agents is a great step towards better automation, greater performance, and higher quality of responses.
But there are still challenges, like AI hallucination – when the system generates confident but incorrect information. This can corrupt the whole planning and execution process, creating unwanted outputs or interrupting execution. Training and using AI agents is a time and resource-intensive process. Finally, data privacy concerns and potential security breaches are also serious issues.
The best way to start experimenting with agents is a no-code/low-code platform, like n8n. Here you can find prebuilt components that require only basic configuration to create your first agent.
Feel free to experiment and build something useful or fun, or both!