Your Autonomous AI Assistant is (Almost) Ready to Meet You
Picture this: an assistant who never tires, can multitask effortlessly, learns in real-time, and performs tasks you’d rather not do yourself.
All of this, passing only a goal description.
This is the promise behind Autonomous Agents — an advanced form of artificial intelligence that can manage everything from scheduling meetings to generating leads.
Amazing, isn’t it?
Now, this might sound like the stuff of science fiction, and it’s true we’re not entirely there yet. For complex objectives, our agents can lose their way after a while and throw in the towel. However, it’s far from a lost cause. When designed and used effectively, Agents serve as a potent upgrade to traditional large language models (LLMs).
And as with everything in the AI industry, they are evolving fast.
So, How do Autonomous Agents work?
Agents are a new software that enhances LLMs capabilities through two new superpowers: Task Breakdown & Management and Access to the external world.
Task Management
It’s no secret that ChatGPT, like other LLMs, can occasionally give nonsensical replies to complex requests. To address this, researchers developed two frameworks — Chain-of-Thought and ReAct — that allow the language models to dynamically reason and adjust their action plan according to external data.
In simple terms, the Agents leverage the LLM model to identify a user’s goal, break it down into smaller, manageable tasks, and work through them sequentially. They assess their results at each stage, deciding whether to proceed to the next task, create new ones, revisit the current one until they achieve the desired outcome, or stop processing.
Let’s illustrate this with a prompt example:
Write a weather report for Barcelona today
Instead of trying to answer this prompt directly, the Agent would first break it down into several steps.
Goal
Write a weather report for Barcelona today.Tasks
1. Gather weather conditions data for Barcelona today.
2. Analyze the temperature data and create a weather report.
3. Publish the weather report.
And then process it one at a time, analyzing the responses to see if it’s progressing or not.
For a more thorough explanation of the ReAct framework, look at this short article or the original paper.
Toolkit Access
The second superpower of Agents is that they are given access to a set of tools that can interact with the internet. Depending on the chosen tools, Agents can search Google, send e-mails, save information on your database, and more.
If we look at the first task in our previous example:
Tasks
1. Gather weather conditions data for Barcelona today.Well, ChatGPT and most LLMs do not have direct access to the internet. So it doesn’t matter how smart it gets; it would still fail to answer correctly and stop there.
That’s where the toolkit changes the game; every time you create an Agent, you pass it a list of tools with a description of when to use it.
For example, if your Agent needs to use a Google search, you would pass him a tool with the following description:
Useful when you need to search Google to answer questions about current events.
Now when the agent runs through their task list, it will identify its needs information on the internet, look at the toolkit, and pick the best tool for the job based on its description. In this case, the Search tool.
Agent Simulation
Let’s walk through a typical request process to an Agent using our previous example:
User Prompt
Write a weather report for Barcelona today
Step 1
Task Breakdown: The Agent will process the prompt and create a goal and an initial list of tasks.
Goal
Write a weather report for Barcelona today.Tasks
1. Gather weather conditions data for Barcelona today.
2. Analyze the temperature data and create a weather report.
3. Publish the weather report.
The Agent will process the first task, look at the description of the tools available, identify it needs to use the Search tool to answer current-day events and execute it.
This will return some data about the weather conditions for Barcelona today.
Step 3
The Agent will analyze if it has enough information to move to the next task. In this case, the answer is yes, so it moves on. After analyzing the data, it writes the report below:
Today in Barcelona, Spain, a maximum temperature of 29°C and a minimum temperature of 24°C are expected, with cloudy intervals.
Step 4
After a quick analysis, it concludes task 2 is finished and moves on to task 3. To publish the weather report, the agent identifies it will need to use the Weather Publisher tool. It runs it, passes the report created before, and publishes it.
Step 5
The agent concludes the goal is accomplished, informs the users, and stops until the next request.
This brings us to the end of our introduction to how AI Agents work.
In the following article, we’ll be diving into how you can create your own agent — no coding skills required — using Flowise.
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