Introduction

AI agents are all set to become the next revolution in the GenAI Paradigm. The modern-day agent’s ability to harness AI to think and reason has enabled humans to truly automate routine tasks. The creation of AI agent frameworks and architectures like AutoGen, Crew AI, LangChain, etc, have come in handy in pushing the AI agentic revolution to new heights. With enterprises rapidly adopting this much-hyped piece of technology, it becomes relevant for us to not only familiarise ourselves with the concept of AI agents but also explore its applications in our day-to-day work. In this blog, we will discover five real-life uses of AI agents for day-to-day work.

Overview

• Understand how you can save time and resources by using AI agents in your day-to-day work.
• Explore the various applications of AI agents such as email sorting, labelling, news curation, research, and more.
• Learn how to create a personalised news digest that reaches your mail directly.
• Learn how to sort your email into different pre-set labels using AI agents.
• Understand the shortcomings of traditional RAGs and how agentic RAGs can help you deal with them.

Personalised News Digest

I think we are all on the same page when I say it’s important to stay updated with relevant news and happenings of the world around us. And how do we do that now? We subscribe to all the news-aggregating apps and end up being distracted by the tons of notifications they send. Also, the ads in a lot of these apps are very distracting. Guess even news these days cannot be read without distractions!

This is where AI agents make an entry to save time by getting the news that matters to you. For this, you need to connect your AI agent with an API such as the one from News API. This lets your AI agent access news from all over the web and give you the news you need. You can also set criteria for the order in which the news should be presented and sent to your email.

Read More: How to Create Your Personalized News Digest Using AI Agents?

Email Sorting and Labelling

In the 21st century, it is pretty common for people to have their inboxes bombarded with emails of every kind. It ranges from work emails, personal emails, newsletters, marketing emails, spam, and whatnot. It requires a person’s time and effort to go through these emails, prioritise them and label them manually.

With AI agents, one can easily automate email sorting and labelling by simply running a script at regular intervals. The AI agents will read through the emails, decide which pre-defined label or category each of them should be pushed into, and finally push them to the respective category within your email. All you need to do is get your email provider’s AP so the agent can access your emails.

To know more read our article on Automating Email Sorting and Labelling with CrewAI!

Coding and Code Debugging

Modern-day enterprises are heavily reliant on technology. Now, every tech, which is software, is run on code, making software engineers an irreplaceable part of the team. Most of the time these engineers work with code and the activities involve – writing code, debugging code, improving or updating existing code, or writing test cases. This leaves very little time for them to work on innovative things, making them crave an assistant.

This is where AI agents can be deployed as valuable colleagues. They can take up routine tasks of writing code, debugging code, improving or updating existing code, or writing test cases. They help in freeing up the bandwidth for coders to work on more innovative tasks. AI agent frameworks like Crew AI and AutoGen are capable of building agents for these mundane tasks.

Also Read: How to Build an AI Pair Programmer with CrewAI?

Building Agentic RAGs

The advent of GenAI brought RAGs to the forefront, which turned out to be a phenomenal use case in day-to-day work. Traditional RAGs are widely used in customer support, internal knowledge management, document summarisation, and research, helping users retrieve and generate content based on stored information. However, these come with several limitations that hinder their effectiveness.

The foremost is their inability to access real-time data, making it impossible for them to provide up-to-date information. Additionally, the output is only as good as the input, as the system’s performance heavily relies on the quality of the data stored in the database.

Agentic AI enhances traditional RAG systems by integrating real-time data access and improving retrieval strategies. It connects to live data sources, ensuring that the information provided is up-to-date. For example, in the stock market, an Agentic AI system can retrieve and analyse livestock prices, market trends, and financial news in real time, providing traders with accurate, up-to-the-minute insights for informed decision-making.

Additionally, an agent’s capability to adaptively learn and make decisions enhances the system’s ability to retrieve the most relevant document chunks based on context. Thus minimising irrelevant results and hallucinations in responses. Agentic AI continuously learns from interactions. This increases its reliability and accuracy over time, allowing it to deliver more precise and factual answers compared to traditional RAG systems.

Also Read: A Comprehensive Guide to Building Agentic RAG Systems with LangGraph

Research Assistant

Let’s admit it – we have all hated research work simply because of the time and effort it consumes. Be it research for a project in your university or for some reports you have to submit at work; research has always been a tedious task. It employs all the mental bandwidth of a person in finding and identifying the right resources. This, in turn, ends up draining all the brain power that could have been used for more creative tasks.

With AI agents, you can easily automate this dreaded task. For example, imagine using an AI research agent specialised in economics that automatically scans and retrieves information only from trusted sources like academic journals, government databases, or industry reports. It could be set up to focus specifically on topics such as market trends or fiscal policy, ensuring the results are both relevant and credible. Not only does the agent pull the most up-to-date information, but it can also format the references for you in your preferred citation style. Now, all you have to do is extract the key insights from these curated, high-quality sources, making research faster and more efficient than ever.

Conclusion

By now, you are familiar with the amount of time that can be freed up for creative endeavours with AI agents and agentic frameworks. These intelligent assistants of yours can perform marvels for you if structured and executed well. The uses we discussed – email sorting and labelling, personalised news digest, building agentic RAGs, etc., are just a few of the real-life use cases we touched upon in this article. The possibilities with AI agents are endless, and the constant developments in these frameworks make the future exciting.

To learn more about AI agents, checkout our exclusive offering – Agentic AI Pioneer Program!

Frequently Asked Questions

My name is Abhiraj. I am currently a manager for the Instruction Design team at Analytics Vidhya. My interests include badminton, voracious reading, and meeting new people. On a daily basis I love learning new things and spreading my knowledge.