How to use AI Agents to supercharge the team’s daily workflow and pipeline?

With the booming of AI since the end of 2022, Autonomous AI Agent has shown their power to improve productivity in our daily work. The appearance of AI Agents showed huge potential to change people’s daily work. Within the past few months, we saw tremendous options come out for people to build their own AI agents to speed up their workflow.

So how do we use AI Agents in daily works flows? the following are the most common cases for most teams to use AI Agents. Integrating AI agents into the build process can help automate and streamline various tasks, improving efficiency and reducing errors. Many people can see the potential of AI Agents in their work but they don't have an easy-to-use solution to quickly apply a bunch of AI Agents to their daily work.

ILLA Cloud can provide a really good solution to such scenarios. You can embed the tailored AI Agents built with ILLA.AI when you are building your Internal Apps at ILLA Cloud. For example, there are tons of AI Agents in the current market. So you might need to access different websites to use those Agents in your workflows. However, that will be tedious and messy if you have tons of AI Agents from tons of service providers. You won’t be able to speed up your workflow in that case. Now you can use ILLA Cloud and ILLA.AI to build a perfect workflow to apply multiple AI Agents. ILLA.AI supports multiple mainstream LLMs such as Claude 2, GPT-4, and Llama 2, so you can basically build any kind of AI Agent based on that.

One more exciting thing is you can keep polishing your prompt based on the team members’ feedback to fulfill your team’s specific needs. For example, there are tons of YouTube title, Tag, and Description generators, but your niche is on Music or Transformers Toys. So you actually can build your AI Agents specifically for your niches and keep polishing the prompt while you are using it. There are also many YouTube operation-related generators in our community. We encourage other teams to keep polishing those open-sourced AI Agents in our community according to their demands.

However, in real daily work scenarios, most teams will need more than one AI agent in the whole process. So after you build tons of AI Agents, you can actually use ILLA Cloud to build a front end as a marketplace to gather different AI Agents for your team. Anyone from your team can access the AI Agent built on your customized needs. This can dramatically improve the entire team workflow and supercharge your productivity with it.

Here is an example from our ILLA Cloud user. The team is using ILLA builder to create this Panel with many different AI Agents and can only be accessed by his team. Different teams are able to visit their internal AI Agents marketplace to use those AI Agents tailored for themselves.

Another feature of ILLA Cloud which you can help to coordinate your AI Agents, is Event Handler. You are able to dispatch multiple AI agents for different tasks. For example, if you would like an AI Agent to give you some feedback about a brunch of data set. Let’s call this AI Agent a data analyst. Meanwhile, you will need another AI Agent to write an email based on the analysis result from the Data Analyst. In this case, you are able to use the event handler to automate the entire process.

Once the Data Analyst finishes generating the result from your data resource,(Whether it’s from Airtable, Google Sheets, or MySQL and PostgreSQL) You can set up an Event handler to let the second Agent take over, such as write an email based on the first agent’s result, or generate a blog based that. The rest just need your imagination.

With those few cases, you are able to see the potential of a combination between ILLA Cloud and multiple AI Agents. So Please go check out our products ILLA.AI and ILLA Cloud, and see if you can use our product to build an AI Agent and Internal Apps to speed up your entire workflow.

As many people can see, the entire process provided by ILLA Cloud to apply AI agents is much easier to build than the current process of integrating with AI Agents. For example, the following steps are provided by GPT-3.5 for us to use AI Agents, and here's a general overview of the process:

1. Define your requirements: Determine the specific tasks or problems you want the AI agents to assist within your workflow. This will help you identify the right AI technologies and agents to integrate.
2. Select AI technologies: Research and select the AI technologies that align with your requirements. This may include natural language processing (NLP) models, machine learning algorithms, or pre-trained AI models.
3. Identify data sources: Determine the data sources required to train and deploy the AI agents. This could include structured data from databases, unstructured data from documents or websites, or real-time data from APIs.
4. Data preparation: Clean and preprocess the data to ensure it's in the right format for training and inference. This may involve tasks like data cleaning, feature engineering, and data augmentation.
5. Model training: Train the AI models using the prepared data. This step depends on the specific AI technologies you've chosen. For example, if you're using a pre-trained NLP model, you may fine-tune it on your domain-specific data. Alternatively, if you're using machine learning algorithms, you may train them on your labeled dataset.
6. Integration with workflow: Determine how the AI agents will fit into your existing workflow. This may involve developing custom software or leveraging existing tools and platforms. For example, you might integrate AI agents into your web applications, chatbots, or backend systems.
7. Testing and validation: Thoroughly test the AI agents to ensure they perform as expected. Validate their accuracy, speed, and reliability. This step is crucial to ensure the AI agents are providing the intended value and meeting your requirements.
8. Deployment and monitoring: Deploy the AI agents into your production environment. Set up monitoring systems to track their performance and make any necessary adjustments or improvements as you gather feedback and usage data.
9. Iterative improvement: Continuously monitor and improve the AI agents based on user feedback and evolving requirements. This may involve retraining models, fine-tuning parameters, or adding new features.

However now with ILLA Cloud, the entire process will be much shortened just like the following steps:

1. Find the AI Agents in our community that might fit your cases. If not you can just create one can adjust the prompt based on your needs.
2. Simply use the iFrame component to insert the AI Agent or call the AI Agent at Action List->Create New.
3. Add EventHandler with the specific act to the AI Agent, allowing different AI Agents to take over different tasks in a workflow.

Above all are the few steps you need to apply different AI Agents in your real workflows. If you believe this is worth a try, click this banner to start building your AI Agents and App Marketplaces right away.