Entrio’s Agentic AI Approach to Data Collection

Data moves very fast these days, as do the people and organizations that consume it. As technology advances, so does our need for obtaining the latest information, regardless of who we are and what we do. 

Obtaining new types of data, as well as, maintaining existing data and enhancing it, is a huge challenge and can be very complex. It doesn’t have to be. 

Entrio has developed a unique approach to data processing, which overcomes all of these challenges. By leveraging Agentic AI, we’ve created a framework that delivers the data enterprise architects need, reducing time, effort, and frustration.

Data challenges

Before we dive into how Entrio solves this issue, let’s outline the specific challenges:

• New datapoints are being created every day. Information we didn’t know existed last week can be on everyone’s mind in a matter of days.

• Data points are not static. Updates need to happen frequently. We cannot afford to have data that is out of date. But it is also important to have historical  or "old" data in certain cases such as name changes, versions, employee count, etc.

• Data is tricky. Whether new or not, our data needs to be clean, whole, and accurate, which is often not the case.

• Data travels. Having great data is only half of the battle. It’s equally important that we have it in a form that we can easily view, gain insights from, and perform actions with.

The traditional solution to these issues is to deploy several departments of highly trained specialists to work on both automated and manual processes related to data wrangling, with each department focusing on a specific data type, function, or role.

Entrio does not have hundreds of data personnel who handle complex processing pipelines. Instead, we’ve decided to solve the issue using Generative AI. 

Why did Entrio choose Generative AI?

The answer is relatively simple. AI is meant to replace human actions. If we cannot bring in  hundreds of people who will work on our pipeline, we will leverage AI to simulate the actions of those people. Not only is AI currently mature enough to do this, it also excels at it. 

AI is great but why go for Agentic AI specifically?

Our goal was to go beyond simulating human actions via Generative AI and the use of workflows. Our goal was to simulate the departments which collect data. It is not enough to replace a worker because as you all know, a team doesn’t consist of different people who are completely isolated from each other. A team communicates, it collaborates and consolidates on different matters, which is where Agentic AI comes in.

Agentic AI is an advancement within the field of Generative AI which helps us simulate a network of Agents. These Agents are fueled by powerful reasoning models which can make decisions based on many many factors, which is perfect because we want to simulate a Manager and Team Leaders.

Now, a team doesn’t just consist of leadership, it also has employees. Moreover, there are also tools that each team has access to, which help them achieve their goals. In order to reproduce this behavior, Agents can (and should) be equipped with different prompts and tools that can help them perform their work. For example, if I have created a weather forecast bot, I’d like it to have access to an API which returns the current weather or prompts/models that help enrich the information and predict how the weather will be tomorrow based on our data.

Just like in any organization, a single team does not have access to everything and neither do Agents (unless you want them to). Each Agent has a Route directing it to the specific tools it needs in order to function. 

Entrio’s unique approach to Agentic AI

Here at Entrio, we’ve taken the Agentic AI approach to the next level by adding the following ingredients into the mix:

1. We are using our unique internal data in order to make our agents smarter. Entrio has a vast amount of information related to technology that we have been cultivating for years. 
2. We’ve created a system that allows us to reuse existing tools/prompts for different agents without having to duplicate/slightly alter them. This enables us to save a lot of time and focus on what’s important - more data! 
3. Entrio employs an approach that separates the QA, security, and auditing functions from the regular process. Instead, we have a special architecture that allows us to control agents and alert engineers/analysts based on different insights.
4. The data doesn’t stop there. By adding Entrio’s continuously expanding taxonomy as well as leveraging existing datapoints and features to enrich our collections, we provide a living and breathing reference catalog which can be used for anything from capability mapping to analytics to decision making.

Wrapping up

Data collection is hard. It requires a lot of knowledge, many specialists, and tons of processes. 

The traditional approach of bringing in more people to collect more data is not scalable. Data grows fast and changes fast. Adapting to these changes by adding people to the mix is not a long term solution.By using Agentic AI with a layer of specialized data, additional QA, and security features as well as adding features which enable analytics and decision making to the mix, we avoid having non-scalable solutions and make sure the correct data reaches the correct people on time.