Currently, software development and automation are painful. The software or automation team has to complete almost 95% of the process, taking care of all corner cases or the tribal knowledge accumulated over the years. If the developer misses anything, it comes back as a bug, and only the software engineer or automation developer can fix it by including the corner case. In addition to fixing the code, this process has to go through the entire lengthy software development life cycle of change management, QA and deployment in the sandbox and production.
With Kognitos, our customers develop the basic logic for their processes using English syntaxes and improve their accuracy over time by adding learnings. The learnings could be a simple one-liner, new logic to address the corner case, or a new document type. Thus, we create a way to capture tribal knowledge methodically and keep the records forever.
Neuroplasticity: Kognitos's method is not new, but this is how our human brains are designed. Babies are born with fewer neural connections. Humans learn a lot from their surroundings, and other developed humans in the first few years.
With Kognitos, when the system encounters a new condition/situation, the system prompts an exception and waits for the process owner to review the English exception. The business operations team provides guidance on addressing the new situation. Until the exception is handled, the process will not use any compute resources.
Kognitos supports multiple learnings for similar exceptions, and Gen AI guides the system to the best context based Learning for the current situation or document. Ref: https://caff-ai-nate.blogspot.com/2024/03/vector-databases.html
Unlearning an obsolete condition:
It is as easy as deleting the Learning from the UI instead of having to rewrite the entire automation.
Example:
The Main Process was activated through an email titled "CarDealer---Customer-Service-PUBLISHED-to-review-an-email-7jxxxxx@sb.kognitos.com." As you can read, we request chat GPT to classify this email concisely and send an email accordingly.
get the email body as the email text
ask koncierge
the openai model is "gpt-4o"
the task is "Review {the email text} and {the email subject}and classify the email based on the following rules: For any email inquiring about when a new vehicle will be delivered, the output should be 'Vehicle Delivery Updates'. For any email about fuel for their car, the output should be 'EV Card Issues'. For any email with mileage questions, the output should be 'Mina'. For any email where the sender is stating that they have been in an accident or their vehicle has been damaged, the output should be 'Please refer abcd.sharepoint.com/dealing-in-an-accident'. For any email inquiring about service or repairs, the output should be 'Please refer abcd.sharepoint.com/how-to-service'. Be concise."
get the above as the output
split the email sender with
the delimiter is "@"
get the above as the email values
get the first email value
get that as the username
send an email to the email sender where
the subject is "RE: {the email subject}"
the message is "Dear {the username},<br><br>Thank you for reaching out.<br><br>Based on the content of your message, I have determined that you should...<br><br><br>{the output} <br><br><br>Thank you again for your inquiry. Please feel free to reach out with any further inquiries.<br><br>Cheers,<br>Kognitos<br><br><br>From: {the email sender}<br>Date: {the email date}<br>Subject: {the email subject}<br><br>{the email body}"
As we see, we forgot to add a condition to see what happens if the information is missing from the email,
Our excellent car salesman can answer the exception used in similar situations. (Learning)
What is AgenticAI?
Agentic AI is a type of AI-driven automation that allows AI agents to perform complex tasks independently and to adapt to changing situations. It can analyse data, recognise patterns, and make decisions without human intervention.
This forays into an Agentic AI solution for your automation. As Kognitos Automation develops, all these exceptions can be used to learn as much as possible without the pitfalls of current GenAI (hallucinations and lack of predictable outcomes). These human interventions, i.e., exceptions, can be learnt. Thus, the Kognitos process created for automation can become more Agentic as the written process and LLMs evolve.
Our product has all the elements of Agentic AI except that we require minimal human intervention when encountering a new situation that needs to be considered while implementing the Kognitos process. As the Kognitos system learns these exceptions, it will eventually be trained to become Agentic. Our Kognitos system can generate new processes with minimal human input as the LLM models evolve.
Nonetheless, we are enhancing the process development lifecycle through the SDLC feature. Stay tuned for more updates.
