Build a use case

In Kubiya, there are limitless possible ways to use your AI Teammates. Kubiya helps you get started by offering pre-built use cases and functionalities, but it's also possible to extend your AI Teammates with custom functionality and to create custom use cases.

Regardless of whether you choose to set up an out-of-the-box use case or a custom one, it will have certain prerequisites, which include Kubiya resources that you need to set up and in some cases, ones you need to set up in 3rd-party platforms (e.g. a webhook that you need to configure in GitHub). The prerequisites for a use case vary depending on the use case.

In this page, you'll learn:

1. The concepts of Tools and Tasks in Kubiya

2. Some of the common use cases for Kubiya AI Teammates

3. How to identify the prerequisites you'll need for your use case

Key Concept #1: Tools

Kubiya offers both flexibility and extensibility, to meet you and your team's needs and preferences. To explain how, let's start with some context.

In Kubiya, the basic unit of Teammate functionality is called a tool. Functionally speaking, a tool is an atomic action that an AI Teammate can perform. From a technical standpoint, a tool is a stateless service written in Kubiya's schema that performs an operation in a given platform.

Examples of tools:

• Create a Lambda function tool

• Get the status of EC2 instances tool

• Search Jira issues tool

• Manage Kubernetes pods tool

You can connect tools to your Kubiya Teammates to equip them with actions you want them to perform. Kubiya Teammates are designed to solve painful operational problems in your organization, and while tools are powerful, they should be combined together in order to give your organization even more powerful solutions.

In order for AI Teammates to perform their expected functionalities using tools, they also need additional resources – e.g. runners, integrations, API keys, knowledge – varying according to the use case. In essence, these additional resources are dependencies needed for the AI Teammate to perform its job.

Did you know? Kubiya resources can be created via Terraform using our Terraform Provider

Here enters the concept of use cases in Kubiya.

Key Concept #2: Use Cases

Use cases group together an AI Teammate with its tools and all of the dependencies, making it easy to set up, manage and use them. Practically-speaking, use cases are Terraform modules containing each of the resources necessary for your teammate to perform specific jobs. The IaC-oriented approach allows you to get your solution up and running faster.

There are unlimited possible use cases for Kubiya AI Teammates. Kubiya makes it easy to get started by offering pre-built use cases, which are powerful solutions you can get up and running in minutes.

Common Use Cases for Kubiya Teammates

You might be wondering about the situations in which Kubiya Teammates can help you and your organization. Here are a few examples:

• Solving Ticket Queues: Have an AI Teammate take charge of your Jira queue - reviewing, resolving, updating and nudging. Consider it Done.

• JIT Cloud Permissions: Access when needed, secured when not. AI-driven JIT Permissions ensure resources are available only when required, seamlessly within your workflow, and without the hassle.

• Infrastructure Provisioning: Automate resource deployment with AI-driven JIT access, budget enforcement, and TTL management, ensuring efficient provisioning aligned with your best practices.

• Q&A Help Desk: Boost your help desk with AI-powered support that leverages your knowledge sources, processes, and tools to manage inquiries, resolve issues, and escalate complex cases – accelerating responses and freeing your team.

• Incident Response: Reimagine incident response with AI-Teammates. Quickly detect, triage, and resolve issues in real time, while closing communication loops to ensure your operations stay on track.

• Budget Enforcement: Maximize ROI with Teammate-powered budget enforcement. Manage spending in real time, eliminate idle resources, and streamline just-in-time access and approval flows.

How to Identify Prerequisites for Your Use Case

Here's a recap of some key Kubiya resources:

• Runner – A Kubiya runner enables you to run Kubiya Teammates and all of their dependencies within your Kubernetes cluster. Regardless of your use case, you need a runner. Learn more

• Integrations – Depending on the tools themselves, you will need to set up integrations so that your AI Teammate can access the platforms in which your Teammate is meant to perform operations.

• Secrets – In addition to integrations, you might need to add secrets for your AI Teammate to use. For example, API keys for platforms you're integrated with or authentication tokens for platforms for which Kubiya doesn't have a native integration.

• Knowledge – Any piece(s) of organizational knowledge that your AI Teammate needs in order to perform its responsibilities (if any). For example, the answers to questions developers might ask.

• Users – People from your organization (e.g. could be needed for granting approvals)

• Groups – Groups of Kubiya users from your organization

Questions to ask yourself

These can help you determine which resources your use case requires:

1. Do I already have a runner?

   1. All Kubiya use cases require a runner. For most use cases, either local runners or hosted runners is fine. However, if your use case involves operations in your Kubernetes cluster, then you need a local runner.

2. Which platforms does my use case involve?

   1. Kubiya will need to access those platforms either via a native Kubiya integration (GitHub, AWS, Jira, Slack, Teams) or by adding authentication tokens or API keys as Kubiya secrets.

3. Does my AI Teammate need any supplementary knowledge from my organization in order to perform its task?

   1. If the answer is yes, you will need to add it as knowledge in Kubiya.