Hello everyone and welcome.
In today's session we will learn about agents and Amazon bedrock.
What are agents?
An agent helps your end user complete actions based on organizational data and user input.
An agent orchestrates interaction between foundation models, data sources, software applications,
and user conversations.
Agents automatically call APIs to take actions and invoke knowledge bases to supplement information
to these actions.
Developers can save weeks of development effort by interacting agents to accelerate the delivery of
generative AI applications.
What are the different tasks that agent can perform?
They can extend foundation models to understand user requests and break down the tasks that the agent
must perform.
In smaller steps, they collect additional information from a user through natural conversation.
They take actions to fulfill a customer's request by making API calls.
Agents can also augment performance and accuracy by querying the data sources.
What are the prerequisite steps for agents to use an agent?
You perform the following steps.
Configure an agent and add at least one of the following components.
You can add action groups that agent can perform.
You can associate a knowledge base with agents to augment the agent's performance.
Optionally, you can customize the agent's behavior by modifying the prompt templates for the pre-processing,
orchestration, knowledge based response generation, and post-processing steps that the agent performs.
You can then test your agent in the bedrock console.
So what are the different components within the agents.
The core component of agents are as follows.
Foundation model agent invokes foundation model to interpret user input and subsequent prompt in its
orchestration process.
Instructions.
You can write instructions that describe what the agent is designed to do.
Then you can also have one of the following action groups.
You can define actions that agents should perform for users by providing the following resource.
You can either invoke OpenAPI schema to define the API operations that agent can invoke to perform its
tasks, or you can write a Lambda function with input and output.
The other important aspect of the components that agent has is knowledge bases.
You can associate knowledge base with an agent.
You can also provide prompt templates.
Prompt templates are the basis for creating prompts that need to be provided to the functional model
or the foundation model.
So how does it look like doing the build time?
Here's the build time diagram of the agent.
If you notice here, center is the agent that interacts with action groups on the left hand side.
It has open API schema or Lambda.
It can also interact with the knowledge bases to get the context of the relevant use case.
It pulls the prompt from the prompt store and there's a default prompt templates available that it uses
as well.
So this is the build time phase of the agent that has different components and how it interacts with
them.
Let's understand in our next video how we can use these components and create a runtime environment
for agentic behaviors to execute.
Thank you and I'll see you in the next video.