AWS nova documentation change
Summary
Restructured documentation to focus on integration with AgentCore and Strands frameworks. Removed detailed agent implementation examples and Bedrock Agents/Converse API sections. Added new code examples for framework integrations.
Security assessment
The changes primarily reorganize content and update integration examples without addressing specific security vulnerabilities. While the AgentCore section mentions operating agents 'securely, at scale', this is a general feature description rather than documentation of security fixes or vulnerabilities. No evidence of patching security issues or addressing CVEs.
Diff
diff --git a/nova/latest/userguide/agents.md b/nova/latest/userguide/agents.md index b844337d3..f0b0a13b8 100644 --- a//nova/latest/userguide/agents.md +++ b//nova/latest/userguide/agents.md @@ -5 +5 @@ -Amazon Nova as an AI agentAgentic examplesAdditional Resources +Using Amazon Nova with AgentCoreUsing Amazon Nova with StrandsAdditional Resources @@ -30 +30 @@ Agents perform the following tasks: - * Amazon Nova as an AI agent + * [Using Amazon Nova as a foundation model in an AI agent](./agents-use-nova.html) @@ -32 +32 @@ Agents perform the following tasks: - * Agentic examples + * Using Amazon Nova with AgentCore @@ -34,45 +34 @@ Agents perform the following tasks: - * Additional Resources - - - - -## Amazon Nova as an AI agent - -To use Amazon Nova models as the foundation model in an AI agent, you can use [Amazon Bedrock Agents](https://docs.aws.amazon.com/bedrock/latest/userguide/agents-how.html) or you can [call a tool with the Converse API](https://docs.aws.amazon.com/bedrock/latest/userguide/tool-use-inference-call.html) or InvokeModel API. Amazon Bedrock Agents is a fully managed service that you can use to build and configure autonomous agents in your application. You can also use the converse API and Invoke model API to connect to other out-of-the-box agent frameworks or build your own agent framework. - -You can use placeholder variables in agent prompt templates. The variables will be populated by pre-existing configurations when the prompt template is called. For information about these placeholder variables, see [Use placeholder variables in Amazon Bedrock agent prompt templates](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-placeholders.html). - -Amazon Bedrock Agents - - -Amazon Bedrock Agents is a fully managed service for building autonomous agents. Key features include: - - * **Autonomous Agents:** Create agents that can understand user requests, break down complex tasks, and execute multi-step workflows - - * **Built-in API Invocation:** Agents automatically call APIs to take actions based on user requests - - * **Memory and Context Management:** Maintain conversation history and context across interactions - - * **Knowledge Base Integration:** Connect to knowledge bases to augment responses with relevant information - - * **Prompt Engineering:** Managed prompt templates optimized for agent workflows - - * **Code Interpreter:** Execute code to perform calculations and data analysis - - * **Multi-Agent Collaboration:** Coordinate multiple specialized agents to handle complex workflows - - - - -For more information, see [Automate tasks in your application using agents](https://docs.aws.amazon.com/bedrock/latest/userguide/agents.html). - -Converse API - - -The Converse API provides a consistent interface for building conversational agents. When building agents with the Converse API, consider: - - 1. **Store Conversation and User Data:** Maintain conversation history by including all previous messages in subsequent API calls - - 2. **Automatic Tool Invocation:** Define tools that the model can automatically invoke to perform actions or retrieve information - - 3. **Built-in Memory:** Use the conversation history to provide context for multi-turn interactions + * Using Amazon Nova with Strands @@ -79,0 +36 @@ The Converse API provides a consistent interface for building conversational age + * Additional Resources @@ -83 +39,0 @@ The Converse API provides a consistent interface for building conversational age -For more information, see [Tool use (function calling) with Amazon Nova](./tool-use.html). @@ -85 +41 @@ For more information, see [Tool use (function calling) with Amazon Nova](./tool- -AgentCore +## Using Amazon Nova with AgentCore @@ -86,0 +43 @@ AgentCore +Amazon Nova models can be used with all Bedrock AgentCore services, enabling you to deploy and operate highly effective agents securely, at scale. Key Services include Runtime, Identity, Memory, Gateway, and Observability. See Amazon Bedrock AgentCore for additional details and {placeholder} for sample code. @@ -88 +45 @@ AgentCore -AgentCore is an internal framework for building custom agents. Example implementation: +The following is an example of using Amazon Nova with AgentCore: @@ -90,0 +48 @@ AgentCore is an internal framework for building custom agents. Example implement + import boto3 @@ -92 +49,0 @@ AgentCore is an internal framework for building custom agents. Example implement - from strands.agent_core import Agent @@ -94,2 +51,4 @@ AgentCore is an internal framework for building custom agents. Example implement - model = BedrockModel( - model_id="us.amazon.nova-premier-v1:0" + # Create a Bedrock model with the custom session + bedrock_model = BedrockModel( + model_id="model_id", + boto_session=session @@ -98 +56,0 @@ AgentCore is an internal framework for building custom agents. Example implement - agent = Agent(model=model) @@ -100 +58 @@ AgentCore is an internal framework for building custom agents. Example implement -For more information, see internal AgentCore documentation. +## Using Amazon Nova with Strands @@ -102 +60 @@ For more information, see internal AgentCore documentation. -Strands +Strands provides native support for Amazon Bedrock, allowing you to use these Nova models in your agents with minimal configuration. See Strands Amazon Bedrock for more details. @@ -104,2 +62 @@ Strands - -Strands is an internal framework for building agents with Amazon Nova. Example implementation: +The following is a code example showing how to use Strands with Amazon Nova: @@ -111 +68,2 @@ Strands is an internal framework for building agents with Amazon Nova. Example i - model = BedrockModel( + # Create a Bedrock model instance + bedrock_model = BedrockModel( @@ -114 +72 @@ Strands is an internal framework for building agents with Amazon Nova. Example i - top_p=0.8 + top_p=0.8, @@ -117,94 +75,6 @@ Strands is an internal framework for building agents with Amazon Nova. Example i - agent = Agent(model=model) - -For more information, see internal Strands documentation. - -## Agentic examples - -The following examples demonstrate multi-tool agent configurations for common use cases. - -This example shows a hotel reservation cancellation system with coordinated tools: - - - toolConfiguration: { - tools: [ - { - toolSpec: { - name: "getReservation", - description: "Retrieves hotel reservation information based on the guest's name and check-in date", - inputSchema: { - json: JSON.stringify({ - type: "object", - properties: { - name: { - type: "string", - description: "Guest's full name" - }, - checkInDate: { - type: "string", - description: "Check-in date in YYYY-MM-DD format" - } - }, - required: ["name", "checkInDate"] - }) - } - } - }, - { - toolSpec: { - name: "cancelReservation", - description: "Cancels a hotel reservation using the reservation ID", - inputSchema: { - json: JSON.stringify({ - type: "object", - properties: { - reservationId: { - type: "string", - description: "Unique reservation identifier" - } - }, - required: ["reservationId"] - }) - } - } - } - ] - } - -This example shows a hotel search agent with filtering capabilities: - - - toolSpec: { - name: "searchHotels", - description: "Searches for available hotels based on location, dates, and preferences", - inputSchema: { - json: JSON.stringify({ - type: "object", - properties: { - location: { - type: "string", - description: "City or region to search" - }, - checkIn: { - type: "string", - description: "Check-in date in YYYY-MM-DD format" - }, - checkOut: { - type: "string", - description: "Check-out date in YYYY-MM-DD format" - }, - guests: { - type: "integer", - description: "Number of guests" - }, - priceRange: { - type: "object", - properties: { - min: { type: "number" }, - max: { type: "number" } - } - } - }, - required: ["location", "checkIn", "checkOut", "guests"] - }) - } - } + # Create an agent using the BedrockModel instance + agent = Agent(model=bedrock_model) +