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Amazon Bedrock FAQs changed AWS, Oct 4, 2025

AWS · Oct 4, 2025 · 30 added, 16 removed · found in an Internet Archive capture

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  1. Added: Choice of leading FMs: Amazon Bedrock offers an easy-to-use developer experience to work with a broad range of high-performing FMs from leading AI companies. You can quickly experiment with a variety of FMs in the playground, and use a single API for inference regardless of the models you choose, giving you the flexibility to use FMs from different providers and keep up to date with the latest model versions with minimal code changes.
  2. Removed: Choice of leading FMs: Amazon Bedrock offers an easy-to-use developer experience to work with a broad range of high-performing FMs from Amazon and leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, and Stability AI. You can quickly experiment with a variety of FMs in the playground, and use a single API for inference regardless of the models you choose, giving you the flexibility to use FMs from different providers and keep up to date with the latest model versions with minimal code changes.
  3. Added: Amazon Bedrock Agents is a fully managed capability that makes it easier for developers to create generative-AI based applications that can complete complex tasks for a wide range of use cases and deliver up-to-date answers based on proprietary knowledge sources. In just a few short steps, Amazon Bedrock Agents automatically breaks down tasks and creates an orchestration plan-without any manual coding. Agents created in Bedrock can securely connect to company data through an API, automatically converting data into a machine-readable format, and augmenting the request with relevant information to generate the most accurate response. Agents can then automatically call APIs to fulfill a user's request. As a fully managed capability, Amazon Bedrock Agents removes the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing developers to use generative AI to its full extent throughout their organization.
  4. Added: AgentCore enables developers to accelerate AI agents into production with the scale, reliability, and security, critical to real-world deployment. AgentCore provides tools and capabilities to make agents more effective and capable, purpose-built infrastructure to securely scale agents, and controls to operate trustworthy agents. AgentCore capabilities are composable and work with popular open-source frameworks and any model, so you don't have to choose between open-source flexibility and enterprise-grade security and reliability. Learn more visit Amazon Bedrock AgentCore .
  5. Added: AgentCore is designed for organizations who want to move AI agents from proofs of concept built using open source or custom agent frameworks to production. It serves developers and enterprises who need robust infrastructure to support dynamic execution paths at runtime, controls to monitor behavior, powerful tools to enhance agents, and the flexibility to adapt as the landscape evolves.
  6. Added: What key capabilities does AgentCore provide?
  7. Added: AgentCore includes services and tools that offer unique capabilities. These include:
  8. Added: Runtime : A secure, serverless runtime purpose-built for deploying and scaling dynamic AI agents and tools.
  9. Added: Memory : Makes it easy for developers to build context-aware agents by eliminating complex memory infrastructure management while providing full control over what the AI agent remembers.
  10. Added: Gateway : Provides a secure way for agents to discover and use tools along with easy transformation of APIs, Lambda functions, and existing services into agent-compatible tools.
  11. Added: Browser tool : Provides a fast, secure, cloud-based browser runtime to enable AI agents to interact with websites at scale.
  12. Added: Code Interpreter : Enables AI agents to write and execute code securely in sandbox environments, enhancing their accuracy and expanding their ability to solve complex end-to-end tasks.
  13. Added: Identity : Enables AI agents to securely access AWS services and third-party tools on behalf of users or autonomously with pre-authorization.
  14. Added: Observability : Gives developers complete visibility into agent workflows to trace, debug, and monitor AI agents' performance in production environments. With support for OpenTelemetry compatible telemetry and detailed visualizations of each step of the agent workflow, AgentCore enables developers to easily gain visibility into agent behavior and maintain quality standards at scale.
  15. Added: Which agent frameworks does AgentCore support?
  16. Added: AgentCore works with any open source agent framework including popular open-source frameworks like CrewAI, LangGraph, Strands Agents, and custom frameworks.
  17. Added: I am using Amazon Bedrock Agents today. Should I switch to AgentCore?
  18. Added: If you are using Amazon Bedrock Agents today, you can continue to use it. However, if you need additional functionalities such as being able to use any agent authoring framework (such as Strands Agents, Crew AI, LangGraph, LangChain, or LlamaIndex) and use any model along with fine-grained control on identity, memory, and observability, we recommend using AgentCore. AgentCore also provides upgraded tools and infrastructure for running agents at scale including identity, customizable long-term memory, an enhanced code interpreter tool, built-in browser tool, observability, native support for Model Context Protocol for connection to thousands of tools and a runtime with industry-leading execution time, payload size, and complete session isolation. To help customers take advantage of these improvements, we will have an option to easily export existing Bedrock Agents configurations as code that is compatible with Strands (for orchestration) and AgentCore (for production-grade deployment and more).
  19. Removed: Amazon Bedrock Agents are fully managed capabilities that make it easier for developers to create generative AI-based applications that can complete complex tasks for a wide range of use cases and deliver up-to-date answers based on proprietary knowledge sources. In just a few short steps, Amazon Bedrock Agents automatically break down tasks and create an orchestration plan-without any manual coding. The agent securely connects to company data through an API, automatically converting data into a machine-readable format, and augmenting the request with relevant information to generate the most accurate response. Agents can then automatically call APIs to fulfill a user's request. For example, a manufacturing company might want to develop a generative AI application that automates tracking inventory levels, sales data, supply chain information and that can recommend optimal reorder points and quantities to maximize efficiency. As fully managed capabilities, Amazon Bedrock Agents remove the undifferentiated lifting of managing system integration and infrastructure provisioning, allowing developers to use generative AI to its full extent throughout their organization.
  20. Removed: How can I connect FMs to my company data sources?
  21. Removed: You can securely connect FMs to your company data sources using Amazon Bedrock Agents. With a knowledge base, you can use agents to give FMs in Amazon Bedrock access to additional data that helps the model generate more relevant, context-specific, and accurate responses without continually retraining the FM. Based on user input, agents identify the appropriate knowledge base, retrieve the relevant information, and add the information to the input prompt, giving the model more context information to generate a completion.
  22. Removed: What are some use cases for Amazon Bedrock Agents?
  23. Removed: Amazon Bedrock Agents can help you increase productivity, improve your customer service experience, and automate workflows (such as processing insurance claims).
  24. Removed: How do Amazon Bedrock Agents help improve developer productivity?
  25. Removed: With agents, developers have seamless support for monitoring, encryption, user permissions, versioning, and API invocation management without writing custom code. Amazon Bedrock Agents automate the prompt engineering and orchestration of user-requested tasks. Developers can use the agent-created prompt template as a baseline to further refine it for an enhanced user experience. They can update the user input, orchestration plan, and the FM response. With access to the prompt template developers have better control over the Agent orchestration.
  26. Removed: With fully managed agents, you don't have to worry about provisioning or managing infrastructure and can take applications to production faster.
  27. Added: Sensitive information filters - Configure filters to help block or mask sensitive information, such as personally identifiable information (PII), or custom regex in user inputs and model responses. Blocking or masking is done based on probabilistic detection of sensitive information in standard formats in entities such as SSN number, Date of Birth, address, etc. This also allows configuring regular expression-based detection of patterns for identifiers.
  28. Added: Contextual grounding checks - Help detect and filter hallucinations if the responses are not grounded (e.g., factually inaccurate or new information) in the source information and irrelevant to user's query or instruction.
  29. Removed: Sensitive information filters - Configure filters to help block or mask sensitive information, such as personally identifiable information (PII), or custom regex in user inputs and model responses. Blocking or masking is done based on probabilistic detection of sensitive information in standard formats in entities such as SSN number, Date of Birth, address, etc. This also allows configuring regular expression based detection of patterns for identifiers.
  30. Removed: Contextual grounding check - Help detect and filter hallucinations if the responses are not grounded (e.g., factually inaccurate or new information) in the source information and irrelevant to user's query or instruction.
  31. Added: What are the safeguard tiers in Bedrock Guardrails?
  32. Added: Bedrock Guardrails provides safeguard tiers for content filters and denied topics with distinct performance characteristics and expanded language support for different application requirements and use cases. There are two tiers with Bedrock Guardrails: Standard tiers that provide robust performance with comprehensive language support for up to 60 languages. This tier requires opting into cross-region inference. Classic tier with established functionality and limited language support of 3 languages.
  33. Added: What languages are supported by Amazon Bedrock Guardrails?
  34. Added: With Standard Tier, Bedrock Guardrails supports 60 languages with varying support depending on the policy. The details of language support can be found here . With Classic Tier, Bedrock Guardrails supports English, French, and Spanish languages. Any unsupported language in either Classic or Standard Tier will result in ineffective results.
  35. Removed: What languages are supported with Bedrock Guardrails?
  36. Removed: Currently, Amazon Bedrock Guardrails supports English, French, and Spanish in natural language. Using any other language will result in ineffective results.
  37. Added: There are six guardrail policies, each with different off-the-shelf protections:
  38. Added: Sensitive information filter - These come with 30+ off the shelf PIIs. It can be further customized by adding customers' proprietary information that are sensitive.
  39. Removed: Sensitive information filter - These come with 30+ off the shelf PIIs. It can be further customized by adding customer's proprietary information that are sensitive.
  40. Added: Automated Reasoning checks - This safeguard validates against a completely custom domain knowledge policy that you can create and refine starting from a simple document. Using formal verification techniques, Automated Reasoning checks identifies correct model responses with up to 99% accuracy to minimize hallucinations.
  41. Added: Yes, Amazon Bedrock Guardrail APIs help customers run automated tests. "Test case builder" maybe something you want to use prior to deploying guardrails in production. There is no native test case builder yet. For ongoing monitoring of production traffic, guardrails help provide detailed logs of all violations for each input and output, so that customers can granularly monitor every input coming and going out of their gen AI application. These logs can be stored in Amazon CloudWatch or S3 and can be used to create custom dashboards based on customers' requirements.
  42. Removed: Yes, Amazon Bedrock Guardrail APIs help customers run automated tests. "Test case builder" maybe something you want to use prior to deploying guardrails in production. There is no native test case builder yet. For ongoing monitoring of production traffic, guardrails help provide detailed logs of all violation for each input and output, so that customers can granularly monitor each and every input coming and going out of their gen AI application. These logs can be stored in CloudWatch or S3 and can be used to create custom dashboards based on customers' requirements.
  43. Added: Using an Automated Reasoning Policy, Automated Reasoning checks can point out both accurate claims and factual inaccuracies in content. For both accurate and inaccurate statements, Automated Reasoning check provides verifiable, logical explanations for its output. Automated Reasoning check requires upfront involvement from a domain expert to create a Policy and only supports content that defines rules. On the other hand, Contextual grounding checks in Bedrock Guardrails uses machine learning techniques to ensure the generated content closely follows the documents that were provided as input from a knowledge base, without requiring any additional upfront work. Both Automated Reasoning Checks and Contextual Grounding provide their feedback in the Guardrail API output. You can use feedback to update the generated content.
  44. Removed: Using an Automated Reasoning Policy, Automated Reasoning checks can point both accurate claims and factual inaccuracies in content. For both accurate and inaccurate statements, Automated Reasoning check provides verifiable, logical explanations for its output. Automated Reasoning check requires upfront involvement from a domain expert to create a Policy and only supports content that defines rules. On the other hand, Contextual grounding checks in Bedrock Guardrails uses machine learning techniques to ensure the generated content closely follows the documents that were provided as input from a knowledge base, without requiring any additional upfront work. Both Automated Reasoning Checks and Contextual Grounding provide their feedback in the Guardrail API output. You can use the feedback to update the generated content.
  45. Added: How are you able to deliver 99% correctness of model responses using Automated Reasoning checks in Bedrock Guardrails?
  46. Added: We use Automated Reasoning formal verification techniques alongside LLMs to identify up to 99% of the valid statements. Automated Reasoning checks' feedback on content points out ambiguity and suggests corrections for wrong or incomplete answers. The feedback makes it easy to rewrite answers until they are judged valid.
About this change
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aws.amazon.com/bedrock/faqs/
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Help article
Text hash
23994df84a23 to 9f8ce63e77fc
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