Agentic generative AI assistants represent a significant advancement in artificial intelligence, featuring dynamic systems powered by large language models (LLMs) that engage in open-ended dialogue and tackle complex tasks. Unlike basic chatbots, these implementations possess broad intelligence, maintaining multi-step conversations while adapting to user needs and executing necessary backend tasks. These systems retrieve business-specific data in real-time through API calls and database lookups, incorporating this information into LLM-generated responses or providing it alongside them using predefined standards. This combination of LLM capabilities with dynamic data retrieval is known as Retrieval-Augmented Generation (RAG). For example, an agentic assistant handling hotel booking would first query a database to find properties that match the guest's specific requirements. The assistant would then make API calls to retrieve real-time information about room availability and current rates. This retrieved data can be handled in two ways: either the LLM can process it to generate a comprehensive response, or it can be displayed alongside an LLM-generated summary. Both approaches allow guests receive precise, current information that's integrated into their ongoing conversation with the assistant. In this post, we show how to implement a generative AI agentic assistant that uses both semantic and text-based search using Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents and Amazon OpenSearch.
Architecting Semantic Search with Amazon Bedrock
The foundation of any high-performance agentic system lies in the retrieval layer. When designing a solution for cloud engineers, the choice between vector-based semantic search and traditional keyword matching is critical. Amazon Bedrock provides native integration with vector databases, allowing models to understand the context and meaning behind user queries rather than just matching exact strings. This is particularly useful when dealing with unstructured data like technical documentation or customer support logs.
Consider a scenario where a DevOps engineer asks the assistant to troubleshoot a Kubernetes cluster. A simple keyword search might fail if the error message uses slightly different terminology than the documentation. Semantic search, however, maps the query to a vector space, finding relevant documents based on conceptual similarity. To implement this, you configure the embedding model within your Bedrock application. The system converts both the user query and the document chunks into numerical vectors. The retrieval process then calculates the cosine similarity between the query vector and the document vectors stored in the index. This architectural decision ensures that the system retrieves the most contextually relevant information, even if the wording differs significantly.
Implementing Hybrid Search with Amazon OpenSearch
While semantic search excels at understanding context, it can sometimes miss specific technical details or exact identifiers. This is where hybrid search becomes essential for production-grade applications. Amazon OpenSearch Service enables a hybrid approach by combining vector similarity search with keyword-based BM25 scoring. This dual-layer retrieval strategy ensures that the system captures both the semantic intent of the query and the precise technical terms required for accurate results.
In a practical configuration, you might set up a hybrid query that weights the vector score at 60% and the keyword score at 40%. This balance allows the system to prioritize semantic relevance while still respecting exact matches for critical identifiers like IP addresses, error codes, or specific API endpoints. For instance, if a user queries for "AWS Lambda timeout error," the vector search finds documents discussing timeout handling generally, while the keyword search ensures that documents explicitly containing "Lambda" and "timeout" are ranked highly. This configuration detail is vital for maintaining high accuracy in enterprise environments where precision is non-negotiable.
When building these pipelines, engineers must consider the data ingestion workflow. You typically use AWS Lambda functions to process incoming data, generate embeddings using Bedrock models, and index the results into OpenSearch. The indexing strategy should include both the vector payload and the original text for BM25 scoring. This requires careful schema design in OpenSearch to support multi-field searching. By optimizing the index settings, you can ensure low-latency retrieval, which is crucial for maintaining the conversational flow of an agentic assistant.
Orchestrating Agents with Bedrock AgentCore
Once the retrieval layer is established, the next challenge is orchestrating the agent logic. Amazon Bedrock AgentCore simplifies the creation of agentic workflows by providing a standardized interface for defining tools and knowledge bases. This abstraction layer allows developers to focus on business logic rather than low-level orchestration code. You can define specific tools for the agent to use, such as querying the OpenSearch index or calling external APIs for real-time data.
The agent's decision-making process relies on the quality of the retrieved context. When the agent receives a user prompt, it first attempts to resolve the intent. If the intent requires external data, the agent invokes the retrieval tool. The retrieved documents are then passed back to the LLM, which synthesizes the information into a coherent response. This loop must be managed efficiently to prevent latency issues. For complex tasks, the agent might need to perform multiple retrieval steps, refining its query based on initial results. This iterative process is a hallmark of advanced agentic systems and requires robust error handling to ensure the conversation remains smooth.
What This Means For You
Mastering these technologies is essential for cloud professionals aiming to build next-generation AI solutions. Whether you are preparing for the AWS ML Specialty certification or the AIF-C01 exam, understanding the nuances of hybrid RAG is a key competency. The ability to architect systems that seamlessly blend semantic understanding with precise keyword matching sets you apart in the job market. By leveraging Amazon Bedrock and OpenSearch, you can deploy agentic assistants that are both intelligent and accurate. This technical depth ensures that your solutions are production-ready and capable of handling the complex, real-world demands of modern enterprise applications.