How to Manage Amazon Bedrock Agent File Storage

When building an agent on Amazon Bedrock, you need to decide how it should handle files. There are usually two paths. The first is knowledge retrieval, where the agent looks through a large library of documents to find answers. Bedrock Knowledge Bases handle this. The second path is artifact management, where the agent needs to read a specific file for one session or save a new file as an output.

Knowledge Bases work best for long-term document storage. They use Retrieval-Augmented Generation (RAG) to index files from an S3 bucket into a vector store. This gives the agent a way to reference technical manuals, contracts, or product specs. Session-based artifacts are temporary. You pass a specific S3 location to the agent during a chat so it can look at one spreadsheet or create a PDF report for a user.

Getting these two paths right makes your agent smarter and more capable. A legal agent might use a Knowledge Base to look up laws, but use session artifacts to draft a contract and save it to a secure folder. Distinguishing between them is the first step in a solid file storage strategy.

Helpful references: Fast.io Workspaces, Fast.io Collaboration, and Fast.io AI.

A Knowledge Base is the standard way to give your Bedrock agent access to files. The setup involves three steps: preparing data in S3, creating the Knowledge Base in the Bedrock console, and syncing the data. Your S3 bucket is the source of truth. Any file you put there can be indexed and used by your agent.

1. Prepare Your S3 Bucket Create a standard S3 bucket in the same AWS Region where you run Bedrock. Organization matters here. We suggest using a specific prefix for your agent documents. Keep in mind that individual files for indexing must be 49 MB or smaller. If you have bigger documents, you will need to split them up before uploading.

2. Create and Connect the Knowledge Base In the Amazon Bedrock console, go to Knowledge Bases and click Create. You will choose S3 as your data source and provide the S3 URI for your bucket. Bedrock will ask you to pick an embedding model, like Titan Text Embeddings, and a vector store. If you do not have a vector store ready, AWS can set up an Amazon OpenSearch Serverless instance for you.

3. Synchronize Data Indexing happens during the sync step. Bedrock reads the files from S3, breaks them into text segments, turns them into numerical vectors, and stores them in your vector database. You need to trigger a sync manually or through the API when you add or change files in S3. This keeps the agent up to date.

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Sometimes an agent needs to work with a file that does not belong in a permanent library. A user might upload a budget and ask the agent to find errors. You do not want to index this into a permanent Knowledge Base. Instead, use the S3 URI reference pattern within the agent session.

When calling the Bedrock Agent API, you can pass a sessionState object that includes an s3Location. This points the agent to the file for that specific interaction. The agent can then see the file contents to answer questions. This approach is better for sensitive user data that should not stay in a shared vector database.

Evidence and Benchmarks According to AWS technical specifications, Amazon S3 can store hundreds of trillions of objects with high durability. For AI agents, this means you can scale artifact storage to millions of sessions without hitting limits. This gives developers a reliable way to handle the many intermediate files created during agent workflows.

Amazon Bedrock agents can create files and give them to users. An agent might analyze data and then write a summary report as a Markdown file or a PDF. To store these, you use Action Groups. These are AWS Lambda functions the agent calls to do specific tasks.

When the agent needs to save a file, it sends the content to your Lambda function. The function then uses the AWS SDK to run an s3.put_object call. After the file is in S3, the Lambda function returns the S3 link or a pre-signed URL to the agent. The agent then gives this link to the user as the finished work.

This creates a clear handoff. The agent does the processing, while S3 provides the secure storage for the result. This pattern works well for any agent that produces actual files rather than just chat messages.

AWS handles the infrastructure, but managing files at scale can get messy. Fast.io helps by acting as a file layer between your Bedrock agents and your team. Instead of managing S3 buckets and permissions for every person, you can use Fast.io workspaces to organize access.

Fast.io has a free tier for developers with 50GB of storage and 5,000 monthly credits. This works well for building and testing Bedrock agents without costs. Using the Fast.io MCP server, your Bedrock agents can index files, lock them for editing, and hand off finished work to team members.

For example, you can use the Fast.io URL Import feature to move documents from Google Drive or Box into your agent's workspace. The agent can process those files and save the results in a shared folder where your team can see them immediately. This connects the AWS environment to the tools your team already uses.

To keep your Bedrock agent storage secure, follow a few simple rules. First, keep your S3 bucket and your Bedrock Knowledge Base in the same AWS Region. Accessing data across regions adds latency and costs that you can avoid with a little planning.

Second, use narrow permissions. Your Bedrock agent role should only have the access it needs. Use IAM policies to limit access to specific folders instead of the whole bucket. This is important when you handle sensitive customer data.

Finally, use metadata filtering. When you upload files to S3 for a Knowledge Base, you can add a .metadata.json file for each document. This lets you tag files with things like "department" or "security level." When the agent searches the Knowledge Base, it uses these tags to filter results, so it only finds the information authorized for that user.