Overview
Search Agent Fleet
Search Agent Fleet is an orchestrated multi-agent system that routes your queries to four specialist AI agents and synthesizes cited, coherent answers - no manual tool selection required.
How It Works
A central orchestrator receives each query and determines which specialist agent(s) to invoke:
- Research Agent - Searches academic sources including Semantic Scholar, arXiv, and DBLP to surface relevant papers and citations.
- Web Search Agent - Queries the live web via Tavily and Brave Search to find current information and online resources.
- Vector Search Agent - Runs entirely inside the container to search your own indexed documents. No external vector database or API key required.
- Database Search Agent - Queries your SQL and MongoDB data stores to retrieve structured information.
The orchestrator synthesizes results from one or more specialists into a single cited answer, giving you comprehensive coverage across multiple knowledge domains in a single request.
Key Benefits
- Intelligent routing - The orchestrator automatically selects the right specialist(s) for each query, eliminating the need to manually choose tools or configure search strategies.
- Self-contained vector search - The embedding model is baked into the container image. No external vector database service or additional API keys are needed for document search.
- Runs in your AWS account - The product deploys entirely within your own AWS environment using your own credentials. No data leaves your account except to the disclosed third-party APIs you configure.
- Single invocation metering - Each /invocations turn counts as one charge regardless of how many specialists are invoked, keeping costs predictable.
External Dependencies
This product requires an ongoing internet connection and the following external services using API keys you supply:
- Google Gemini (generativelanguage.googleapis.com or Vertex AI) - Used for the orchestrator's and specialists' LLM reasoning on every request.
- Tavily and Brave Search - Used for web search queries.
- Semantic Scholar, arXiv, DBLP - Used for academic research queries.
Your prompts are sent to Google for processing. HuggingFace is not required at runtime since the embedding model is included in the image.
Deployment
Search Agent Fleet is delivered as a container-based AI agent running on AWS Bedrock AgentCore Runtime. It deploys in the us-east-1 region. Review the Deployment Guide's full environment-variable contract before deploying to ensure all required API keys and configurations are in place.
Who Is This For?
Search Agent Fleet is built for teams that need to query across multiple knowledge sources - academic literature, the open web, proprietary documents, and structured databases - and receive unified, cited answers without building and maintaining separate search pipelines.
Highlights
- One orchestrator automatically routes each query to the right specialist agent (or several) and synthesizes a cited answer. You never need to manually select tools or configure search strategies. Whether your question requires academic research, live web results, document search, or database queries, the system determines the optimal path and delivers a unified response.
- Vector search runs entirely inside the container with the embedding model baked into the image. No external vector database service, no additional API keys, and no extra infrastructure to manage. Your indexed documents are searchable out of the box without depending on third-party vector search providers.
- Runs entirely in your AWS account on your own credentials using AWS Bedrock AgentCore Runtime. No data leaves your account except to the third-party APIs you explicitly configure (Google Gemini, Tavily, Brave Search, Semantic Scholar, arXiv, DBLP). You maintain full control over your data and access policies.
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Description | Cost/unit |
|---|---|---|
Invocation | One orchestrated query across research, web, vector, and DB search agents. | $0.03 |
Vendor refund policy
Refunds are considered on a case-by-case basis for confirmed billing errors or verified product defects (e.g., an invocation charge with no completed orchestrator response), reported within 14 days of the charge. Refunds are not issued for buyer-side misconfiguration or third-party API costs (Gemini, Tavily, Brave, Semantic Scholar). Contact support@9dtechnologies.com with your AWS account ID and MeteringRecordId to request a refund.
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Delivery details
Container deployment
- Amazon Bedrock AgentCore
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Reliability and security fixes since 1.0.6: fixed a real ARM64 crash (SIGSEGV) that could occur during document indexing under sustained use, by switching the vector-search embedding model to a safer implementation. Improved index persistence across container restarts. Added a self-service recovery path and clearer error messages if a vector collection needs re-indexing after this update. Reduced image size by removing an unused embedding model that was never actually used at runtime. Fixed a PostgreSQL connectivity gap (missing driver) and a database-credential redaction gap in the Database Search agent's responses.
Additional details
Usage instructions
This product requires an ongoing internet connection. The following ongoing external services are required, using API keys you supply:
- Google Gemini (API host generativelanguage[.]googleapis[.]com, or Vertex AI): used on every request, for the orchestrator's and specialists' reasoning.
- Tavily and Brave Search: web search queries.
- Semantic Scholar, arXiv, DBLP: research queries.
HuggingFace is not required at runtime. The embedding model is baked into the image and the container runs fully offline for embeddings. Your prompts are sent to Google to be processed.
Required IAM permissions on the AgentCore Runtime execution role. This product calls the AWS Marketplace Metering Service directly from inside the container to bill your subscription. The "Create default role" option in the AgentCore Runtime console does NOT include this permission. Attach this AWS managed policy to the execution role before launching:
- AWSMarketplaceMeteringFullAccess: required for billing (MeterUsage). Do not substitute AWSMarketplaceMeteringRegisterUsage, despite the similar name; it grants a different, unrelated action.
Without it, the container self-terminates at startup rather than running unmetered; this is expected anti-fraud behavior, not a bug.
A second, separate IAM permission is also required, not covered by the policy above. This product also verifies your subscription's agreement status directly from inside the container at startup, calling aws-marketplace:SearchAgreements and aws-marketplace:DescribeAgreement. No AWS managed policy bundles just these actions; attach this inline policy to the execution role:
{ "Effect": "Allow", "Action": [ "aws-marketplace:SearchAgreements", "aws-marketplace:DescribeAgreement", "aws-marketplace:GetAgreementEntitlements" ], "Resource": "*", "Condition": { "ForAllValues:StringEquals": { "aws-marketplace:AgreementType": ["PurchaseAgreement"] }, "StringEquals": { "aws-marketplace:PartyType": "Acceptor" } } }
Using Database Search. There is no connection-string configuration field: you include your database's connection string directly in your prompt text, e.g. "Connect to postgresql://user:pass@host:5432/dbname and list the tables". Supported: SQLite, PostgreSQL, MySQL, and MongoDB. Only read-only statements are permitted (SELECT / WITH...SELECT / SHOW / DESCRIBE / EXPLAIN for SQL); mutations are rejected. Your database must be reachable from the runtime's network; if it's private, run the AgentCore Runtime in VPC mode with connectivity to it. Connection strings are masked in logs and never echoed back in a response, but treat each prompt as sensitive since it carries live database credentials.
Using Vector Search durably. Without extra configuration, indexed documents are lost on every restart; fine within one session, not for a persistent knowledge base. To persist across restarts, mount an EFS access point into the runtime and point CHROMA_PERSIST_DIR at the mount path. Mounting EFS (or S3 Files) is required either way, since this product has no upload endpoint of its own.
Support
Vendor support
Search Agent Fleet is provided under the AWS Standard Contract for AWS Marketplace. For questions about deploying or configuring the product, please refer to the Deployment Guide's environment-variable contract for required API keys and setup instructions.
If you experience issues with the product, including deployment problems, agent routing behavior, or metering questions, reach out directly at support@9dtechnologies.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.