Run an AI SRE Agent Entirely Inside AWS with Bedrock and S3: AURA

An on-call question returns the threshold and the escalation owner from your own runbooks, and the answer comes back without a call to anyone outside.

Learn more about AURA → https://www.mezmo.com/aura
Get started today → https://github.com/mezmo/aura
Get deployment help → https://www.mezmo.com/contact

AURA runs against Bedrock as its model provider, using Claude Sonnet 5 served by AWS in the same region. Authentication is the normal AWS credential chain: a profile on a laptop, an IAM role in EKS.

The knowledge base is a managed Bedrock knowledge base, declared with its ID. No embedding model appears in the config, because AURA does not do the retrieval. Bedrock does, inside the account. The documents are markdown files in an S3 bucket: ops runbooks, an incident policy, and an error code reference. The knowledge base syncs straight from the bucket, and access control uses IAM like everything else.

Two questions follow, of the kind an on-call engineer would ask. The tool call is visible along with the exact query run against the knowledge base, and the answer comes back out of the runbooks: 250 failed transactions a minute, with rollback going through the release captain. The second question turns on an error code that exists nowhere on the public internet, only in these documents, and the answer is still correct.

Then the request path itself. AURA talks to Bedrock for the model and to the Knowledge Base Retrieve API for the search, and the search pulls from S3. Every one of those hops starts and ends inside the AWS account. Run it in EKS with VPC endpoints and it never touches the public internet. Every model call and every retrieval is an AWS API call, so all of it is already tracked in CloudTrail.

AURA reads and reports here. It answers from the documents and does not act on the environment.

For more detail, visit

Chapters

0:00 Why a third-party agent stalls the security review

0:29 The agent config: Bedrock provider and AWS credentials

0:47 The knowledge base and the runbooks in S3

1:22 Every model call and retrieval lands in CloudTrail

1:35 Asking a question an on-call engineer would ask

1:58 The tool call, the query, and the answer from your runbooks

2:15 An error code that exists only in your docs

2:36 Tracing every hop inside the account

3:03 What changes if you use a different model

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