BGL democratizes business intelligence with Claude Agent SDK and Amazon Bedrock AgentCore
BGL's business users without technical knowledge had to rely on the data team for queries, creating a bottleneck, while traditional text-to-SQL solutions failed to provide consistent or accurate results across their complex financial compliance data.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits question via Slack
A user asks a business question using Slack.
Tools used
Claude Agent SDKAmazon Bedrock AgentCoreAmazon Athenadbt LabsAmazon S3PythonAmazon BedrockClaude Code
Outcome
For BGL's more than 200 employees, the AI agent represents a significant shift in how they extract business intelligence — product managers can validate hypotheses instantly, compliance teams can spot risk trends without SQL, and the data team is freed to focus on strategic initiatives.
What failed first
Text-to-SQL agents that try to handle everything — schema understanding, joins, aggregations, and business logic — produce inconsistent results by joining tables incorrectly, missing edge cases, or generating wrong aggregations.