AWS details Deep Insight, an open-source multi-agent data analysis system
The AWS Solutions Architecture team published the first post in a three-part series describing Deep Insight, a production multi-agent system that takes a CSV file and a natural-language prompt and produces a professional .docx report with citations. The system runs a hierarchy of specialized agents — Coordinator, Planner, Supervisor, Coder, Validator, Reporter and Tracker — built on the Strands Agents SDK, Amazon Bedrock, Amazon Bedrock AgentCore, an Application Load Balancer and AWS Fargate, with Claude models assigned by role (Haiku, Opus and Sonnet). The authors say Deep Insight is open source and that a human-in-the-loop Plan Reviewer, which auto-approves after a 300-second countdown in the web version, addresses the problem that agents make context-based judgments rather than following fixed instructions. All performance and adoption claims come from the vendor authors and were not independently measured.
The post argues that getting multi-agent systems into production is a matter of architecture — explicit planning, human-in-the-loop approval and sandboxed code execution — rather than model capability or prompt engineering alone.
Building a multi-agent system is now pretty straightforward. Taking it to production is where the real challenges begin. Thanks to open source frameworks and cloud services, you can set up an agent in a matter of days. A few tool calls, a few lines of prompt, and you have something that looks impressive. But the moment teams try to move beyond a pilot and into real business applications, they hit a familiar wall. If you’ve ever tried to put an agent into production, these frustrations will sound familiar:
Why doesn’t the agent follow our business logic, skip validation steps, or catch edge cases?
You tweak the prompt, add more examples, swap out the model. The same problems keep coming back. That’s because agents don’t follow a fixed sequence of instructions. They make their own judgments based on context. Instructions alone aren’t enough. You need to design how the agent thinks, when it verifies, and what it does when things go wrong. This is exactly why it’s become critical to go beyond prompt engineering alone and design the agent’s entire cognitive architecture and execution flow.
#AWS#Deep Insight#Strands Agents SDK#Amazon Bedrock#Amazon Bedrock AgentCore#Claude#AWS Fargate#LG Electronics#TechRecon