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How Indegene is modernizing and scaling enterprise data quality in life sciences

This post explores how Indegene built a production-grade, LLM-powered Data Quality Agent in its NEXT Unified Data Platform on AWS. The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python under human-in-the-loop governance, helping life sciences organizations scale enterprise data quality.

We haven't written up this one. AWS Machine Learning has the full story — the link below goes straight to it.

Read the original at aws.amazon.com →

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Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

In production AI agents, context management is the real bottleneck. This post presents a four-layer context engineering framework (architecture, prompts, file-based tools, and verification agents)…

  • Deep Insight employs a four-layer context engineering system to manage AI agent context.
  • Layer 1 isolates context using multi-agent architecture with specialized agents.
  • Coder agent uses only 25K tokens, minimizing context depletion.

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