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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Every credible analysis of MCP in regulated environments converges on the same architecture — a centralized gateway proxy. But several widely-cited claims about this pattern are wrong. Here's what the evidence actually supports, what the regulators actually require, and what the vendor landscape actually offers.
Read Article →The Model Context Protocol turns LLMs into active agents that can query LIMS, QMS, and clinical databases. Raw MCP is a compliance liability in GxP environments. The protocol isn't the problem — the implementation is. Here's the architecture that makes MCP regulator-ready, backed by OWASP, GAMP 5, and the FDA's own CSA framework.
Read Article →Self-learning agents don't retrain their neural weights in production. They use externalized memory loops, self-reflection, and skill crystallization. Here's the verified architecture — and how to deploy it in a GxP environment without triggering an FDA warning letter.
Read Article →Whether an AI agent drafting SOPs in biopharma needs its own QMS account depends on your architecture. The service account vs. no-account debate maps directly to how deeply the AI integrates with your validated systems — and EU Annex 22, FDA CSA, and the incoming GxP-AI framework all have something to say about it.
Read Article →When files land in S3 or MinIO, they should automatically flow through parsing, chunking, embedding, and indexing. Six architectural approaches for automating this pipeline locally — from simple webhook scripts to orchestrated workflow engines — compared side by side.
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