Agile Research Management System

Managing structured research and autonomous AI runs requires rigorous state and safety controls. This project is a robust research-operations backend built to enforce process invariants natively.

Enforcing Process Invariants

The core of the system is a sprint state machine that actively rejects illegal transitions. It features:

• A closure gate requiring linked synthesis memos.
• Cryptographic SHA-256 artifact hashing.
• Dynamic ledger burn alerts triggering at 80% and 100% consumption.

The enterprise compliance and export pipeline implements FDA 21 CFR Part 11 Electronic Signatures (HMAC-SHA256 JWT) for Human-in-the-Loop governance gates. It also includes an automated ERP egress seam and a Quarto-prioritized Manuscript Compiler.

Automated Anomaly Detection

I deployed anomaly and opportunity detection systems using ARQ cron workers and SwarmOS auditors. These systems autonomously monitor budget ledgers and match researcher activities against external grants (PubMed, ClinicalTrials.gov, SEC EDGAR) using vector-similarity scoring (NIM embeddings + pgvector).

High-Throughput Scalability

The platform successfully scaled to support high-throughput operations. I stress-tested 5 parallel SwarmOS AI sprint runs and verified zero cross-tenant data bleed. The architecture leverages provider-agnostic object storage (AWS S3/MinIO via aioboto3) and cross-sprint RAG bundles through a tenant-scoped, read-only graph query endpoint—all backed by a stabilized, 100% passing test harness and a 732-line formal specification.