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Architecture documents from real cases — ADRs, design docs, post-mortem analyses and teardowns — with my reading as a solutions architect.
An in-depth architectural analysis of the resilient graph-based data center networks AWS is building to support AI workloads at scale — covering topology, congestion control, energy efficiency, and the trade-offs that define the next generation of cloud infrastructure.
This ADR evaluates the decision to adopt AWS Transform (with AI agents for .NET, Mainframe, VMware, and custom code) versus a traditional human-engineering modernization factory, or a hybrid approach. The analysis covers regression risk, test coverage, code ownership, security, total cost, and change governance in an enterprise-scale modernization program.
LLM agents in production silently degrade as models, tools, and prompts evolve — without a continuous evaluation discipline, regressions reach users before they are detected. This document proposes a complete offline and online evaluation architecture using Amazon Bedrock AgentCore, with versioned datasets, CI/CD quality gates, runtime signals, and systematic adversarial testing.
This document proposes an end-to-end observability architecture for LLM inference platforms running on Amazon SageMaker AI and Amazon Bedrock, covering everything from hardware metrics (GPU utilization, memory) to semantic response quality, behavioral drift, and per-tenant cost. The design integrates CloudWatch, Amazon Managed Grafana, prompt-level tracing, and automated regression alarms, with clear separation of concerns across collection, storage, evaluation, and alerting layers.
This ADR examines when and how to adopt multi-region User Pool replication in Amazon Cognito to reduce authentication downtime on identity platforms with high-availability requirements. It covers regional failover, customer-managed KMS keys, user synchronization, session and token impact, custom domains, and customer experience, with explicit reasoning on operational and cost trade-offs.
This ADR evaluates vector search infrastructure options for a multi-tenant agentic RAG platform on AWS, comparing OpenSearch Serverless, dedicated vector databases (Pinecone, pgvector), and a self-managed hybrid search layer. The decision weighs cost, p99 latency, permission-based filtering, incremental ingestion, and native Bedrock Knowledge Bases integration.
This document proposes an SRE platform built on AWS Resilience Hub with a GenAI layer to automate dependency discovery, failure mode analysis, and runbook generation for critical applications. The goal is to reduce operational risk through modular resiliency policies and organization-level consolidated reports, replacing manual processes prone to coverage gaps. The design prioritizes traceability, incremental automation, and integration with existing CI/CD pipelines.
This document proposes an agentic automation architecture for backoffice, support, and IT operations, connecting Amazon Q Business, the Model Context Protocol (MCP), internal tools, and Amazon Bedrock into a unified layer with mandatory human approval, immutable audit trail, and explicit action boundaries. The goal is to reduce repetitive manual work without sacrificing control, traceability, and security in regulated environments.
This document proposes an AI Gateway architecture to orchestrate and govern multiple frontier models — OpenAI GPT-5.5/GPT-4.5, Anthropic Claude, Amazon Nova, and specialized models — within Amazon Bedrock. The design covers intelligent routing, guardrails, prompt registry, inference logging, per-tenant IAM, data residency, and fallback policy, with a focus on auditability and cost control in enterprise environments.