Architectural AI Digest

Key highlights from Sam Newman, Jacqui Read, and Simon Rohrer's GOTO Copenhagen 2025 session:

• Explores nature of software design and its...

Incremental changes like service splitting and DB sharding quietly erode velocity in high-growth SaaS.

Key warning signs:

• Request paths need...

• Coordination Ownership: Request-response puts workflow control in caller for linear flows; event-driven distributes it for emergent reactions.
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Event-Driven Failure Handling on Azure

• 🔥 EDA Azure Episode 4: Failure & Backpressure: Examines failure propagation through Event Grid, Service...

Agent Router decouples user intent from agents, replacing brittle hardcoded logic for enterprise-scale systems.

• Problem Forces: Handles ambiguous...

Key lessons on failure and backpressure in Azure event-driven architectures:

• Propagation paths: Failures follow events through Event Grid, Service...

Start simple: Begin with a monolith to avoid overengineering buzzwords like microservices until pains emerge—deployments slow, blast radius grows, or...

AI-Powered SaaS Architectures

• 🔥 Building a Full-Stack AI SaaS: The Architecture of a High-Frequency ...: Describes building a full-stack AI...

Building AI-powered SaaS now requires architectures beyond simple API calls, tailored for high-frequency full-stack applications in evolving software development.

Master Saga for microservices interviews:

• Breaks distributed transactions into local transactions with compensating transactions for rollback.
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Key architecture patterns for multi-tenant SaaS hitting 100K users from real-world builds:

• Shared DB, Shared Schema: Lowest cost, simplest ops, but...

LLM Integration Pitfalls

• 🔥 The LLM as a Microservice: Why Adding AI is Crashing Your Servers: Video explains why treating LLMs like standard...

Staff+ engineers prevent technical debt by focusing architecture reviews on failure modes and coupling, catching shortcuts like shared databases.

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Treating LLMs like fast REST APIs dooms your servers—here's why and how to fix it:

• 4 LLM quirks: Extreme latency (up to 60s), token streaming, high...

Key insights from Hanno Liem on AI via event-driven data processing reshaping telecom networks:

• Core challenges: Integrating AI/ML into existing...

Vendor blueprint for secure SaaS: Embed identity early to counter 44% of breaches from credentials and 80% exposures from misconfigs.

• Separate...

Production ML Pipelines

• Building Production ML Pipelines for Log Intelligence: Adds machine learning models to log processing for predicting...

• Core challenge: AI excels at scratch-built apps but fails to match existing codebase conventions and patterns.
• Duolingo's fix: Built a pipeline of...

Key patterns for productionizing ML across domains:

• Automated pipelines beat manual workflows, handling data drift and ensuring reproducibility via...

Two angles to bulletproof enterprise AI data pipelines:

• Postmortem Diagnosis (Art1): Classify failures into structural, retrieval misalignment,...