The Idempotency Crisis: LLM Agents as Event Stream Consumers
At-least-once delivery assumes reprocessing an event produces the same result. LLMs don't. A practical guide to idempotency keys, deduplication windows, and compensating read-models for AI-powered Kafka consumers.
The Mental Model Shift That Separates Good AI Engineers from the Rest
The transition from deterministic to stochastic systems trips up strong engineers. Here are the mental models, debugging intuitions, and practices that actually separate experienced AI engineers from everyone else.
Model Deprecation Is a Production Incident Waiting to Happen
LLM providers deprecate models on 6–12 month windows, but most teams treat migration as a backlog item—until it becomes a 3 AM outage. Here's the operational playbook to make model upgrades boring.
Multi-Tenant AI Systems: Isolation, Customization, and Cost Attribution at Scale
How to serve multiple customers from shared AI infrastructure without leaking data, creating noisy neighbors, or losing track of who's spending what.
Multi-Modal Agents in Production: What Text-Only Evals Never Catch
Adding vision and document inputs to agent pipelines introduces failure modes that text-only evals never surface. Here's what practitioners are running into and how to build evals that catch it.
Multimodal AI in Production: The Gap Between Benchmarks and Reality
Vision and audio models look impressive in demos. In production, they face latency penalties, grounding failures, and extraction inconsistencies that most benchmark scores hide entirely.
The 90% Reliability Wall: Why AI Features Plateau and What to Do About It
Why AI features stall around 90% reliability, how to diagnose reducible vs. irreducible error, and the product-architecture decisions that let you ship honest value.
On-Call for Stochastic Systems: Why Your AI Runbook Needs a Rewrite
Traditional incident response assumes reproducible failures. LLM-powered systems don't. Here's how to rewrite your alerting schema, triage decision tree, and post-mortem template for non-deterministic AI.
The Orchestration Framework Trap: When LangChain Makes You Slower to Ship
AI orchestration frameworks like LangChain accelerate prototyping but create debugging opacity, versioning brittleness, and leaky abstractions at scale. Here's the decision framework for knowing when to use them and when to drop down a layer.
The Over-Tooled Agent Problem: Why More Tools Make Your LLM Dumber
Tool selection accuracy drops to 13% when LLMs face large tool sets. Here's why over-tooling breaks your agents and how to architect around it with routing layers, hierarchical toolsets, and lazy-loading registries.
The Privacy Architecture of Embeddings: What Your Vector Store Knows About Your Users
Embedding a user's documents creates a novel privacy surface area that traditional databases don't have. Here's how re-identification risks work, where access control breaks down in RAG pipelines, and the architectural patterns that actually fix it.
The Prompt Governance Problem: Managing Business Logic That Lives Outside Your Codebase
When you have 50+ active prompts across product, ML, and infra teams, you have a distributed systems problem — not a writing problem. Here's the infrastructure that keeps it from becoming a liability.