Architecture Decisions That Cost Startups 6 Months
The startup AI architecture decisions that quietly cost six months: wrong abstraction layers, premature agents, weak evals, unsafe tool access, and missing ownership.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
The startup AI architecture decisions that quietly cost six months: wrong abstraction layers, premature agents, weak evals, unsafe tool access, and missing ownership.
A practical 30-day enterprise AI governance review: decision artifacts, risk map, ownership model, approval points, vendor scoring, and rollout priorities.
A practical architecture audit for AI agents: state, tools, review paths, evaluations, blast radius, and the design choices that become expensive later.
Five signs your AI system needs a production audit before reliability, governance, cost, or architecture debt gets harder to unwind.
Most AI automation projects fail because teams automate visible workflows, not valuable ones. Here's the framework for identifying and sequencing
Context engineering is replacing prompt engineering as the discipline that determines whether AI agents succeed in production. Here's the architecture
How to build Graph RAG with Neo4j for AI agent memory. Real architecture, Cypher patterns, and the failure modes vector-only pipelines hit at production
Build a production-grade self-correcting RAG pipeline with a LangGraph critic agent. Covers hallucination detection, retrieval grading, and loop escape
How to build a low-latency RAG pipeline that retrieves from live Kafka streams — architecture patterns, ingestion trade-offs, and failure modes from production.
A deep technical guide to Human-in-the-Loop (HITL) engineering patterns using LangGraph interrupts. Learn how to implement production-grade approval workflows, checkpoint-backed state management, and async human feedback loops for AI agents.
Prompt engineering is not enough for production AI agents. This deep-dive covers context engineering -- the architectural discipline of designing, curating, and dynamically managing LLM context windows at runtime with token budgets, memory hierarchies, and retrieval patterns.
A principal engineer's guide to building production-grade AI agent systems with security guardrails, governance controls, and full observability.