When Your AI Agent Needs a Principal Engineer, Not More Prompt Tuning
A practical guide for founders and CTOs: the signs your AI agent no longer needs more prompt tuning and now needs principal-level engineering judgment.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
A practical guide for founders and CTOs: the signs your AI agent no longer needs more prompt tuning and now needs principal-level engineering judgment.
LangGraph state schema design, checkpointer backend selection, selective checkpointing, and crash recovery patterns for production AI agent deployments.
What a stabilization sprint actually looks like for a stressed AI system: isolate the hot path, bound the rescue scope, remediate the failure mode, and restore a safer operating baseline.
CrewAI memory in production requires decisions about persistence backends, retrieval strategies, and state recovery that the quickstart docs do not cover.
A practical 30-day enterprise agentic portfolio review: initiative inventory, classification rules, funding decisions, governance gates, and a 90-day priority list.
A production readiness checklist for CrewAI and multi-agent systems: orchestration, delegation, tool safety, evals, observability, and human review.
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