Surviving LangChain Version Upgrades: Migration Patterns for Production Systems
LangChain's 0.1→0.3 migration path broke production systems in ways teams did not anticipate. These patterns reduce the damage next time.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
LangChain's 0.1→0.3 migration path broke production systems in ways teams did not anticipate. These patterns reduce the damage next time.
How to build an evaluation layer for production AI systems: golden sets, failure taxonomies, regression gates, tool choices, thresholds, and release criteria.
Diagnose CrewAI failures by layer: delegation loops, role confusion, tool errors. Structured logging, trace correlation IDs, and callback handler patterns.
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.