The Data Product Pattern Language: 5 AI Blueprints
A strategic guide to data products. Explore 5 powerful blueprints (Curator, Matchmaker, Oracle, Guide, Gatekeeper) and the key algorithms used to build them.
Production patterns for AI agents, RAG pipelines, data infrastructure, and MLOps. No theory-only posts — every article comes from a real deployment.
A strategic guide to data products. Explore 5 powerful blueprints (Curator, Matchmaker, Oracle, Guide, Gatekeeper) and the key algorithms used to build them.
A deep-dive playbook for product teams. Learn our 4-step process: diagnose with cohort analysis, investigate with funnels, understand with ML, and validate with A/B tests.
A framework for structuring your data team into two functions: an 'Insight Engine' and a 'Value Engine' to maximize business impact and ROI from your data.
A practical agent engineering guide covering AI agent architecture, frameworks, orchestration patterns, production reliability, and the systems discipline required for real deployments.
Learn how to build an AI agent CI/CD and deployment pipeline with GitHub Actions, Docker, Kubernetes, and production release discipline for agent systems.
Learn how Temporal enables durable AI agents with fault-tolerant execution, workflow state persistence, retries, and long-running Python orchestration.
Hierarchical AI agents in CrewAI are useful only when manager-worker delegation solves a real coordination problem. Use this framework before adding `allow_delegation`.
A practical CrewAI tutorial covering your first agent, `from crewai import Agent, Task, Crew, Process`, and when to use sequential or parallel crews.
A practical CrewAI tutorial for building an autonomous agent crew for competitor analysis, covering specialist agents, orchestration, structured outputs, and report generation.
A production-grade architecture for a GitHub code analysis agent with LangChain, language-aware parsing, code indexing, retrieval, and repository Q&A.
A refreshed CTO framework for deciding between prompt optimization, RAG, and fine-tuning based on knowledge freshness, behavior control, cost, and operating complexity.
Use FastAPI to deploy LangChain and LangGraph agents in production with async request handling, Pydantic validation, dependency injection, and cleaner LLM API architecture.