Coding

Vanilla RAG retrieves once and hopes. Agentic RAG plans sub-queries, retrieves iteratively, and verifies evidence before answering. Here is the 2026 architecture, a vanilla-vs-agentic comparison, and the honest latency and token-cost tradeoffs.

OpenAI Agents SDK (provider-agnostic via LiteLLM, ~10.3M downloads) vs PydanticAI (type-safe durable). For Python teams the decision grounds in runtime flexibility versus hygienic type-safety.

Evaluation in production is a capital-F Feedback loop: capture traces, promote hard ones into datasets, run regression suites, and gate each deploy. Every robust 2026 AI team works this way.

CrewAI's role-played agents sit at ~52.8K GitHub stars, ~5.2M downloads, and ~60% Fortune 500 pilots, while LangGraph runs ~34.5M monthly downloads with Uber, Klarna, and LinkedIn. Here's how to run both.

A grounded 2026 cost-benefit analysis of Langfuse, AgentOps, and LangSmith for tracing, debugging, and growing agentic AI in production — including token economics, pricing, and where each genuinely wins.

Explore the architecture behind Cursor's 2026 Agent Mode and Google Workspace integration, enabling safe, autonomous multi-file refactoring at scale.