Building production AI agents, without the guesswork.
Everything we learned shipping agents at Einstein Labs — the production layer, the framework trade-offs, durable human-in-the-loop, the tools that matter. No hype, just what actually works in front of paying customers.
The Production Layer Every AI Agent Needs (and Why LangGraph Doesn't Give It to You)
Orchestration is solved. The production layer — memory, observability, guardrails, evals, human-in-the-loop — isn't. Here's what it takes to make an AI agent production-grade, and why every team rebuilds it from scratch.
Read →LangGraph vs CrewAI vs a Vanilla Loop: Which Should You Actually Use in 2026
A practical, no-hype comparison of the three ways to build an AI agent in 2026 — LangGraph, CrewAI, and a plain function-calling loop — with a decision table and where each one breaks in production.
Read →How to Add Human-in-the-Loop Approval to Any AI Agent (With Durable Execution)
Risky agent actions need a human to approve them — and the pause has to survive a crash. Here's how durable, checkpointed human-in-the-loop works, and why in-memory approval flows lose state the moment a process dies.
Read →The 50 MCP Tools Every Production AI Agent Actually Needs
A working AI agent is 90% tools. Here are the 50 MCP-native tools — grouped by support, research, and coding agents — that cover the vast majority of real production use cases, and the wrapper pattern that makes adding your own a one-liner.
Read →What SaaS Boilerplates Taught Us About Building the Agent-Era Equivalent
ShipFast, Bullet Train and T3 collapsed weeks of SaaS plumbing into an afternoon and minted a generation of shipped products. AI agents are at that exact pre-boilerplate moment. Here's the playbook, applied.
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