Alex Rivera Most developers have tried an AI coding assistant. Fewer understand what separates a glorified autocomplete engine from a true deep agent — one that can read a codebase, reason about architecture, execute multi-step changes, and recover from mistakes without hand-holding. That gap matters enormously in 2025. The tools in the first category save you minutes. … Read more
The AI agent development landscape is moving at breakneck speed. Every week brings new frameworks, evaluation tools, and architectural innovations that reshape how engineers build autonomous systems. For developers navigating this ecosystem, staying informed about the latest tools, frameworks, and best practices isn’t just helpful—it’s essential for making sound technology decisions. This roundup covers the … Read more
When engineers first started building LLM-powered applications in 2023, most of them reached for the same tool: LangChain. Three years later, that’s still largely true — but for different reasons. What started as a glue layer for prompt chaining has matured into a full-stack agent engineering platform, anchored by LangGraph for orchestration and LangSmith for … Read more
LangChain got you started. It provided the abstractions, the tool integrations, and the community to build your first agent systems. But as those systems moved to production, you’ve likely hit the friction points: deeply nested abstractions that make debugging painful, version upgrades that break existing chains, and production reliability patterns that need to be built … Read more
CrewAI has become the default choice for teams building multi-agent systems. It’s well-documented, quick to prototype with, and the role-based agent abstraction makes complex workflows easy to reason about. For prototypes and small-scale deployments, it works well. But enterprise teams hit friction points that prototypes never encounter: agents that need to handle 10,000 requests per … Read more
Testing AI agents is fundamentally different from testing traditional software. A unit test passes or fails deterministically. An agent evaluation passes or fails probabilistically, because the same input can produce different outputs across runs, and “correct” often requires judgment rather than exact matching. The evaluation tooling landscape has matured in 2026, but choosing between platforms … Read more
Your agent works in development. It passes your test suite. You deploy it to production. Three days later, a customer reports that the agent recommended a product that was discontinued two years ago. Your logs show 200 OK on every API call. Nothing failed. The agent just quietly produced wrong answers while every traditional monitoring … Read more
Every “best AI agent frameworks” article gives you the same thing: a list of frameworks with feature bullets and GitHub star counts. None of them answer the question that actually matters: which one will still work when your agent handles real traffic, real edge cases, and real money? This guide takes a different approach. We … Read more
Most “getting started” tutorials show you how to build an agent. This one shows you how to build an agent that works reliably. The difference is the harness: the verification, cost controls, and error handling that separate a demo from a production system. By the end of this tutorial, you will have a working agent … Read more
LangChain’s own team published a blog post titled “Agent Frameworks, Runtimes, and Harnesses – oh my!” that explains the distinction. Frameworks provide abstractions for building agents. Runtimes provide infrastructure for running them. Harnesses provide opinionated defaults and built-in capabilities for deploying them reliably. LangChain is the first. An agent harness is the third. They are … Read more