📰 Dev.to · Nebula

Your agent works in development. After a prompt change, it silently uses the wrong tool path. Here's how to catch agent regressions before they hit users — usin

With 177,000+ public MCP tools, agents can't load every tool definition into context. Here's how to implement deferred tool loading — the 2026 pattern that cuts

What turns an LLM into an agent isn't the model — it's the harness. Here's how to build the orchestration layer that manages tools, memory, guardrails, and the

Your agent's dashboards are green but it's looping silently, burning tokens, and hallucinating. Traditional APM can't see this. Here's the observability stack t

LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, Smolagents, and FastMCP compared. Which one should you pick for your next AI agent project?

Your AI agent demo worked. Then production broke it silently. Here are five battle-tested error handling patterns — circuit breakers, validation gates, idempote

The decision between structured outputs and tool calling isn't just semantics — it shapes your agent's behavior, reliability, and cost. Here's how to pick.

A quick, code-first guide to building a working MCP server in Python — no boilerplate, just the essentials.

Real war stories from an autonomous Nebula agent running daily content workflows — retry storms, context rot, and the six lines of code that stopped a $47,000 i

Sentry throws 200 errors at my team every day. Most of them are noise. The ones that matter arrive at...

Four years into running a codebase with half a million lines of Python and TypeScript, my PR review...

Side-by-side comparison of LangChain Deep Agents and OpenAI Agents SDK with code examples, feature tables, and a decision framework.

Build a working MCP server with two custom tools using FastMCP and Python. Copy-paste, run, connect to Claude Desktop.

Build resilient AI agents with retry, fallback, circuit breaker, and graceful degradation patterns. Pure Python code you can copy-paste today.

Compare the best AI agent memory frameworks in 2026: Mem0, Zep, Letta, Cognee, LangChain Memory, and LlamaIndex Memory.

Stop parsing messy LLM text. Use PydanticAI to get typed, validated JSON from any model in under 30 lines of Python.

Learn 4 battle-tested event-driven architecture patterns for AI agents with runnable Python code, retry logic, and production tips.

Build an AI agent that converts natural language to SQL queries using PydanticAI and SQLite — no database server required.

A practical comparison of the top 7 AI agent frameworks in 2026 — from LangGraph to CrewAI — with honest trade-offs and a decision matrix.

Build a working Model Context Protocol server with TypeScript that exposes a custom tool to any AI assistant.

Build a simple fallback chain so your AI agent switches to a backup model when the primary LLM fails. Copy-paste Python code included.

AI agents fail silently in production. Here are 5 testing patterns -- from deterministic assertions to budget tripwires -- that catch failures before your users

Connect to any MCP server from your own Python code. Discover tools, call them, and get results -- all in under 40 lines.

Most teams go multi-agent too early. Learn the 4 orchestration patterns that cover 90% of production use cases, with code and cost math.