n8n AI Agent Infinite Loop Fix: Why Overlapping Tools Self-Call
A field-tested walkthrough of why n8n AI Agent nodes recursively call themselves when tool descriptions overlap, plus three prevention patterns I now apply to every production workflow.
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Why your n8n AI Agent isn't calling its tools (6 real causes)
I lost most of a Tuesday to an n8n AI Agent node that flat-out refused to call the HTTP Request tool I'd wired into it. After pulling apart a dozen variations I have six reproducible causes.
RAG Chunking Strategies in 2026: Late Chunking, Contextual Retrieval, and Semantic Splitting
How to chunk documents for RAG in 2026: contextual retrieval, late chunking, semantic splitting, plus production Python code, benchmarks, and evaluation tips.
Mem0 vs Letta vs Zep: Agent Memory for Production AI Agents (2026)
Compare Mem0, Letta, and Zep on architecture, latency, recall accuracy, and cost. Includes LangGraph integration patterns and production pitfalls to avoid.
How to Build Hybrid Search RAG Pipelines with BM25, RRF Fusion, and Cross-Encoder Reranking
Pure vector search fails on roughly 40% of real-world queries. This 2026 production guide shows how to combine BM25 sparse retrieval, dense embeddings, RRF fusion, and a cross-encoder reranker into a single pipeline that pushes recall@10 from 78% to 91% — with working Python code.
How to Build Production Voice AI Agents with Pipecat and Python: Latency, Barge-In, and Deployment
A production guide to building sub-second voice AI agents with Pipecat and Python. Covers pipeline architecture, latency tuning, interruption handling, semantic turn detection, and deployment on Pipecat Cloud or AWS Bedrock AgentCore.
Prompt Caching in Production: How to Cut Claude, OpenAI, and Gemini Costs by 90%
A production guide to prompt caching with Claude, OpenAI, and Gemini. Learn cache breakpoints, TTL strategy, prompt structure, and the seven mistakes that silently kill your cache hit rate.
Context Engineering for Production AI Agents: A Python Implementation Guide
Context engineering — curating what an LLM sees at inference time — is now the defining skill for AI engineers. This guide walks through the four core strategies (write, select, compress, isolate) with production Python implementations using LangGraph, reranking pipelines, and multi-agent isolation.
How to Build Agentic Frontends with the AG-UI Protocol and Python
Build real-time agentic frontends using the AG-UI protocol with Python and FastAPI. Covers streaming events, shared state synchronization, frontend tool calls, and human-in-the-loop patterns — completing the agentic protocol trinity alongside MCP and A2A.
Building LLM Data Extraction Pipelines with Instructor and Pydantic
Learn how to build production LLM data extraction pipelines using Instructor and Pydantic in Python. Covers schema design, validation retries, streaming, batch processing, and multi-provider support with working code examples.
How to Build Production AI Agents with the Claude Agent SDK: Custom Tools, Hooks, and Subagents
Learn to build production AI agents with the Claude Agent SDK in Python. Step-by-step guide covering custom tools, safety hooks, subagents, MCP integration, structured outputs, and deployment patterns with working code.
How to Fine-Tune LLMs with LoRA and QLoRA: A Production Python Guide
Learn to fine-tune LLMs with LoRA and QLoRA on a single GPU using Python and Hugging Face. Covers dataset prep, training with TRL and PEFT, evaluation, GGUF export, and Ollama deployment.