Optimizing LLM Stream Ingestion: Reconstructing Truncated JSON Payloads in 0.0122ms
Every engineering team deploying production-grade LLM agents or RAG pipelines faces the exact same architectural bottleneck: truncated network data st…
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Handoffs can turn one task into a 15x token bill
Handoffs are useful when a specialist agent needs to take over a task. They also make cost easier to hide, because the bill is spread across graph nod…
AI Agents in Finance Live or Die by Runtime Receipts | Focused Labs
The board wants to see the bottom line of the RFP. Show the receipt. Financial services boards want to know what they get from the spend on AI in Lang…
Building a Robust RAG Pipeline Architecture for Production
Answer up front: A RAG pipeline architecture is a set of connected services that ingest raw documents, turn them into embeddings, store them in a vect…
We deployed a LangChain agent for a client and it silently failed for two weeks. Here's what we built to make sure it never happens again.
Six weeks ago, a LangChain agent we'd deployed for a B2B client started failing on roughly 30% of its sessions. No exceptions. No 500s. Nothing in the…
Why Prompt Engineering Isn't Enough for Production AI Agents
TL;DR: Autonomous Agents frequently get trapped in execution loops, burning through API tokens and compute. Prompt engineering can't guarantee executi…
Running a Whole RAG Agent Offline: LangGraph + Ollama + Embedded Qdrant (Zero API Keys)
Most RAG tutorials open with "set your OPENAI_API_KEY ." This one doesn't need it. In Part 1 I claimed the LLM and embeddings are behind a swappable b…
Running a LangGraph ReAct Agent in Production: OpenAI-Compatible API + Multi-Model Gateway + One-Line Tracing
Most LangGraph content stops at the notebook. You build a cute ReAct loop, it answers one question, and the article ends before the hard part: how do …
Building a Cross-Border Price-Comparison Agent: A Live Build Log
Why a build log (and not another tutorial) Every AI shopping tutorial shows the same thing: install the SDK, call a tool, ship. None of them show what…
LangGraph Multi-Agent Tutorial: Build AI Agent Workflows with Real Examples
🚀 LangGraph Multi-Agent Tutorial: Build AI Agent Workflows with Real Examples 🧠 Introduction Most AI agent systems fail not because the model is weak …
Orchestrating AI: LangChain Framework Abstraction vs. Pure Native Code
When building prototypes with Generative AI, velocity is everything. Developers want to stitch together prompts, text splitters, vector stores, and mo…
LangChain + Chroma: Multi-turn RAG Memory and Automated Testing That Turned 2-Hour Bugs Into 5-Minute Fixes
At 1 a.m., the customer group chat exploded: “Does your customer service bot have only a 7-second memory? I just gave it the order number, and the nex…
Build Your Own "Longevity Scientist": A Paper-to-Action Agent using LangGraph & Mistral-7B
We live in an era where scientific breakthroughs are published faster than we can read them. For the biohacking community, the gap between a new PubMe…
How to Build an AI Resume Builder with LangChain and Node.js
A few months back, my friend Marcus was applying for a senior backend role at a fintech company. He had five years of solid experience — distributed s…
Agent Series (10): MCP Protocol — Standardizing the Tool Ecosystem
More Tools, More Chaos After you build an Agent, the first thing you usually do is give it tools: search, code execution, database queries, API calls……
Новинка: Прикладные API для искусственного интеллекта и Data Science
В течение последних двух лет мы проделали большую работу по изданию неустаревающих книг, связанных с проектированием и развитием API. Как известно, са…
Agent Series (3): Plan-and-Solve — Think First, Then Act
Where Does ReAct Hit a Wall? The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the next…
Анатомия ИИ-трейдера: Как создать своего автономного ИИ-агента и зарабатывать на бирже
ИИ может торговать на бирже в плюс — и мы это проверили в ходе эксперимента на 6 моделях. Разбираем автономного ИИ-трейдера по косточкам — архитектура…
5 Reasons Your RAG System Will Fail in Production (And the Patterns I Use to Fix Each One)
The 80% Problem Most RAG demos look magical. You drop in 10 PDFs, ask 3 questions, get clean answers. Ship it. Then production hits. The document corp…
RAG Series (17): Agentic RAG — Giving the Agent Control Over Retrieval
The Silent Failure of Pipeline RAG Every article in this series has been trying to answer the same question: how do we make retrieval better? Better c…
AI Agent Frameworks Compared: LangChain vs Custom vs Agentic Systems
AI agents are rapidly moving from experimental prototypes to production infrastructure. Developers are no longer just building chatbots. They’re build…
Deep Dive: LangChain 0.3 LCEL and How It Optimizes Claude 3.7 Calls
If you’ve ever watched a Claude 3.7 API call sit in a pending state for 2.1 seconds while your LangChain pipeline serializes prompts, parses outputs, …
Madrigal's "Failures as Eval Suites" Pattern and How Flow Already Provides the Infrastructure
A blog post on LangChain's site about how Madrigal Pharmaceuticals built their multi-agent AI platform caught my attention this week. Not because of t…
How to Monetize Your AI Agents with LangChain and Kong
Say you built an AI agent and customers are starting to pay for it. Sooner or later you'll want to charge them by what they actually use, because some…
Build a RAG agent with LangChain and Ollama
I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions…