Building an Agent from Scratch: A Production-Oriented Walkthrough
A working engineer’s guide to building an LLM agent from zero: the planning loop, tool integration, memory, guardrails, and the production patterns that turn a demo into a service.
Tool-Use
Building Agents with MCP: The Model Context Protocol in Practice
How to design, build, and ship Model Context Protocol servers that turn LLMs into reliable, tool-using agents — with patterns drawn from Claude Desktop, Cursor, and real production deployments.
The Complete AI Agent Architecture: From Perception to Action
A field guide to designing production AI agents — from perception and reasoning loops to memory, tool routing, and the orchestration patterns that keep them reliable at scale.
Building a pi-agent for Long-Horizon Tasks: Patterns That Actually Work
Pi-agents for long-horizon tasks need more than a clever prompt. This post covers the architecture, memory patterns, and failure modes that decide whether the agent finishes the job or burns budget on a loop.
Exploring Agentic RAG Architectures with Vector Databases and Tool Use for Production AI
Introduction Retrieval‑Augmented Generation (RAG) has quickly become the de‑facto pattern for building knowledge‑aware language‑model applications. By coupling a large language model (LLM) with an external knowledge store, developers can overcome the hallucination problem, keep responses up‑to‑date, and dramatically reduce token costs. The next evolutionary step—agentic RAG—adds a layer of autonomy. Instead of a single static retrieval‑then‑generate loop, an agent decides when to retrieve, what to retrieve, which tools to invoke (e.g., calculators, web browsers, code executors), and how to stitch results together into a coherent answer. This architecture mirrors how a human expert works: look up a fact, run a simulation, call a colleague, and finally synthesize a report. ...