In the fast-evolving AI era, we thoroughly explain stable technical foundations.
The article breaks down AI agent continual learning into three layers—model, harness, and context—explains their distinct challenges, shows how traces link them, and argues that focusing on harness and context yields faster, more practical improvements than merely retraining models.
The article examines the noise‑accumulation problem of AI‑Agent memory, explains Claude Code’s Auto Memory and its four‑step Auto Dream consolidation process, details OpenClaw’s three‑stage Dreaming mechanism, compares the two systems across several dimensions, and relates the design to human memory science and practical agent engineering.
The article systematically breaks down the six essential building blocks of a programming agent—live repository context, prompt shape and cache reuse, structured tool access and validation, context reduction, structured session memory, and bounded sub‑agent delegation—illustrated with a Mini Coding Agent implementation and comparisons to Claude Code, Codex, and OpenClaw.
The article explains how KV cache eliminates redundant computations in autoregressive LLM generation, detailing the attention mechanism, the O(n²) waste of recomputing K and V, the cache‑based solution, its impact on time‑to‑first‑token, and the memory‑vs‑speed trade‑off.
This guide explains how to become a world‑class Agent engineer by managing context bloat, defining clear rules and skills, separating research from implementation, using neutral prompts, and writing explicit termination contracts, while emphasizing that the final results remain the developer’s responsibility.
The article details a GAN‑inspired three‑agent architecture—planner, generator, and evaluator—that overcomes Claude's self‑evaluation bias and context‑window limits, enabling hours‑long autonomous coding of complete front‑end and full‑stack applications with measurable cost and quality improvements.
This comprehensive guide walks you through the fundamentals of AI agents, explains the core agent loop, compares workflow patterns with autonomous agents, and provides a practical five‑step process—including tool design, memory handling, testing, and multi‑agent collaboration—complete with real code examples for Anthropic and OpenAI SDKs.
This guide explains what Generative Engine Optimization (GEO) is, why AI‑driven search traffic converts far better than traditional SEO, and provides concrete writing, platform‑specific, and technical steps—including robots.txt, llms.txt, and Schema markup—to make your content reliably cited by Chinese AI search engines and global models.
This article breaks down agentic memory into four distinct types—In‑context, External, Episodic, and Semantic/Parametric—explains three forgetting strategies (time decay, importance scoring, periodic consolidation), shows how memory flows through an agent loop, and provides complete Python code using OpenAI embeddings and ChromaDB for a production‑ready memory layer.
This article analyzes Claude Code's Sub‑agents and Agent Teams, comparing their architectures, use‑cases, and constraints, and provides practical guidelines, code examples, and design patterns to help developers decide which multi‑agent model best fits their tasks.
