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AI Tech Publishing

In the fast-evolving AI era, we thoroughly explain stable technical foundations.

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Latest from AI Tech Publishing

81 recent articles
AI Tech Publishing
AI Tech Publishing
May 1, 2026 · Artificial Intelligence

5 Counterintuitive Design Principles for Prompt Caching in Claude Code

The article details five counterintuitive design principles for Claude Code's prompt caching—optimizing prompt layout, using message‑based updates, never switching models or tools mid‑conversation, safely compressing context, and monitoring cache health—backed by concrete examples and up to 90% cost savings.

AI EngineeringCache OptimizationClaude Code
0 likes · 10 min read
5 Counterintuitive Design Principles for Prompt Caching in Claude Code
AI Tech Publishing
AI Tech Publishing
May 1, 2026 · Artificial Intelligence

Turning Harness into a Distributed Context Management System for Long‑Task Agents

The article explains why the reliability of long‑task agents now hinges on harness design rather than model strength, and details four harness innovations—programmatic tool calls, sub‑agents as isolation boundaries, context compression, and skill‑search priority—that Glean uses to build a distributed context management system.

Agent HarnessContext CompressionSub‑agents
0 likes · 11 min read
Turning Harness into a Distributed Context Management System for Long‑Task Agents
AI Tech Publishing
AI Tech Publishing
Apr 29, 2026 · Artificial Intelligence

Who Tests When AI Generates 99% of Code? Inside a Self‑Repairing Agent Harness

The article explains how a self‑repairing Agent Harness replaces traditional QA by looping evaluation, triage, automated fixing, verification and AI‑gated canary release, using a three‑judge reviewer, model‑based sampling and six daily engineering tasks to keep AI‑driven products reliable.

AI agentsAI-driven QAContinuous Deployment
0 likes · 16 min read
Who Tests When AI Generates 99% of Code? Inside a Self‑Repairing Agent Harness
AI Tech Publishing
AI Tech Publishing
Apr 29, 2026 · Artificial Intelligence

Why Do AI Agents Forget and Hallucinate? A Complete Guide to KV‑Cache Memory Mechanisms

The article explains that AI agents’ forgetting and hallucinations stem from token‑level attention scores causing key‑value cache eviction before retrieval, then surveys KV‑cache basics, naive growth, streaming‑LLM windowing, SnapKV’s attention‑guided compression, token‑retention studies, Memory Sparse Attention, compares these methods, and discusses practical system pitfalls and design implications.

AI agentsKV CacheMemory Sparse Attention
0 likes · 20 min read
Why Do AI Agents Forget and Hallucinate? A Complete Guide to KV‑Cache Memory Mechanisms
AI Tech Publishing
AI Tech Publishing
Apr 27, 2026 · Artificial Intelligence

Why Build Your Own AI Evaluation Harness? 7 OpenAI‑Inspired Recommendations

The article explains why generic AI testing platforms fall short, outlines how to design a testable AI system from day one, and presents seven practical recommendations—from using Codex or Claude Code to manage regression and iteration test sets, to leveraging entropy diagnostics and custom domain‑expert UX.

AI evaluationEvaluation FrameworkOpenAI
0 likes · 8 min read
Why Build Your Own AI Evaluation Harness? 7 OpenAI‑Inspired Recommendations
AI Tech Publishing
AI Tech Publishing
Apr 27, 2026 · Artificial Intelligence

Context Window Strategies in Agent Harnesses: Pi, OpenClaw, Claude Code, Letta, Alyx

The article analyzes how five Agent Harness frameworks—Pi, OpenClaw, Claude Code, Letta, and Alyx—handle context windows, file pagination, tool result limits, session pruning, and sub‑agent isolation, revealing convergent design patterns that treat the context as a managed memory system.

Agent HarnessContext ManagementFile Pagination
0 likes · 21 min read
Context Window Strategies in Agent Harnesses: Pi, OpenClaw, Claude Code, Letta, Alyx
AI Tech Publishing
AI Tech Publishing
Apr 25, 2026 · Artificial Intelligence

A Comprehensive Guide to Harness Engineering for Reliable AI Agents

This article systematically breaks down Harness Engineering—a framework that organizes large models, context, tools, state, sandboxing, security, and evaluation into a reliable AI agent engineering system, showing how to move agents from demo to production.

AI agentsContext ManagementHarness Engineering
0 likes · 21 min read
A Comprehensive Guide to Harness Engineering for Reliable AI Agents
AI Tech Publishing
AI Tech Publishing
Apr 22, 2026 · Artificial Intelligence

Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions

The article analyzes how extending the context window of large language models leads to rapid forgetting, outlines seven concrete failure modes, examines cognitive‑science‑based memory architectures, and walks through practical layers—from Python lists to markdown files to vector retrieval—highlighting why simple context expansion alone cannot solve the problem.

Agent DesignLLM MemoryVector Retrieval
0 likes · 10 min read
Why Longer Context Makes LLMs Forget Faster: 7 Failure Modes and Memory System Solutions
AI Tech Publishing
AI Tech Publishing
Apr 21, 2026 · Artificial Intelligence

Why Your AI Agent Stays a Toy: Six Production‑Readiness Gaps and How to Bridge Them

Moving an AI agent from a controlled demo to an unattended production environment introduces six critical gaps—fault handling, state persistence, observability, credential security, cost control, and human supervision—each requiring specific infrastructure, practices, and a comprehensive readiness checklist to avoid costly failures.

AI agentsCost ManagementObservability
0 likes · 15 min read
Why Your AI Agent Stays a Toy: Six Production‑Readiness Gaps and How to Bridge Them