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ThinkingAgent

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Latest from ThinkingAgent

60 recent articles

ThinkingAgent

Jun 24, 2026 · Artificial Intelligence

Knowledge Engineering for RAG: Ontology, GraphRAG, Agentic RAG, and Context Engineering

By 2026, teams find standard RAG insufficient and turn to knowledge engineering—using Ontology to structure domain concepts, GraphRAG to add graph‑based retrieval, Agentic RAG for proactive multi‑round searching, and Context Engineering to finely manage prompts—resulting in higher relevance, lower token cost, and richer AI answers.

Agentic RAGGraphRAGLLM

0 likes · 18 min read

Knowledge Engineering for RAG: Ontology, GraphRAG, Agentic RAG, and Context Engineering

ThinkingAgent

Jun 22, 2026 · Artificial Intelligence

How to Achieve Full‑Stack AI Observability: Tracking Prompts, Tool Calls, Traces, and Tokens

The article explains why modern LLM‑based AI systems are opaque, defines AI observability as a four‑dimensional practice (Prompt, Tool Call, Trace, Token), and provides concrete architectures, code samples, best‑practice checklists, and real‑world case studies to turn black‑box AI into a transparent, monitorable service.

AI observabilityLangfusePrompt tracking

0 likes · 30 min read

How to Achieve Full‑Stack AI Observability: Tracking Prompts, Tool Calls, Traces, and Tokens

ThinkingAgent

Jun 21, 2026 · Artificial Intelligence

The 6‑Layer Architecture of AI Agents: Perception, Planning, Tools, Memory, Execution, and Feedback

This article breaks down the complete cognition‑action system of modern AI agents into six inter‑connected layers—Perception, Planning, Tools, Memory, Execution, and Feedback—explaining their core problems, engineering designs, common pitfalls, and best‑practice metrics with concrete code examples and real‑world use cases.

AI agentsAgent ArchitectureMemory Management

0 likes · 40 min read

The 6‑Layer Architecture of AI Agents: Perception, Planning, Tools, Memory, Execution, and Feedback

ThinkingAgent

Jun 19, 2026 · Industry Insights

The AI Trifecta: How Tokens, Power, and Data Define AI’s Limits

The article breaks down the three core factors—tokens, electricity, and data—that together determine AI model cost, speed, capability, and industry competition, illustrating the trade‑offs with concrete numbers, examples, and future outlooks.

AIAI industryEnergy Consumption

0 likes · 10 min read

The AI Trifecta: How Tokens, Power, and Data Define AI’s Limits

ThinkingAgent

Jun 18, 2026 · Artificial Intelligence

Evolving AI Skills Yield 116% Accuracy Boost – From Handwritten Prompts to Autonomous Creation

The 2026 Memento‑Skills system lets AI agents create and refine their own Skills, boosting General AI Assistants accuracy by 26.2% and Humanity's Last Exam accuracy by 116.2%, while outlining three generations of Skill evolution, self‑evolution frameworks, benchmark results, practical applications, and best‑practice guidelines for technical leaders.

AI agentsSelf-Evolving SystemsSkill Engineering

0 likes · 17 min read

Evolving AI Skills Yield 116% Accuracy Boost – From Handwritten Prompts to Autonomous Creation

ThinkingAgent

Jun 17, 2026 · Artificial Intelligence

Why a 7B Model Can Outperform a 70B Model: The Power of Knowledge Distillation

The article explains how knowledge distillation lets a small LLM learn from a much larger teacher model, achieving near‑teacher performance while cutting inference cost, latency and memory, and provides a step‑by‑step guide, benchmark results, advanced on‑policy techniques, common pitfalls and best‑practice recommendations.

LLMLLaMA-3On-Policy Distillation

0 likes · 13 min read

Why a 7B Model Can Outperform a 70B Model: The Power of Knowledge Distillation

ThinkingAgent

Jun 16, 2026 · Artificial Intelligence

A Systematic Approach to AI Evaluation: From Benchmarks to Real‑World Scenarios

This article outlines a comprehensive methodology for evaluating large language models, covering classic benchmarks, human and multimodal assessments, common pitfalls such as data contamination and benchmark overfitting, and practical guidelines for building a scientific, multi‑layered AI evaluation framework.

AI evaluationLLM benchmarksLLM-as-Judge

0 likes · 27 min read

A Systematic Approach to AI Evaluation: From Benchmarks to Real‑World Scenarios

ThinkingAgent

Jun 14, 2026 · Artificial Intelligence

Ontology: The Overlooked Knowledge Infrastructure Driving AI Understanding

The article explains how ontology—a 2,500‑year‑old philosophical concept—provides the structured knowledge backbone that large language models lack, detailing its definition, differences from databases and knowledge graphs, its role in reducing hallucinations, defining knowledge boundaries, enabling reasoning, and four practical AI application scenarios.

AI agentsOntologyRAG

0 likes · 17 min read

Ontology: The Overlooked Knowledge Infrastructure Driving AI Understanding

ThinkingAgent

Jun 13, 2026 · Artificial Intelligence

Prompt Engineering Is Dead—Why Loop Engineering Is the New AI Work Unit

The article introduces Loop Engineering as the next paradigm in AI development, explaining how autonomous, self‑sustaining loops replace manual prompting, compares it with Prompt, Agent, and Harness engineering, outlines core loop structures, modes, goal design, and provides practical code‑first guidelines.

AI AutomationGoal DesignHarness Engineering

0 likes · 20 min read

Prompt Engineering Is Dead—Why Loop Engineering Is the New AI Work Unit

ThinkingAgent

Jun 12, 2026 · Artificial Intelligence

From Hand‑Crafted Features to Harnesses: Five AI Engineering Leaps

Over the past four decades AI has undergone five fundamental shifts—from manual feature engineering, through deep neural networks and prompt engineering, to autonomous agents and finally the Harness era—each redefining core technology, scarce talent, and value creation, with the 2026 Harness era emphasizing system‑level scalability over model size.

AI EngineeringAgent SystemsHarness

0 likes · 16 min read

From Hand‑Crafted Features to Harnesses: Five AI Engineering Leaps