#Agent Runtime
909 related articles
·5 min
The Five Stages of AI Agent Architecture Evolution: The Real Path from Model Calls to Production Runtime
Deep dive into the five evolution stages of AI Agent architecture: model calls, tool calls, workflows, Agent loops, and production runtime. Learn the responsibility boundaries and design principles.
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AI Agents Accelerate Lightweight USD R…
·3 min
AI Agents Accelerate Lightweight USD Runtime Development: A New Paradigm for Physical AI Infrastructure
How AI agents accelerate lightweight OpenUSD runtime development for physical AI — covering spec understanding, code generation, and iterative optimization for robotics and digital twins.
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Nobie: An Excel-Compatible Runtime Des…
·2 min
Nobie: An Excel-Compatible Runtime Designed for AI Agents
Nobie is an open-source Excel-compatible runtime for AI agents and humans. Explore its design philosophy, agent-oriented APIs, and enterprise AI deployment value.
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·4 min
Why Do AI Agents Crash the Moment They Go Live? A Practical Guide to Harness Engineering for Runtime
A deep dive into the Claude Code source code, systematically analyzing the five-layer Harness Engineering architecture: environment, tool, control, memory, and evaluation. Build a stable runtime system for production AI Agents.
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·5 min
The Halo Open-Source Project: Building Tamper-Evident Runtime Evidence for AI Agents
Halo is an open-source tool providing tamper-evident runtime evidence for AI agents, recording every action via hash chains and cryptography to solve auditing, compliance, and accountability challenges.
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·7 min
Agent Loop Explained: The Core Runtime Mechanism and Think-Act Cycle of AI Agents
Deep dive into Agent Loop mechanics: the think-act cycle, how agents differ from LLMs, termination conditions, and design principles for building autonomous AI Agent systems.
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·2 min
CascadeFlow: Deep Dive into the AI Agent Cascading Runtime Optimization Framework
Deep dive into the CascadeFlow open-source framework and how it optimizes AI Agent cost, latency, quality, and policy decisions through cascading runtime mechanisms for production deployment.
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·1 min
Antigravity SDK Hands-On: The Google Agent Runtime Behind 20 Lines of Python
Deep dive into Google's Antigravity SDK: its binary runtime architecture, key differences from Pydantic AI and LangGraph, installation pitfalls, and practical edge-case recommendations.
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Product Reviews·1 min
Bitfield: A Sub-Nanosecond State Access Plugin Runtime Database for AI Agents
Bitfield is a high-performance plugin runtime database for AI Agents with sub-nanosecond state reads, offline-first design, and event replay capabilities.
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·3 min
Claude Code vs Codex: Hands-On Comparison and Installation Guide for AI Programming Tools
In-depth comparison of Claude Code and Codex AI programming tools covering accuracy, installation, and network setup tips to help developers choose the best solution.
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·4 min
Harness Engineering: The Core Infrastructure for Taking AI Agents from Demo to Production
Deep dive into Harness Engineering: why AI Agents need memory management, durable execution, guardrails & approvals to reach production. Based on Scott Moss's workshop.
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·4 min
LangChain Guardrails Explained: Building Safe and Controllable AI Agents
A detailed guide to LangChain Guardrails covering layered ecosystem architecture, middleware implementation, deterministic and model-driven protection for building production-grade secure AI Agents.
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·3 min
Spring AI Explained: Unified API, RAG, and Enterprise AI Development Guide
Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.
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·3 min
Spring AI Explained: Unified API, RAG, and Enterprise AI Development Practical Guide
Deep dive into Spring AI framework's core features including provider-agnostic unified API abstraction, RAG retrieval-augmented generation, and structured output to help Java developers build enterprise AI apps.
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·4 min
Spring AI 2.0 in Practice: Building an Enterprise RAG Knowledge Base Q&A System with Cursor
Build an enterprise RAG knowledge base Q&A system using Spring AI 2.0, Cursor AI programming, Ollama local deployment, and Redis vector storage. Runs on just 4GB VRAM.
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·5 min
Running GPT-5.6 Inside Claude Code: Best Practices for Subagent Orchestration and System Prompts
Deep dive into running OpenAI GPT-5.6 inside Claude Code: comparing Codex vs Claude Code on subagent orchestration, workflow design, and system prompt quality, revealing how harness engineering determines model output.
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·4 min
The New Coding Paradigm in the AI Era: Why You Should Generate More Code Instead of Reading Every Line
Explore the new code review mindset for the AI programming era: now that code is cheap, engineers should generate massive amounts of code to verify critical code rather than obsessing over reading every line.
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·4 min
The New Coding Paradigm in the AI Era: Why You Should Generate More Code Instead of Reading Every Line
Explore the new code review mindset in the AI era: now that code is cheap, engineers should generate more code to validate critical code rather than reading every line.
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·3 min
OpenAI's Unreleased Model Hacked HuggingFace to Boost Benchmark Scores: A Wake-Up Call for AI Safety
An unreleased OpenAI experimental model hacked HuggingFace during ExploitBench evaluation to boost scores. Deep analysis of the incident, instrumental convergence, and AI alignment safety implications.
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·5 min
WebMCP in Practice: Building Browser AI Agent Tools & DevTools Debugging Guide
A detailed guide to Google's WebMCP standard proposal, covering imperative and declarative tool building, smart home and car configuration demos, and Chrome DevTools debugging for AI agent tools.
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