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AI Architecture Hub
AI Architecture Hub
Jun 17, 2026 · Artificial Intelligence

Stop Misusing AI Agent Loops: Why Most Fail Early and How to Use Them Correctly

The article explains the two main AI Agent Loop patterns—human‑in‑the‑loop and fully autonomous agentic loops—highlights the hidden costs, product‑drift risks, and budget limits of the latter, and provides concrete, low‑risk scenarios and a step‑by‑step code‑review loop that keeps humans in control.

AI Agent LoopAI productivityagentic loop
0 likes · 9 min read
Stop Misusing AI Agent Loops: Why Most Fail Early and How to Use Them Correctly
AI Insight Log
AI Insight Log
Jun 5, 2026 · R&D Management

How Claude Code’s Team Went Four Months Without a Single Human‑Written Line of Code

In a detailed account, Fiona Fung explains how Anthropic’s Claude Code team eliminated the coding bottleneck by relying entirely on AI‑generated code for four months, reshaping planning, information flow, code review, role boundaries, and hiring practices while tracking new performance metrics.

AI code generationcode review automationjust‑in‑time planning
0 likes · 8 min read
How Claude Code’s Team Went Four Months Without a Single Human‑Written Line of Code
DeepHub IMBA
DeepHub IMBA
May 28, 2026 · Artificial Intelligence

AutoGen Multi‑Agent Demo: Coder, Reviewer, and Executor Automatically Complete a Code Review

The article explains how Microsoft’s AutoGen framework enables a Planner‑Executor‑Critic loop and a three‑agent GroupChat workflow, providing step‑by‑step Python code that configures AssistantAgent, UserProxyAgent, and ReviewerAgent to generate, review, and execute code automatically, and discusses the system’s advantages, scalability, and real‑world deployments.

AutoGenGroupChatLLM
0 likes · 13 min read
AutoGen Multi‑Agent Demo: Coder, Reviewer, and Executor Automatically Complete a Code Review
o-ai.tech
o-ai.tech
Mar 31, 2026 · Artificial Intelligence

Why CE’s Agent Design Treats Expert Prompts as Decision Modules, Not Personas

The article explains how many teams instinctively create multiple expert personas for AI agents, but CE instead builds agents as well‑defined judgment modules with clear input and output boundaries, explicit non‑responsibilities, confidence calibration, and systematic orchestration, resulting in a more reliable and maintainable review pipeline.

AI agentsOrchestrationcode review automation
0 likes · 14 min read
Why CE’s Agent Design Treats Expert Prompts as Decision Modules, Not Personas