SDET·Oct 25Improving AI Agent Reasoning with In-loop PlanningIn-loop planning is a technique that allows an AI agent to plan, act, observe the results, and revise its plan repeatedly while solving a…
Agent Native·Sep 13Your Agent Is Stuck in Fractal Reasoning BasinIf you are building an AI agent, your eval stack probably measures success rate, tokens, latency, cost, and maybe tool-call accuracy.A response icon1A response icon1
Micheal Lanham·Sep 2Your Agent’s Reasoning Lives in Your Code, Not in Your PromptTips 65 to 70 of 100. A forward pass evaluates. It does not prune, backtrack or iterate, because none of those are things a single pass can…
Nishu singh·May 18Why most “AI analytics” agents hallucinate — and the two bets that fixed it for usA field report from building a multi-agent reasoning system on top of Databricks, Unity Catalog metric views, and LangChain’s deepagents…
Agent Native·May 5ReasoningBank: Memory Architecture Behind Self-Evolving AgentsReasoningBank is a memory framework from Google Research for agentic systems that extracts reusable reasoning strategies from both…
Muhyun Kim·Apr 25What OTEL Traces Reveal About How AI Agents Actually ThinkWhen you ask an AI agent to “analyze this decision” or “create a plan,” what actually happens under the hood? Most people see only the…
Micheal Lanham·Apr 23Reasoning and Planning After Native Reasoning: What’s Left for Architecture in 2026The base model now handles most of the thinking. Here is what the agent harness still has to do about reasoning, planning, and when to…A response icon1A response icon1
Micheal Lanham·Mar 16, 2025The Four Pillars of Effective AI Agents: Planning, Reasoning, Evaluation, and FeedbackIntroduction: Building Better AI Agents
InAI.societybydave ginsburg·Feb 24, 2025AI Society for 2.24.25 — Deep Research Tools UnpackedToday: A deep dive into deep research, AI and your job search, more on OpenAI’s chips and evolving revenue model, how reasoning models…
