Context Rot: How Deep Agents Keeps Long-Running Agents Focused
Context rot degrades long-running agents as tool output piles up. How LangChain's Deep Agents offloads, summarizes and isolates context, and what to tune.
LangSmith AI Search: Finding Agent Failures by Behavior
LangSmith AI Search finds agent traces by behavior, such as false claims of success, using a model that reads each trace. How it works and where it falls short.
AI Agents Context Reset: Fork-and-Flush Lifts Scores 66%
A context reset helps long-running AI agents escape idea basins. Fork-and-Flush beat single runs by 66% on equal compute. What it means for agent design.
Nvidia's SoL-Pi Cuts Coding Agent Token Usage by Nearly Half — Without Touching the Model
Nvidia's SoL-Pi cuts coding agent token use 44-54% by optimizing the harness, not the model: action fusion, context compaction, and observation packing.
GPT-6 Astra Prompting: OpenAI's Slop Blocklist and Action Rules
OpenAI published specific prompting tips for GPT-6 Astra: a slop blocklist, action-bias instructions, and debugging guidance for stalled agents.
Only Believe What You Can Validate: A Verification Framework for Agentic AI
Microsoft's Julia Kordick argues that agentic AI outputs require systematic verification — not expert spot-checks — and maps exactly how to build that into modernization workflows.
Building Production Agentic AI: IBM's Hardest-Learned Lessons
IBM picked LangGraph, A2A, and MCP for production agentic AI — then discovered data access was the real constraint. The architectural decisions that cost them time.
AI Context Compression Has a User Trust Problem: Only 17% of Session Rules Survive
Penn State researchers found that when AI systems compress long conversations, an average of only 17% of user-set session constraints survive. The implications for agentic AI are serious.
Your AI Agent Is Silently Ignoring Your Instructions
When AI systems compress long conversations, they drop most user-defined behavioral rules. A Penn State study found only 17% of session constraints survive — and agents proceed without warning.
AI Rewrites Don't Preserve Meaning — and That Changes How You Should Use Them
Sophie Alpert's principle: every AI rephrase changes what you said. The engineering policy implication is more specific than 'use AI carefully.'
Stateless MCP: Why the Protocol Dropped Session IDs
The 2026-07-28 MCP spec update replaced two-request session handshakes with a single stateless call — simplifying how agentic tools scale.
How to Add a Custom MCP Server to Claude and ChatGPT
A step-by-step look at connecting a custom MCP server to Claude and ChatGPT's chat interfaces — and where ChatGPT's setup gets far more involved.
The Framework for Picking the Right AI Tool, Not Just Any Chatbot
Ethan Mollick's decision framework sorts AI tools by stakes and autonomy, not brand loyalty — and it explains why 'just use ChatGPT for everything' is bad advice.
AI Agents Win by Throwing Away the Chat Log
Researchers replaced growing agent transcripts with a 5-layer structured memory system and cut token usage 90x while doubling win rates. The implication for agent architecture is significant.
The Organizational Iceberg: What's Breaking Enterprise AI Agents
The data layer that sinks enterprise AI agents isn't missing tables or bad ETL — it's the institutional reasoning, judgment calls, and exception logic that never made it into any system.
AI Search Agents: Why Asking Beats Searching More (by 41 Points)
A new benchmark shows AI search agents that ask clarifying questions score 93.4% — while agents that search more score 51.9%. The gap exposes a fundamental problem in how agents handle ambiguous queries.
GPT-5.6 Sol Reasoning Levels: Which One for Which Task
Sol ships with five reasoning levels from Light to Ultra. An OpenAI staffer mapped which task complexity each targets — and why defaulting to the highest level is an expensive mistake.
Agent Memory: The Format Is Markdown — But the Problem Isn't
Andrej Karpathy, Google, and Garry Tan all independently chose Markdown for agent memory in 2026. They agree on the format and disagree on the problem it solves — and that distinction matters for how you build.
Five Fable 5 Prompting Techniques That Fix What Vague Prompts Break
An Anthropic developer's framework for getting better Fable 5 output starts with finding your own knowledge gaps before writing the prompt — not refining the prompt itself.
Anthropic Cut Claude Code's System Prompt by 80% — Because More Instructions Made It Worse
Anthropic reduced Claude Code's system prompt by 80% when switching to Fable 5. The reason inverts a common assumption: more capable models perform worse with more instructions.
shot-scraper video: Your AI Coding Agent Can Now Record Its Own Work
shot-scraper 1.10 adds a storyboard.yml-driven video recorder that lets coding agents autonomously document web app workflows — turning agent output into watchable, reviewable demos.
The AI Coworker Gap: It's About Finishing, Not Answering
Tencent's Youtu Lab defines when AI becomes a real coworker: persistent workspaces that finish tasks, not smarter responses. Most systems aren't there yet.
Four Engineering Pillars for Reliable Agentic AI in Production
Bayer's PRINCE platform proves agentic AI reliability comes from engineering context and harness, not better prompts. Four replicable pillars — context engineering, harness engineering, layered reflection, structured retrieval — from a system live since 2024.
Agentic AI Reliability: Two Engineering Disciplines Most Teams Skip
A Thoughtworks case study on PRINCE, a drug discovery agentic AI platform, reveals why context and harness engineering — not model choice — determine production reliability.
Agentic Engineering vs. Vibe Coding: Simon Willison's Line That Matters
Simon Willison defines agentic engineering as software development with coding agents — and draws a sharp line between it and 'vibe coding.' The distinction has real production consequences.
The Discipline That Makes Agentic AI Worth Using
Most teams chase agentic AI productivity gains without the engineering habits that make them real. A practitioner's stress test reveals what actually works.
Markdown for Agents: Designing Content for the Autonomous Web
Why the future of the web isn't for human eyes, but for agentic consumption via Markdown.
Agentic Workflows: Beyond the Prompt to Orchestrated Logic
How the industry is shifting from single-shot prompts to complex multi-agent graphs in 2026.
Agentic Engineering Patterns: Moving Beyond the Hype to Reliability in 2026
Analysis of emerging patterns in agentic workflows, vibe coding, and the architectural shift toward the Agentic Stack 2026.