Turing Post

Today’s editorial:What is graph engineering, why is everyone suddenly talking about it, and how is it different from loop engineering, GraphRAG, and ordinary workflow orchestration? Friday / Global AI Affairs: Where Global AI Rankings Fall Short Sunday / Library: AI protocols every builder should know We recommend: 👨‍🔧 Designing an Agentic Platform: The Infrastructure That Makes Agents Work Build Agents That Survive Production. Gartner predicts 40% of agentic AI projects will be canceled by 2027.

TL;DR  AI can make individual tasks faster without improving the business. Value appears only when saved effort becomes usable capacity, the organization deliberately absorbs that capacity, and a measurable outcome changes. This four-stage Capacity-to-Outcome Chain explains where enterprise AI returns keep disappearing.

Today’s editorial: We propose a Post-Necessity Institute to test how AI abundance can expand agency, purpose, and access before economic disruption arrives.

AI coding tools help developers write, debug, review, test, and ship code with LLMs, and in 2026, this means much more than autocomplete. The category includes coding assistants, AI-native IDEs, terminal agents, repo-level agents, and open-source coding models that can run locally or inside existing dev workflows.

What is an AI-native enterprise? An AI-native enterprise is an organization designed so AI can understand, operate, and improve its work. It is not a company that simply gives employees access to chatbots or adds agents on top of old processes. It is a company whose workflows, data, tools, permissions, feedback loops, and business rules are structured so machines can participate in the work reliably. In practice, almost no large enterprise is fully AI-native yet.

Announcing a super interesting workshop from our partners β†’ AI coding agents like Claude Code are transforming software development, but most security teams are still protecting against human-speed threats. When an agent can read, write, and execute code autonomously, EDR, DLP, and static rules-based controls simply weren't built for it. The attack surface has changed. The security playbook has to change with it.

TL;DR: AI agents in 2026 are becoming durable systðms with memory, tools, skills, local control, physical action, and self-improvement loops. This recap maps the shift from OpenClaw and Hermes to VLA models, Web World Models, RSI, and Responsible AI infrastructure. AI progress in 2026 is now coming from many different directions. Some advances rethink model structure, like DeepSeek mHC and depth-addressable Transformers.

TL;DR: These agentic AI surveys map how modern agents reason, plan, use tools, remember, evaluate, and stay safe. Use them to understand agent reasoning frameworks, LLM planning, tool use, production architectures, and the shift from chatbots to autonomous AI systems in 2026. Interest in agentic systems keeps growing, but the field is also getting harder to follow.

Announcing a super interesting workshop from our partners β†’ Claude is running in your enterprise. It’s scheduling, drafting, analyzing, and making calls – and in most organizations, nobody has a clear answer to a very simple question: what exactly is it doing? That’s not a Claude problem. That’s a governance problem.

TL;DR: AI agents in 2026 are becoming durable systðms with memory, tools, skills, local control, physical action, and self-improvement loops. This recap maps the shift from OpenClaw and Hermes to VLA models, Web World Models, RSI, and Responsible AI infrastructure. AI agents became the center of the first half of 2026. We got persistent systems with memory, tools, skills, that can act across software and physical environments.