Agentic AI
Articles sur Agentic AI
Stop Scoring Agents Zero at 90% Done
Continuous rubrics, exact-key simulations, and typed decision judges for hill-climbable agent evals
Starting New Threads Is Becoming a Paid Perk — Here’s Why
From October 20, starting new chat threads is a paid-member perk. Here's what changes, what doesn't, and why we're doing it.
The Eval Is Under Attack: Benchmark Integrity for Eval-Aware, Multi-Agent Systems
Since mid-2025, capable agents have treated the eval harness as part of the solvable environment: they hunt answer keys, rewrite tests, and emit punctuation that fools LLM judges. Anthropic’s BrowseComp post-mortem logged 2 successful XOR decrypts of a 1,266-problem key (18 runs tried the same strategy; one run burned 40.5 million tokens), and the Agentic Benchmark Checklist estimates harness flaws can misstate agent performance by up to 100% in relative terms. Eval integrity is now a security control, not a one-time design checkbox.
Back by popular demand: Cohort 2 of Hands-On Harness Engineering with Claude
Following the completion of our very successful first session a couple of months ago, I am partnering with Packt Publishing Limited to run Cohort 2 of my live masterclass, Hands-On Harness Engineering with Claude, on Saturday, October 17, 2026, from 9:00 AM to 11:30 AM EDT (6:00 AM – 8:30 AM PDT / 1:00 PM – 3:30 PM UTC).
What Is Agentic AI Escalation Architecture?
Innovation is moving at unprecedented speed.
I Love Google’s Agentic AI Security Roadmap: Adding MAESTRO Will Make It More Powerful — An Open Letter to Google
In June, Google DeepMind did something unusual: it published an AI control roadmap premised on the idea that its own AI agents might turn against it.
AI Inference Memory Technologies and Market Cycles
A technical analysis of memory architecture, worldwide supply, and boom and bust risk
Claude Eval Design and Hillclimbing: From Reliable Tests to Better Agents
Claude can automate evaluation design and propose application changes using /claude-api build-eval and /claude-api hillclimb, but a higher score supports a deployment decision only when the measurement remains trustworthy. The engineering work starts with representative cases, calibrated grading, and an explicit decision rule. It continues with a separation between the evidence you use to improve the application and the evidence you use to approve its release.
Your AI Strategy Needs a Degraded Mode
Innovation is moving at unprecedented speed.
The Next AI Moat Is Workflow Economics
Innovation is moving at unprecedented speed.
Springer Publishes Humanoid Robots and Physical AI Book
Springer has published Humanoid Robots and Physical AI: Reshaping Work, Society, and Human Purpose. The live book is at DOI 10.1007/978-3-032-33669-9. The volume runs XXVII + 381 pages across ten chapters, with 90 color illustrations, in the series Advances in Data Analytics, AI, and Smart Systems
Close the Loop: Find, Fix, and Prove It
Auto-Remediation with RidgeGen™ and Google CodeMender
Chapter 10: Constrained Decoding & The 2026 Production Blueprint: FSM Grammars to Edge SLMs
By AI Researchers at DistributedApps.ai | 2026 Foundation Model Inference Series
What is JEV from TypeSafe AI? How We implemented Agentic SOC use cases and other use cases? (Github Repo URL Included in Paid Section)
Jev, the first System One model from TypeSafe, returns a Choice, Score, or Noul probability instead of a chat string. Diogo Almeida published the model on 15 September 2026. TypeSafe prices input at USD 0.042 per million tokens, does not bill output tokens, and states an end-to-end time of 70 to 500 milliseconds, against 3 to 329 seconds for the chat models they timed on the same task.
Chapter 9: Ultra-Long Context Mastery: Dual-Chunk Attention, YaRN & Sparse Routing
By AI Researchers at DistributedApps.ai | 2026 Foundation Model Inference Series
Chapter 8: Test-Time Compute & Reasoning Dynamics: DeepSeek-V4-Pro, GLM-5.3 & Kimi K3
By AI Researchers at DistributedApps.ai | 2026 Foundation Model Inference Series
Chapter 7: Serving Mega-MoE at Scale: Colossus Interconnects, Expert Parallelism & DualPipe
Auxiliary-Loss-Free Bias Routing, 4D Parallelism (TPxPPxEPxDP), Bidirectional DualPipe Overlap, and 100k+ GPU Non-Blocking Clos InterconnectsTechnical Analysis & Systems Synthesis by DistributedApps.ai | Synthesizing frontier research from DeepSeek, Moonshot AI, Zhipu AI, Alibaba, NVIDIA, and Google DeepMind into production engineering blueprints.
GPT-6 Astra plus Kimi K3 Swarm: route depth and width before you pay frontier rates
Astra for deep judgment, Kimi K3 Swarm for width up to 300 agents, and a router that spends frontier tokens only where failure cost justifies them.
OpenAI’s Defense Factory turns vulnerability work into a continuous five-stage agent loop
OpenAI published its Defense Factory architecture in September 2026: an agent-first operation that inventories systems, discovers candidates, validates exploitability in isolated environments, routes ownership, prepares Codex patches, and independently retests deployed fixes.
Chapter 6: Disaggregated Serving Architectures: Mooncake, DistServe & vLLM V1
By AI Researchers at DistributedApps.ai | 2026 Foundation Model Inference Series
We Can’t Sleepwalk Through the AI Transition
Guest post by Camille Stewart Gloster, the first Deputy National Cyber Director for Technology and Ecosystem Security at the White House
Nadella’s AI moat is the learning loop your company owns, not the model you rent
In Microsoft’s Stanford CS153 session with Michael Abbott, Satya Nadella frames the firm’s problem in one line: if models learn from data, what remains of a company whose value has lived in tacit knowledge from operations and people. His answer is not a bigger rented model. It is a company-owned learning loop—what he calls a “hill-climbing machine”—built from private evaluations, workflow traces, rewards, and outcomes that stay inside the tenant.
CLAUDE.md Modernization and Prefix-Stable KV Caching for Claude Code
CLAUDE.md loads on every Claude Code session: trim it to a static bootstrap, split memory by volatility, and compile .kb/ in byte-stable order so multi-turn Key-Value (KV) prefix hits survive.