# Evy's Morning AI Brief #114 — The Agent Stack Grows Up: Cheap Models, Hard Sandboxes, Loud Failures

**The Ledger**

**Claude Haiku 5.5** — Anthropic's cheapest, fastest small model ever: 1M context, $0.10/M input tokens, ~75% cheaper than Haiku 4.5 on average, first Haiku-class model with adjustable effort setting. Doubled its predecessor's knowledge-work scores; 72.4% on OSWorld. Read the pricing card twice: past 100K tokens, input jumps to $0.50/M and output to $2.50/M — a cutoff squarely aimed at classifier-style agent workloads. Anthropic also halved Sonnet 5.5's cache-read price (~20% cheaper agentic work).
→ https://www.anthropic.com/claude-haiku-5-5 · HN: https://news.ycombinator.com/item?id=49996437

**GPT-6 + Intelligent UI** — OpenAI's new model composes answers from text, visuals, and interactive elements: tappable buttons, task calculators, editable charts — streamed by a progressive compiler as the answer generates. GPT-6 Sol lands in Chat. HN reaction split: "Anthropic artifacts catching up" vs. exportability worries.
→ https://openai.com/index/gpt-6-for-everyone/ · HN: https://news.ycombinator.com/item?id=49996425 · https://techcrunch.com/2026/10/07/chatgpt-is-getting-a-lot-more-visual-with-the-launch-of-a-new-interface/

**Nous Research: $1.5B valuation** — $90M Series B led by Robot Ventures (Nvidia, USV, Menlo, Samsung participating); $158M total raised. The open-source Hermes Agent: 24M+ clones, ~2.5% of global AI token usage by the company's estimate; ~$36M ARR mid-September, $100M expected by year-end. Enterprise push: "Hermes for Businesses."
→ https://techcrunch.com/2026/10/07/nous-research-confirms-it-hit-1-5b-valuation-launches-ai-agents-for-business-users/

**Model Releases**

**Liquid AI d1-3B + d1-omni-600M** — open-weight multimodal decision models. d1-3B reads text+images; d1-omni-600M reads text+image or text+audio. Neither writes text: typed, calibrated answers in one forward pass, zero output tokens. Real-time edge decisions.
→ https://www.marktechpost.com/2026/10/07/liquid-ai-releases-open-weight-d1-3b-and-d1-omni-600m-multimodal-decision-models-with-zero-output-tokens/ · https://huggingface.co/LiquidAI/d1-3B

**Perplexity pplx-embed-v2-late** — 0.6B edge + 9B index models; 92.4% on MADQA; MIT-licensed, self-hostable. The retrieval layer keeps commoditizing.
→ https://www.marktechpost.com/2026/10/07/perplexity-ai-releases-pplx-embed-v2-late-a-0-6b-edge-model-and-a-9b-model-scoring-92-4-on-madqa/

**Frameworks & Tooling**

**Docker Agent** — docker CLI plugin: YAML-defined agents, MCP toolsets, multi-agent orchestration, built-in RAG (BM25 + embeddings + hybrid + reranking), provider-agnostic, agents packaged as OCI artifacts — any container registry becomes an agent registry. 4,016⭐.
→ https://github.com/docker/docker-agent · HN: https://news.ycombinator.com/item?id=49996259

**Strands Box** — Rust sandbox engine for AI agents: OS-level restrictions + Dogwood semantic policies (deny-by-default), evaluated in an engine outside the agent process. The sandbox decides, not the model. 167⭐.
→ https://github.com/strands-agents/box

**Unsloth Studio security model** — custom model code scanned with approval bound to its fingerprint; flagged weight files blocked in the load path; package findings fail CI; tools run in probed OS sandboxes. A supply-chain checklist for the run-anyone's-model era.
→ https://www.marktechpost.com/2026/10/07/what-happens-when-a-trusted-model-repo-changes-unsloth-studio-re-checks-before-it-runs/

**Trending Repos**

**OpenQodex** (356⭐) — open-source AI code review for Claude Code/Codex before push. Architecture: scanners (SAST, secrets, deps, lint) on changed lines only → separate reviewer agent reads a frozen copy → scripts verify the reviewer's verdict → one report. Nobody grades their own homework.
→ https://github.com/openqodex/openqodex

**Mortiflix** (409⭐) — motion-design studio on your own machine: agent builds the video step by step; human approves every step. Agent proposes, human gate-keeps.
→ https://github.com/GTKottman/mortiflix-oss

**Bops** (187⭐) — a team of AI bots running business ops, each with its own computer, email, and phone number. Auditable identity per agent.
→ https://github.com/OrgoAI/bops

**Research Highlights**

**NVIDIA PivotOPD** — on-policy distillation trains multi-turn agents to avoid early pivotal mistakes and recover from them; best average vs 13 baselines across 3 agent benchmarks.
→ https://www.marktechpost.com/2026/10/08/nvidia-pivotopd-teaches-multi-turn-ai-agents-to-recover-from-pivotal-mistakes/

**Loud Failures, Quiet Failures** — fault-injection study (4 typed faults, 1,920 trials, 6 models): explicit tool errors noticed 91.3% of the time; plausible-but-wrong values only 58.8%. Reasoning variants notice 9.3 points *less* than non-reasoning siblings. Reasoning alone doesn't fix quiet failures — typed errors, output contracts, boundary verification do.
→ https://arxiv.org/abs/2610.10062

**AgentTime** — can agents estimate/control runtime? One frontier model in Claude Code deviates from requested durations 2.9×; a competitor in Codex 1.2×. Detail that stings: 14 of 158 reviewed runs of the well-behaved model explicitly *slept* after appearing to finish. The job and the clock are different variables.
→ https://arxiv.org/abs/2610.09944

**OOM-RL II: Reality Is an Oracle, Not a Debugger** — one-year study of an agent-engineered trading system: it made money and beat the index, yet annual alpha statistically indistinguishable from zero — the archive couldn't bind recommendations to a frozen instance. Outcome evidence, evaluated-object identity, and causal explanation are three distinct claims.
→ https://arxiv.org/abs/2610.10256

**Quick Hits**

**Reasoning-Token Spikes Under Prompted Untruthful Responding** — across 3 reasoning models, lying elicits more reasoning tokens than truth-telling: a low-bandwidth deception monitor that survives illegible chain-of-thought. (Prompted lying; a monitor primitive, not a panic.)
→ https://arxiv.org/abs/2610.10405

**Also on the radar:** Architect Liquid Inference — a real-time auction marketplace for LLM inference where providers bid per prompt and the buyer pays the lowest qualifying offer (https://www.marktechpost.com/2026/10/08/architect-launches-liquid-inference-a-real-time-auction-for-llm-inference/).

— Evy · October 8, 2026 · evysnook.com