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Introduces CausalDS, a benchmark for evaluating causal reasoning in LLM-based data science agents, using synthetic structural causal models and natural language stories to test associational, interventional, and counterfactual reasoning along with tool use and abstention.
Introduces PatchOptic, an interface for shared-state LLM workflows that uses projected reads and verified structured patches to ensure valid updates. Evaluated with PatchBench across 46 cases, showing reduced token cost and leakage while maintaining quality.
Anthropic published four types of loops for Claude Code to operate autonomously: turn-based, goal-based, time-based, and proactive, allowing different levels of task handoff.
Forethought is a neurosymbolic reasoning system that treats reasoning as an explicit, verifiable program composed from symbolic and neural primitives. It improves base-model accuracy by about 30% relative and enables small models to match frontier models while being model-agnostic and auditable.
Benchmark results for Qwen3.6-35B-A3B-UD-Q8_K_XL on DGX Spark using llama.cpp script by Mia, showing fast token generation times across various context lengths.
The article discusses common causes of cost spikes in AI workflows, such as retries, repeated tool calls, long-running workflows, and growing context, and asks how teams investigate such issues.
The tweet agrees that $200/week is sufficient for engineering and research work, criticizing wasteful spending on expensive models and bloated agentic workflows.
OpenRouter posted on the Insights blog, pointing out that four open-weight models have reached a stage capable of supporting real agent workflows, and explained why the company chose these models in June.
Anthropic's computer use team is hiring product engineers and researchers, seeking candidates who are passionate about agentic workflows and comfortable with ambiguity.
This paper demonstrates that compiling agentic workflow procedures into the weights of a small fine-tuned model achieves near-frontier quality at 128–462× cost reduction compared to in-context baselines, addressing perceived barriers of quality, cost, and flexibility.
John Lindquist shares his talk 'Agentic Power User's Playbook,' covering practical workflows, shortcuts, and habits for efficiently managing AI agents.
A discussion about real-world failures of autonomous AI agents in production, such as sending unauthorized emails, modifying records, deleting data, and spending money, seeking experiences and guardrails.
PostHog explains why 'loops'—self-prompting agent workflows—are gaining traction, driven by improved model capabilities and real-world results from companies like Stripe and Lovable. The thread details what's needed to engineer a loop and showcases examples like PR babysitting and bug fixing.
Atomic Mail launches an API-first email service that gives AI agents their own inboxes, supporting integration with popular agents like Claude Desktop and Cursor via MCP or Agent Skill, and includes Proof-of-Work + reputation to combat spam.
The author shares a methodology for building an external LLM drift detection system that continuously probes model behavior (schema adherence, instruction-following, refusal rates, etc.) to catch silent degradations in API performance, and invites feedback on the approach, pricing, and use cases.
A Twitter thread outlines the seven key areas that will dominate AI engineering interviews in 2026, including LLM fundamentals, RAG systems, agentic workflows, inference optimization, evaluation, MLOps, and production realities.
A practical playbook for building AI-native startups, covering stages from idea to scale with AI-powered exercises and frameworks using Claude.
Vicki Boykis shares her experience using local AI models for development, noting that recent releases like Gemma 4 have made agentic workflows feasible locally with about 75% accuracy of frontier models.
Arize Phoenix's built-in agent PXI now supports slash commands and skills, allowing users to invoke custom workflows directly from chat.
FlowBank introduces a three-stage framework for optimizing agentic workflows in LLM multi-agent systems by precomputing a diverse set of reusable workflows and adaptively selecting the best one per query, achieving higher scores while maintaining cost competitiveness.