TL;DR
A design pattern is introduced that lets a coding agent work inside the environment directly, and marimo’s rules accumulate the agent’s exploration into a runnable, reproducible Python program.
Large language models (LLMs) can plan behavior for embodied agents from natural language, but treating the LLM as a request/response oracle on the critical path is fundamentally at odds with real-time control and concurrent goals. We argue for an operating-system-style runtime for embodied agents, and instantiate this...
Six model-facing ideas are identified that NOOA is, to the authors' knowledge, the first to combine on a single surface: typed input/output, pass-by-reference over live objects, code as action, programmable loop engineering, explicit object state, and model-callable harness APIs for context and events.
Agent Gym is introduced, a modular, domain-agnostic framework that wraps any existing LLM-based agent in a continuous evaluation-and-evolution loop and introduces the Spec-to-Note Gap, an autoencoder-inspired view of agentic system transparency.
Agentao is presented, a governed local-first runtime for tool-using LLM agents that separates model-generated action proposals from host-authorized execution through a layered architecture consisting of host-facing surfaces, a host contract, a runtime core, a permission-mediated tool system, and supporting subsystems f...
Skill Compilation is introduced, realized in SIGIL, which compiles a prose skill into an executable harness, and is model-independent: the harness holds at 86% across two model generations while prose swings from 56% to 68%.
Traditional application architectures assume behavioral logic authored in advance, leaving reachable behavior largely bounded by explicit code and workflow rules. Large language model (LLM)-based agents challenge this assumption by enabling runtime reasoning and autonomous action to become part of application behavior,...
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