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The article questions whether AI agents' capabilities stem from true intelligence or improved orchestration of tools around LLMs, exploring what defines a genuine AI agent.
The article asks which AI model—GPT-5.6, Claude Opus 5, or Gemini 3.7 Flash—would be most trusted to handle a production incident, comparing their capabilities in tool orchestration, context retention, and cost efficiency.
Skillscript is a declarative, sandboxed language that allows AI agents to define and execute reusable, composable workflows (skills) for orchestrating tools, models, and data stores, reducing reliance on costly frontier inference for routine tasks.
This paper introduces CanvasCraft, a large-scale multimodal tool-use dataset for complex image creation and editing, and CanvasAgent, a tool-augmented multimodal agent that learns to orchestrate heterogeneous visual tools through multi-turn interactions and hybrid reward optimization.
This paper proposes Robust-TO, an agentic video understanding framework that integrates per-frame trustworthiness to address the Blind Trust Problem, achieving significant accuracy gains under realistic perturbations.
Vercel released AI SDK 7, a major update to their TypeScript SDK for building AI applications, adding enhanced agent development, reasoning control, tool context, runtime context, and more.
Robust-TO addresses the Blind Trust Problem in video reasoning by integrating per-frame trustworthiness into an agentic framework, improving accuracy under realistic perturbations through calibrated evidence weighting and reliability-aware reasoning.
Santiago (@svpino) highlights AG-UI as the fastest-growing agentic protocol after MCP, a lightweight event-streaming protocol for building user-facing AI agents with support for real-time updates, tool orchestration, shared mutable state, security, UI sync, and now threads for resumable conversations.
The LOOP Skill Engine achieves 99% success and 99% token reduction for periodic AI agent tasks by recording a single LLM-driven execution and replaying it deterministically via a parameterized, branch-free skill, eliminating stochastic failures and high costs.
This paper proposes typed mediation, where language models orchestrate deterministic tools instead of generating analytical code, ensuring identical outputs across regenerations. Evaluated on photoluminescence analysis, the pattern achieves perfect reproducibility across multiple runs, unlike commercial foundation models, and has been deployed successfully in real instruments.