Agentic AI

Hugging Face has introduced Holo3.1, a new model designed for executing tasks on local desktop environments. The release emphasizes speed and the ability for agents to interact with computer interfaces directly. This update targets practitioners needing autonomous software interaction capabilities without relying on external APIs.

• Holo3.1 focuses on rapid execution for desktop automation tasks.
• Enables local agents to interact with GUIs without cloud dependencies.
• Part of Hugging Face's push toward practical AI agent utilities.
• Released June 2, 2026, as an update to previous agent models.

This paper addresses the unreliability of LLM-generated web scrapers by shifting output from free-form code to constrained, typed JSON collector configurations. The proposed framework uses a six-type collector taxonomy, static Airflow DAG execution, and rule-based quality checks to ensure stability. Experiments on 138 tasks demonstrate that while description-based typing works, reliable instantiation requires completing source, field, and execution constraints beyond initial generation.

• LLM scrapers often fail due to dependency errors and schema mismatches in heterogeneous pages.
• Output is constrained to typed JSON using a six-type collector taxonomy for better reliability.
• Static Airflow DAG execution and rule-based quality checks enforce structural integrity.
• Complete source, field, and execution constraints are critical for stable collector instantiation.
• Structured feedback correction helps resolve errors during the verification process.

Mnemosyne proposes Agentic Transaction Processing (ATP) to treat AI-generated workflow actions as untrusted proposals until they pass deterministic admission against a declared constraint set. This model ensures that only runtime-admitted actions are committed, preventing stale, infeasible, or destructive outputs from disrupting system state. When unforeseen disruptions occur, the system repairs reactively within bounds rather than blindly trusting new proposals.

• Treats LLM-generated actions as untrusted until validated against executable constraints.
• Prevents destructive AI outputs by enforcing deterministic admission at runtime.
• Enables reactive repair within bounds instead of relying on fresh AI proposals.
• Decouples proposal generation from state commitment to ensure system stability.

herdr is a Rust-based terminal multiplexer designed to run multiple coding agents simultaneously within a single session. It provides real terminal sessions for each agent, ensuring full compatibility with TUIs, and allows users to monitor agent states like blocked or done via split panes. The tool supports SSH reattachment and remote execution, eliminating the need for GUI wrappers or telemetry.

• Runs multiple coding agents in one terminal with real TUI support
• Monitor agent status (blocked, working, done) via split panes
• SSH reattachable from any device, including phones
• Built in Rust as a single binary with no GUI or telemetry