AI Agent Papers2026
Guides an LLM agent to build persistent, editable DAG-based data pipelines via typed, incremental mutations instead of free-form scripts. Combines DataFlow-Skills, a Model Context Protocol exposing live operator registry and pipeline state, and a synchronized Web UI; achieves 93.3% end-to-end pass rate on a 12-task benchmark while cutting cost and latency versus script baselines.
AI Agent Papers2026
Lets canvas-native agents plan, generate, edit, and organize long-horizon multimodal creative projects by representing artifacts, versions, and actions as typed canvas nodes and links. Uses a three-layer design (canvas state, protocol bridge, agent runtime) so agents act within an inspectable, editable project state.