We present PDEFlow, an autonomous agentic framework that turns user-level ODE and PDE descriptions into solver-backed neural-operator pipelines. The workflow links problem specification, data generation, operator training, and checkpoint-based inference. A stateful input graph converts multi-turn natural-language input and user edits into validated problem specifications. The data-generation module then samples parameters, solves the configured governing-equation with FEniCSx finite-element backend, and stores the solutions as operator-ready tensors. The training and inference stages use a registry-based interface, allowing different neural operators to be trained and deployed without changing the surrounding pipeline. In the current implementation, we instantiate this interface with a multi-branch Bayesian DeepONet. Experiments on benchmark ODE and PDE tasks show that PDEFlow can construct valid specifications, generate solver-backed datasets, train neural operators across steady and transient problem classes, and provide solver-free predictions from saved checkpoints. The framework is designed for repeatable scientific and engineering workflows where many related physics configurations must be specified, simulated, learned, and queried with minimal manual intervention.
Agentic AI is enabling cloud-based workflows in which autonomous agents reason over operational state, invoke authorized tools, modify software and infrastructure, deploy services, verify execution outcomes, and adapt across long-horizon, multistep tasks. Engineering such workflows requires explicit mechanisms for workflow progression, constrained execution, failure recovery, and verifiable completion. We present Agentic Cloud Workflow Engineering, an agentic AI framework that transforms natural-language agentic cloud-engineering tasks into validated code repositories and verified operational cloud deployments for automating cloud-based agentic workflows. The framework separates three complementary concerns: graph engineering specifies long-horizon workflow progression and verification-dependent transitions; loop engineering provides bounded diagnosis, repair or re-planning, retry, and re-verification; and agent harness engineering enforces zero-trust execution through identity, authorization, policy-scoped capabilities, isolation, and runtime safeguards. Workflow progression and completion require machine-checkable repository, deployment, and runtime evidence, with recovery constrained by explicit operational bounds and termination criteria. We instantiate the framework on Google Cloud and evaluate repository completeness, controlled execution, evidence-gated progression, operational deployment, and bounded recovery. Experimental results show that executions terminate with either a verified operational cloud deployment or an auditable terminal failure under bounded recovery. The framework provides a unified engineering architecture for cloud-based workflows spanning Agentic DevOps, Agentic CloudOps, Agentic SRE/AIOps, Agentic SecOps, Agentic DataOps, Agentic MLOps/LLMOps, AgentOps, Agentic RAG/GraphRAG, and related cloud-engineering domains.
As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules to improve their performance. These concepts are applied to explain other players' actions, and to inform their own actions in-game. Understanding other players' strategies is a crucial part of such improvement, but requires time and effort. In this paper, we propose a strategy similar to human cognition for training RL agents to synthesize learned strategies and policies as executable procedures based on sequences of gameplay actions. We present methods to automatically learn such programs to play chess and to solve tasks in a grid-based environment. We show that the learned strategies produce effective actions, and can be learned from gameplay data.
Piping and Instrumentation Diagrams (P IDs) are the authoritative maps of process plants: isolation, maintenance, and HAZOP decisions depend on what connects to what. Vision-language models describe these sheets fluently, yet they often invent or miss process connections—and an invented or missed link can reverse an isolation or reachability call, so a plant decision cannot trust a fluent answer that was never checked against the linework. We instead recover an explicit graph of the drawing—its symbols, the process connections between them, and the tags that name them—and then require the model to answer only by querying that graph through seven read-only operators, so a topology claim is returned only when it cites the query results that support it. On TopoPID-VQA, a new suite of 3000 topology questions over these sheets, Graph-Grounded Harness (Ours) raises exact match accuracy from 36.7–41.3
Turning an engineering request into a simulation requires consistent geometry, physics, boundary conditions, and numerical settings. Recent agent-assisted systems automate simulation setup, but successful execution alone does not establish whether the specification preserves the intended problem or correctly handles missing information. We study whether agents can reliably construct complete executable JSON specifications for automated geometry creation, meshing, PDE solving, and result export. Our workflow combines model extraction with deterministic scientific and capability checks, explicit clarification, and validated local patches that preserve unrelated accepted fields. We evaluate specification correctness, execution completion, and numerical verification separately. On 120 template-related requests, the full workflow achieves 98.3
