arXiv (All)
open access
Adaptive Workflow Intelligence: A Cognitive Architecture for Context-Driven Enterprise Automation
Enterprise systems increasingly rely on automated workflows, yet many AI-driven solutions remain brittle under non-stationary conditions, evolving policies, and delayed operational feedback. While reinforcement learning and large language model (LLM) agents offer partial adaptability, they do not by
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- ERPBench: Evaluating LLM Agents for Enterprise Decision-Making Across Competitive Market Ecologies#927934
- Shaping Opinion: Quantifying the Psychological Impact of Autonomous Multi-Agent LLM Interactions#1141691
- Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agents#349699
- A Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability#1325714
- EmailBench: A Benchmark for Evaluating LLM Agents on Enterprise Email and Productivity Tasks#1115862
Record · ID 1232543
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive