arXiv (All)
open access
Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines
Agentic workflows carry out complex tasks by orchestrating multiple large language models (LLMs) and tools. Serving them at a target throughput with low latency is hard because they are written in arbitrary agentic frameworks and their execution times are unpredictable: execution branches, fans out,
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
- Scepsy: Serving Agentic Workflows Using Aggregate LLM Pipelines#18980
- APIFlow-Bench: Measuring Whether Agents Survive Long, Dependent API Workflows#622623
- Semantics, Workflows, and Infrastructure: Understanding Agent Serving at Production Scale#1128702
- Semantics, Workflows, and Infrastructure: Understanding Agent Serving at Production Scale#1108711
- Towards a Systems Foundation for Agentic Skills: Architecture, Lifecycle, and Security#798236
Record · ID 1105012
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