AAFlow: Scalable Patterns for Agentic AI Workflows
Although these frameworks increase flexibility, they don't have a formal execution model that adheres to the principles of high-performance computing. We introduce AAFLOW, a unified distributed runtime that creates communication-efficient execution plans by modeling agentic workflows as an operator abstraction. Using Apache Arrow and Cylon, AAFLOW creates a zero-copy data plane that allows direct interoperability between preprocessing, embedding, and vector retrieval without the need for serialization overhead.
- ▪Although these frameworks increase flexibility, they don't have a formal execution model that adheres to the principles of high-performance computing.
- ▪We introduce AAFLOW, a unified distributed runtime that creates communication-efficient execution plans by modeling agentic workflows as an operator abstraction.
- ▪Using Apache Arrow and Cylon, AAFLOW creates a zero-copy data plane that allows direct interoperability between preprocessing, embedding, and vector retrieval without the need for serialization overhead.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2605.02162 |
| Publication time | Tue, 04 Aug 2026 20:34:04 +0000 |
| Retrieval time | 2026-08-04T20:55:41.500Z |
| Last seen | 2026-08-04T20:55:41.500Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | M2QLAP-fNz_f · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2605.02162 (cs) [Submitted on 4 May 2026] Title:AAFLOW: Scalable Patterns for Agentic AI Workflows Authors:Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi, Gregor von Laszewski, Shantenu Jha, Geoffrey Fox View a PDF of the paper titled AAFLOW: Scalable Patterns for Agentic AI Workflows, by Arup Kumar Sarker and 5 other authors View PDF HTML (experimental) Abstract:Agentic workflows in large language model systems integrate retrieval, reasoning, and memory, but existing frameworks suffer from scalability and reproducibility limitations due to fragmented data orchestration, serialization overhead, and non-deterministic execution.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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