Enhancing AI Systems with Agentic Workflows Patterns in Large Language Model
Open accessAbstract
This paper explores the significant shift towards agentic workflows in the application of Large Language Models (LLMs), moving away from traditional, linear interactions between users and AI. Through a case study analysis, we highlight the effectiveness of agentic workflows, which facilitate a more dynamic and iterative engagement, in improving outcomes in tasks such as question answering, code generation or stock analysis. Central to the agentic workflow are four foundational design patterns: reflection, planning, multi-agent collaboration, and tool utilization. These components are crucial for boosting LLM productivity and enhancing performance. The study demonstrates how agentic workflows, by promoting an iterative and reflective process, can serve as a crucial step towards achieving Artificial General Intelligence (AGI).
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The rise and fall of certain research keywords appear to correlate with significant industry milestones such as the release of the Transformer model in 2017, the GPT model in 2018, and ChatGPT-3.5 in December 2022.
“As depicted in Figure 1, the rise and fall of certain keywords appear to have correlated with significant industry milestones, such as the release of the 'Transformer' model in 2017 [13], the GPT model in 2018 [14], and the commercial ChatGPT-3.5 in December 2022.”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [14]
Cites
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BackgroundCRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
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- Authors
- Singh, A., Ehtesham, A., Kumar, S., & Khoei, T. T.
- Affiliations
- Cleveland State University (US)
- The Davey Tree Expert Company
- MathWorks (United States) (US)
- Northeastern University (US)
- Venue
- 2024 IEEE World AI IoT Congress (AIIoT)
- Publisher
- IEEE
- Published
- 2024-05-29
- Pages
- 527-532
- OpenAlex
- W4400491795
- Cited by
- 68
- References
- 15
- Citations by year
- 2026: 25 · 2025: 41 · 2024: 2
- FWCI
- 20.07
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Locations
- IEEE World AI IoT Congress (AIIoT) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- agentic workflows, large language models, design patterns, reflection, planning, agent cooperation, tool use, question answering, code generation, stock market analysis, artificial general intelligence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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