The DOI matches its Crossref record, with a small difference in title or year.
Agentic AI: A Quantitative Analysis of Performance and Applications
Open accessAbstract
This study presents a comprehensive quantitative analysis of Agentic AI performance and applications across various industries. Agentic AI, an emerging field combining advanced AI techniques with enterprise automation, has shown promise in creating autonomous agents capable of complex decision-making and problem-solving. Our research, conducted over a 12-month period, employed a mixed-methods approach, analyzing data from 500 organizations and incorporating insights from 50 industry experts. The study aimed to evaluate the efficiency, accuracy, and impact of Agentic AI systems compared to traditional AI approaches.Results demonstrate that Agentic AI systems significantly outperform traditional AI, with a 34.2% reduction in task completion time, 7.7% increase in accuracy, and 13.6% improvement in resource utilization. Productivity gains varied across industries, with the technology sector showing the highest improvement at 45%. The study also revealed high scalability of Agentic AI solutions across different organizational sizes, although implementation time increased with organization complexity.Key challenges identified include data privacy concerns, integration difficulties with legacy systems, skill gaps, and ethical considerations. Despite these challenges, the study concludes that Agentic AI has significant potential to transform business processes and decision-making across various sectors. Future research directions include enhancing interpretability, optimizing domain-specific applications, and exploring multi-agent collaborations.This research contributes valuable insights into the current state and future prospects of Agentic AI, providing a foundation for further development and implementation strategies in this rapidly evolving field.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Agents model user behavior, provide recommendations and context, coordinate processes across domains, automate business processes, break down goals, utilize external knowledge, and coordinate safely and ethically [53,132,133].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Agentic frameworks like LangGraph [53] and CrewAI [53] show around 94% accuracy in complex task execution.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Model implementations LangGraph [58], Autogen [151], and GoEX [53] averaged 12 ± 2 min TCT, which is a 34.2% improvement against traditional AI in general.”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Agents often function autonomously, and will operate in dynamic and diverse real-world conditions that make them susceptible to a range of risks (e.g., adversarial attacks, data poisoning, or inappropriate use of external tools or APIs) [53].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“There are privacy and security risks; almost 78% of organizations reported problems with data safety while using these, which shows they needed stronger rules to protect personal information [53].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
The cited source says what the claim attributes to it.
In the citing paper
“Agents often function autonomously, and will operate in dynamic and diverse real-world conditions that make them susceptible to a range of risks (e.g., adversarial attacks, data poisoning, or inappropriate use of external tools or APIs) [53].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Effective human-AI interaction often entails poor or insecure interface design, along with overly rigid communication protocols, which fail to address the differing user roles and contexts that may involve non-expert or at-risk user groups [53].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“When executing multi-step and complicated tasks, there is often delay or issues pertaining to computational inefficiencies, especially during simultaneous interaction of the agents and the set of diverse external tools in real time [53].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Operational challenges that arise in the real-world are often the result of coordination failures like inconsistency in data access, communication latency, and misalignment stemming from a failure to share context [53].”
From The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges (2025) · cites this paper as [53]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 1
- Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Preprints.org (www.preprints.org) | NOT PEER-REVIEWED | Posted: 20 February 2025 doi:10.20944/preprints202502.1647.v1
Cited by
Sorted by citations
Bandi, A., Kongari, B., Naguru, R., Pasnoor, S., & Vilipala, S. V. (2025) · Future Internet · doi:10.3390/fi17090404 · Cited by 207
Metadata
- Authors
- Sawant, P.
- Corresponding author
- Sawant, Prashant
- Venue
- Preprints.org
- Publisher
- MDPI AG
- Published
- 2025-02-20
- OpenAlex
- W4407776740
- Cited by
- 8
- References
- 0
- Citations by year
- 2026: 5 · 2025: 3
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- Preprints.org (acceptedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Management Information Systems › Robotic Process Automation Applications
- Keywords
- agentic AI, autonomous agents, enterprise automation, task completion time, resource utilization, productivity improvement, industrial adoption, data privacy, legacy system integration, skills gap, AI ethics, agent cooperation, interpretability
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Agentic AI: A Quantitative Analysis of Performance and Applications. https://app.cochat.ai/papers/10.20944/preprints202502.1647.v1
- The paper
- Sawant, P. (2025). Agentic AI: A Quantitative Analysis of Performance and Applications. Preprints.org. https://doi.org/10.20944/preprints202502.1647.v1
