Review of autonomous systems and collaborative AI agent frameworks
Open accessAbstract
This paper provides an in-depth review of the latest AI agent frameworks, focusing on the comparison of their features, architectures, and use cases. We examine well-known frameworks such as LangGraph, CrewAI, OpenAI Swarm, AutoGen, and IBM Watsonx.Ai, highlighting their strengths, weaknesses, and applicability to various domains. Additionally, we categorize the frameworks based on their specific use cases, including general-purpose agents, enterprise solutions, and open-source frameworks. The paper emphasizes the importance of selecting the appropriate framework to build autonomous AI systems and offers insights into future trends and challenges in AI agent development. By analyzing quantitative metrics such as latency, throughput, and scalability, we provide a data-driven evaluation of the frameworks’ performance. Furthermore, we explore the implications of these advancements in real-world applications, including their impact on financial markets, risk management, and enterprise automation. This review serves as a comprehensive guide for developers and researchers seeking to understand the evolving landscape of AI agent frameworks and their potential for future innovation. Since this is very niche and rapidly evolving field with scarcity of journal papers and this work uses white-paper and model documents to organize this literature review for peer reviewed literature creation. Most of the developments discussed in this work is less than 12 months old. This paper provides a comprehensive review of AI agent frameworks by categorizing literature based on publication year, category, and field.
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Modern agent frameworks often implement a shared knowledge store or blackboard that agents can read from and write to during collaboration.
“Modern agent frameworks often implement a shared knowledge store or blackboard that agents can read from and write to during collaboration [16].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [16]
Section 5.3, “Multi-Agent Systems for Collaborative Problem Solving” (page not provided), discusses collaborative frameworks and proposes that future systems enable agents to share knowledge. It does not say that modern frameworks often implement a shared store or blackboard that agents read from and write to. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMAS orchestration frameworks provide the infrastructure and design patterns to build systems where multiple AI agents collaborate and solve data-analysis problems more effectively than any individual agent alone.
“MAS orchestration frameworks provide the infrastructure and design patterns (messaging systems, shared memory stores, standardized agent interfaces, etc.) to build systems where multiple AI agents collaborate and solve data-analysis problems more effectively than any individual agent alone [9,16].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [16]
Sections 2.1 and 5.3 (pages unavailable) describe orchestration frameworks and multi-agent systems for collaborative problem-solving. They do not establish that agents solve data-analysis problems more effectively than an individual agent, and the cited passages do not specify the messaging, shared-memory, or standardized-interface features in the printed sentence. (LLM, conf=0.86)
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Cites
Review of Gen AI Models for Financial Risk Management
Joshi, S. (2025) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2511114 · Cited by 31 · see citing works
Self-citationJoshi, S. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-23260 · Cited by 14 · see citing works
Self-citationJoshi, S. (2025) · International Journal of Research and Review · doi:10.52403/ijrr.20250212 · Cited by 12 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 7
- Satyadhar Joshi, Review of Data Engineering and Data Lakes for Implementing GenAI in Financial Risk, in JETIR, Jan. 2025.
- Satyadhar Joshi, The Synergy of Generative AI and Big Data for Financial Risk: Review of Recent Developments, IJFMR -International Journal For Multidisciplinary Research, vol. 7, no. 1, doi: g82gmx.
- Satyadhar Joshi, "A literature review of gen AI agents in financial applications: Models and implementations," International Journal of Science and Research (IJSR) ISSN: 2319-7064, vol. 14, no. 1, pp. pp-1094, 2025, Available: https://www.ijsr.net/getabstract.php?paperid=SR25125102816
- Satyadhar Joshi, "ADVANCING FINANCIAL RISK MODELING: VASICEK FRAMEWORK ENHANCED BY AGENTIC GENERATIVE AI," International Research Journal of Modernization in Engineering Technology and Science, vol. 7, no. 1, pp. 4413-4420, 2025.
- Satyadhar Joshi, "Leveraging prompt engineering to enhance financial market integrity and risk management," World Journal of Advanced Research and Reviews, vol. 25, no. 1, pp. 1775-1785, 2025.
- Satyadhar Joshi, "Implementing gen AI for increasing robustness of US financial and regulatory system," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 6, pp. 175-179, 2025.
- Satyadhar Joshi, "Using gen AI agents with GAE and VAE to enhance resilience of US markets," The International Journal of Computational Science, Information Technology and Control Engineering (IJCSITCE), vol. 12, no. 1, pp. 23-38, 2025.
Cited by
Sorted by citations
Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications
Pati, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3585609 · Cited by 100 · see citing works
Joshi, S. (2025) · International Journal of Multidisciplinary Research and Growth Evaluation · doi:10.54660/.ijmrge.2025.6.3.681-688 · Cited by 6 · see citing works
Self-citationLLMOps, AgentOps, and MLOps for Generative AI: A Comprehensive Review
(2025) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr1407.1001 · Cited by 4 · see citing works
Self-citationA Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis
Spieser, J., Balapour, A., Meller, J., Patra, K. C., & Shamsaei, B. (2026) · Methods and Protocols · doi:10.3390/mps9020033 · Cited by 1 · see citing works
“Modern agent frameworks often implement a shared knowledge store or blackboard that agents can read from and write to during collaboration [16].”
- Authors
- Joshi, S.
- Affiliations
- Bank of America (US)
- Corresponding author
- Satyadhar Joshi
- Venue
- International Journal of Science and Research Archive
- Publisher
- GSC Online Press
- Published
- 2025-02-28
- Volume
- 14
- Issue
- 2
- Pages
- 961-972
- ISSN
- 2582-8185 (ISSN-L)
- OpenAlex
- W4407670622
- Cited by
- 31
- References
- 3
- Citations by year
- 2026: 11 · 2025: 20
- FWCI
- 12.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Science and Research Archive (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- AI agent frameworks, autonomous AI systems, LangGraph, CrewAI, AutoGen, agent architecture, throughput and latency, enterprise AI, open source software framework, financial markets, risk management, enterprise automation
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