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Agentic Systems

3 min read

Agentic systems are a category of advanced AI systems focused on autonomy and goal-directed behavior. They possess the following characteristics:

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Agent Skills by Anthropic — Agent Skills are folders of instructions, scripts, and resources that agents can discover and use to do things more accurately and efficiently.

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(Tao et al., 2024)

 MAGIS: a novel LLM-based Multi-Agent framework for GitHub Issue reSolution, MAGIS, consisting of four kinds of agents customized for the software evolution: Manager, Repository Custodian, Developer, and Quality Assurance Engineer agents. This framework leverages the collaboration of various agents in the planning and coding process to unlock the potential of LLMs to resolve GitHub issues. In experiments, we employ the SWE-bench benchmark to compare MAGIS with popular LLMs, including GPT-3.5, GPT-4, and Claude-2.

(Qian et al., 2023)

Note: This paper was mentioned in Crewai’s multi ai agent system course on deeplearning.ai

 In this paper, we present an innovative paradigm that leverages large language models (LLMs) throughout the entire software development process, streamlining and unifying key processes through natural language communication, thereby eliminating the need for specialized models at each phase. At the core of this paradigm lies ChatDev, a virtual chat-powered software development company that mirrors the established waterfall model, meticulously dividing the development process into four distinct chronological stages: designing, coding, testing, and documenting. Each stage engages a team of “software agents”, such as programmers, code reviewers, and test engineers, fostering collaborative dialogue and facilitating a seamless workflow. The chat chain acts as a facilitator, breaking down each stage into atomic subtasks. This enables dual roles, allowing for proposing and validating solutions through context-aware communication, leading to efficient resolution of specific subtasks. The instrumental analysis of ChatDev highlights its remarkable efficacy in software generation, enabling the completion of the entire software development process in under seven minutes at a cost of less than one dollar. It not only identifies and alleviates potential vulnerabilities but also rectifies potential hallucinations while maintaining commendable efficiency and cost-effectiveness.

 Github repo

Microsoft Autogen — (Wu et al., 2023)

To read

Qian, C., Cong, X., Liu, W., Yang, C., Chen, W., Su, Y., Dang, Y., Li, J., Xu, J., Li, D., Liu, Z., & Sun, M. (2023). Communicative Agents for Software Development. https://arxiv.org/abs/2307.07924
Tao, W., Zhou, Y., Zhang, W., & Cheng, Y. (2024). MAGIS: LLM-Based Multi-Agent Framework for GitHub Issue Resolution. https://arxiv.org/abs/2403.17927
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E. (Eric), Li, B., Jiang, L., Zhang, X., & Wang, C. (2023). AutoGen: Enabling next-Gen LLM Applications via Multi-Agent Conversation (Techreport MSR-TR-2023-33). Microsoft. https://www.microsoft.com/en-us/research/publication/autogen-enabling-next-gen-llm-applications-via-multi-agent-conversation-framework/

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