Abstract
With the rapid development of artificial intelligence, Large Language Model (LLM) agents have demonstrated remarkable capabilities in organizing and executing complex tasks. However, their development heavily relies on carefully designed workflows, repeatedly debugged prompts, and deep domain expertise. This highly manual approach significantly hinders the large-scale adoption and cost-effectiveness of agent-based technology across industries.
AI application technologies are becoming increasingly diverse, with Agent technology emerging as the super-popular rising star in research. From ChatGPT to Claude, and various specialized AI assistants, Agent technology is reshaping how we interact with technology, transforming AI from simple Q&A tools into intelligent agents capable of understanding and executing complex tasks.
Current State of Agent Field
Many teams are now working on Agents, and numerous interesting developments have emerged:
- ReAct-style general agent frameworks that solve complex problems through “reasoning-action” loops
- AutoGPT attempting fully autonomous task execution
- Multi-agent systems like Multi-Agent Debate that improve decision quality through collaborative agents
- RAG-based knowledge-enhanced agents trying to compensate for model limitations with external knowledge bases
Our team has also developed strong interest in this field and has begun conducting research work.
What Problems We Identified
During our deep research into existing solutions, we discovered several core challenges:
First, the scalability-stability contradiction. Traditional workflow models rely on meticulously designed call paths and prompts, lacking generalization and robustness. Even when developing numerous low-level tools, they often cannot be easily repurposed for other tasks. Different tasks typically require these tools to be completely rearranged.
Second, the context management dilemma. The core challenge in multi-agent systems lies in balancing context sharing. Without shared context, maintaining alignment with initial requirements becomes difficult, especially as agent numbers increase. Achieving effective performance from systems comprising hundreds or thousands of collaborating agents is challenging.
Third, the quality control dilemma. While some development teams insist on single agents or cascading agents to maintain common context, this leads to instability in complex tasks due to exponential growth in context length.
Finally, the evolution capability gap. Existing RAG, knowledge graphs, or message compression approaches often struggle to support even moderately complex QA testing. Information loss and semantic bias from repeated compression remain serious issues in complex tasks.
After these observations, we believe a fundamental architectural innovation is needed to address these problems.
How We Solved These Problems
Facing these challenges, we proposed InfiAgent, a pyramid-style multi-agent framework based on Directed Acyclic Graph (DAG).
InfiAgent pyramid-style multi-agent framework architecture.
Our core design philosophy uses an “agent-as-a-tool” mechanism to achieve automated decomposition and distribution of complex tasks. The entire architecture presents a pyramid-like hierarchical structure: the top layer contains a root agent receiving user initial tasks; layers below contain numerous planning and routing agents that don’t execute tasks directly but decompose complex problems into more granular subtasks; ultimately, all atomic tasks are delegated to bottom-level functional agents for execution.
To ensure scalability and stability, we strictly limit coordination complexity for each agent, typically managing a maximum of five sub-agents. This design allows the system to accommodate exponential growth in agent numbers while preventing individual node overload.
Our Core Advantages
InfiAgent is built upon four key innovations we believe in:
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Automated Hierarchical Decomposition Capability
Leveraging the “agent-as-a-tool” mechanism, the framework can autonomously plan, decompose, and invoke appropriate subordinate agents for collaborative completion.
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Dual-Audit Mechanism
We introduced auditing at both the execution and system levels. Execution-level auditing monitors the output quality of each agent in real-time, while system-level auditing maintains overall stability and provides context summarization.
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Intelligent Task Routing and Efficient Context Control
InfiAgent integrates intelligent routing that requires no manual configuration, efficiently matching tasks with the most suitable agents. Combined with innovative structured context management, we significantly improve communication efficiency and reduce token consumption.
The framework supports evolution at model, agent, and topology levels, enabling the entire system to continuously improve and adapt to changing needs, truly achieving “self-evolution.”
Experimental Results
Theoretical innovation must be validated in practice. InfiAgent has demonstrated strong performance across multiple standard benchmarks:
On DROP, HumanEval, MBPP, GSM8K, and MATH benchmarks, it performs especially well on complex tasks requiring multi-step reasoning. It outperformed the best baseline by 3.6 percentage points on DROP tasks, achieved 93.1% accuracy on GSM8K math problems, and scored 89.3% on HumanEval code generation.
Experimental results across multiple benchmarks.
Even more convincing is that our AI research assistant InfiHelper, built on the InfiAgent framework, has end-to-end generated research papers accepted by human reviewers at IEEE conferences, with almost no human participation!
InfiHelper research assistant performance demonstration.
Furthermore, the quality of papers generated by InfiHelper also exceeds that of many current workflows specifically designed for AI-Researcher.
Quality comparison of papers generated by InfiHelper vs other workflows.
Conclusion
We believe InfiAgent represents a paradigm shift in multi-agent system design. Its unique pyramid-style DAG architecture, self-evolution capability, and rigorous quality control mechanisms substantially address long-standing industry challenges including scalable adaptability, context management, and system reliability.
Whether for complex enterprise process automation, scientific research, or everyday problem-solving, InfiAgent provides a highly modular, scalable, and robust foundational framework. It not only represents the state-of-the-art in multi-agent technology but also paves a new path toward artificial general intelligence (AGI).
We sincerely welcome industry colleagues and academic partners to join us in exploring InfiAgent’s infinite possibilities and advancing AI agent technology development together.
Citation Information
If you find this work useful, citations to the following papers are welcome:
@misc{yu2025infiagentselfevolvingpyramidagent,
title={InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios},
author={Chenglin Yu and Yang Yu and Songmiao Wang and Yucheng Wang and Yifan Yang and Jinjia Li and Ming Li and Hongxia Yang},
year={2025},
eprint={2509.22502},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2509.22502},
}