InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios

Kari Jaaskelainen

29 Sep 2025 — 1 min read

Auto-generating, routing, auditing, and evolving multi-agent systems as a DAG pyramid

Building effective LLM agents typically demands bespoke workflows, prompts, and tuning—a bottleneck to scale. InfiAgent proposes a pyramid-like DAG framework that automates agent creation, coordination, and continuous improvement across “infinite” scenarios. Its generalized “agent-as-a-tool” mechanism decomposes complex tasks into hierarchical, atomic subtasks handled by specialized agents, enabling parallel execution and reuse.

Quality and stability are enforced via a dual-audit mechanism, while an agent-routing function matches tasks to the best-suited agents. Most notably, the system self-evolves: it restructures its DAG when new tasks appear, performance degrades, or optimization opportunities are detected—reducing the need for manual redesign.

Agent-as-a-tool: hierarchical decomposition into reusable, specialized agents.
Dual audits: checks for both outcome quality and process reliability.
Routing: efficient task–agent matching for speed and accuracy.
Self-evolution: automatic DAG restructuring driven by performance signals.
Parallelism: atomic task design unlocks concurrent execution for throughput gains.

On benchmarks, InfiAgent outperforms a closely related auto-generated agent framework (ADAS) by 9.9%. A case study, InfiHelper—the AI research assistant—illustrates the framework’s practical impact, generating scientific papers that received positive recognition from human reviewers at top IEEE venues.

The bottom line: InfiAgent shifts multi-agent development from bespoke engineering to self-optimizing systems, promising lower deployment costs and broader applicability across industries.

Paper: http://arxiv.org/abs/2509.22502v1
Register: https://www.AiFeta.com

#Agents #LLM #Automation #MultiAgent #Orchestration #AutoGen #AIEngineering #Scalability

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