InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios

Kari Jaaskelainen

29 Sep 2025 — 1 min read

Auto-constructing, auditing, routing, and evolving multi-agent DAGs—at enterprise scale.

Building capable LLM agents typically demands bespoke workflows, brittle prompts, and expert iteration. InfiAgent proposes a general, self-evolving alternative: a pyramid-like, DAG-based multi-agent framework that decomposes complex work into hierarchical, reusable “agent-as-a-tool” components—then continually restructures itself as tasks change.

Key innovations: (1) automatic hierarchical decomposition into specialized sub-agents; (2) a dual-audit mechanism that couples quality checks and stability guarantees; (3) intelligent agent routing for efficient task–agent matching; and (4) a self-evolution routine that rewrites the DAG when performance dips or new opportunities arise. Atomic task design enables parallel execution, cutting latency without sacrificing reliability.

On multiple benchmarks, InfiAgent surpasses ADAS (a similar auto-generated framework) by 9.9%. A case study with InfiHelper—an AI research assistant—shows end-to-end capability, generating scientific papers that received positive responses from reviewers at top IEEE venues. For enterprises, this means faster solution prototyping, scalable operationalization across diverse domains, and lower maintenance overhead as workflows shift.

Why it matters: agentic automation often fails to scale due to hand-tuned fragility. InfiAgent reframes agents as composable tools governed by rigorous audits and guided evolution—more like an operating system than a single prompt-chain.

Considerations: governance, cost control, and evaluation remain essential. But with routing, audits, and self-rewrites built-in, the framework targets long-horizon reliability rather than one-off demos.

Paper: http://arxiv.org/abs/2509.22502v1

Register: https://www.AiFeta.com

#AI #Agents #MultiAgent #Automation #Orchestration #LLM #DAG

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