Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios

TL;DR

Jenius-Agent is a system-level framework that enhances real-world agent performance by integrating adaptive prompts, context-aware tool use, and layered memory, enabling better robustness, observability, and systematic failure analysis.

Contribution

The paper introduces Jenius-Agent, a novel framework that improves agent robustness and observability in real-world scenarios through system design and evaluation methodology.

Findings

Up to 35% improvement in task completion rate.

Reduced token consumption and response latency.

Fewer tool invocation failures.

Abstract

As agent systems powered by large language models (LLMs) advance, improving performance in context understanding, tool usage, and long-horizon execution has become critical. However, existing agent frameworks and benchmarks provide limited visibility into execution-level behavior, making failures in tool invocation, state tracking, and context management difficult to diagnose. This paper presents Jenius-Agent, a system-level agent framework grounded in real-world deployment experience. It integrates adaptive prompt generation, context-aware tool orchestration, and layered memory mechanism to stabilize execution and improve robustness in long-horizon, tool-augmented tasks. Beyond system design, we introduce an evaluation methodology that jointly measures procedural fidelity, semantic correctness, and efficiency. This framework makes agent behavior observable as a structured execution process and enables systematic analysis of failure modes not captured by output-only metrics. Experiments on Jenius-bench show substantial improvements in task completion rate, with up to a 35 percent relative gain over the base agent, along with reduced token consumption, response latency, and tool invocation failures. The framework is already deployed in Jenius ({https://www.jenius.cn}), providing a lightweight and scalable solution for robust, protocol-compatible autonomous agents.