Managing Long Horizon Autonomous Agent Tasks
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Key Takeaways
- Addresses core agent stability issues including goal drift, state loss, and context overflow during long-horizon operations.
- Uses a structured lifecycle: decomposing tasks, executing via ReAct loops, managing memory, and implementing a final verification and repair step.
- Achieved a state-of-the-art score of 0.821 on the AgentIF-OneDay benchmark using the GLM-5.2 backend.
- The system is designed to maintain effectiveness across diverse backend models like the GPT-5.5 medium and Qwen series.
Summary & Methodology Analysis
The OneDayAgent architecture manages long-running agent workflows by decomposing high-level user requests into bounded subtasks. These subtasks are processed through ReAct loops, a prompting strategy where the model generates both reasoning traces and task-specific actions to guide execution. This approach mitigates context overflow by compressing noisy observations and checkpointing the state of individual subtasks, ensuring the agent retains necessary information throughout the session. By explicitly managing the execution memory, the system maintains state integrity that standard single-prompt approaches often lose.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary problem this agent architecture addresses?
It addresses goal drift, state loss, and context overflow during long-horizon tasks across everyday life, work, and study.
Q2. How does the system ensure the agent meets the user's initial request?
It uses a global verification step to compare the final deliverable against the original intent, execution trace, and output, followed by a targeted repair loop if defects are found.
Q3. Does this approach work with multiple models?
Yes, the harness is designed to function across various models including GLM-5.2, Gemini-3.1-Pro-Preview, Qwen3.5-397B-A17B, Qwen3.5-9B, Qwen3.6-27B, and GPT-5.5 medium.
Q4. What specific benchmark was used to validate performance?
The paper uses the AgentIF-OneDay benchmark.
Q5. What is the current state-of-the-art score mentioned?
OneDayAgent achieved a score of 0.821 on the AgentIF-OneDay benchmark using the GLM-5.2 backend.
Q6. Are there any security or isolation features included?
No, the paper specifies that the current implementation does not include workspace isolation.
Q7. How are individual subtasks executed?
Subtasks are executed using ReAct loops, which involve iterative reasoning and action steps.
Q8. What are the limitations of the current results?
The harness results are specific to the AgentIF-OneDay benchmark and require further validation in other environments.
Q9. Does the paper discuss inference latency or cost?
The paper does not specify details regarding inference latency or computational cost.