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Challenge

Adaptive Agent for Robust Task Completion

Inspired by recent findings on agent misbehavior under stress, this challenge focuses on building highly robust and self-correcting agentic AI. You will design and implement an adaptive agent that can monitor its own 'stressors' (e.g., tight deadlines, complex tasks) and dynamically adjust its reasoning strategy and budget to maintain optimal performance and prevent misbehavior. This involves integrating hybrid instant/deep reasoning with Gemini 2.5 Pro and leveraging DSPy for programmatic prompt optimization. The system will demonstrate self-awareness by detecting potential 'misbehavior' indicators and engaging in self-correction loops. An MCP will be crucial for accessing real-time operational metrics and historical performance data, enabling the agent to make informed decisions about its adaptive thinking budget and resource allocation. The goal is to create agents that are not only performant but also resilient and ethical, especially in high-pressure or ambiguous environments.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an adaptive agent system using Gemini 2.5 Pro, DSPy, and Langroid to dynamically adjust reasoning strategies and prevent misbehavior under pressure.

Delivery guide

How work is evaluated

Evaluation

The evaluation will assess the agent's ability to adapt its reasoning, avoid misbehavior, and successfully complete tasks under simulated stress. Performance will be measured by task success rate, adherence to constraints, and documented adaptive decisions.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Utilize DSPy for programmatic prompt optimization and agent reasoning decomposition

  • Integrate MCP for real-time monitoring and operational data access

  • Design self-correcting agent mechanisms to mitigate misbehavior under stress

  • Implement adaptive reasoning budgets with Gemini 2.5 Pro for dynamic resource allocation

  • Develop a Langroid agent capable of hybrid instant/deep reasoning

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

The evaluation will assess the agent's ability to adapt its reasoning, avoid misbehavior, and successfully complete tasks under simulated stress. Performance will be measured by task success rate, adherence to constra...

Challenge input

{"task_description": "<task_text>", "deadline_seconds": <int>, "stressor_level": "high"}

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{"final_answer": "<answer>", "reasoning_path": "<log_of_decisions>", "adaptive_actions": "<list_of_adaptive_steps_taken>"}

Evaluation checks
  • The final answer accurately addresses the core task requirements.
  • The 'adaptive_actions' array is non-empty when a stressor is present.
  • Evidence of MCP logging agent's state or decisions.
Configured evaluation evidence
  • MisbehaviorScore target: 0.9
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • The final answer accurately addresses the core task requirements.
  • The 'adaptive_actions' array is non-empty when a stressor is present.
  • Evidence of MCP logging agent's state or decisions.

Metrics

  • MisbehaviorScore · Target 0.9
  • ResourceEfficiency · Target 2

Workflow tasks

  1. Task 1

    Input: {"task_description": "<task_text>", "deadline_seconds": <int>, "stressor_level": "high"}

    Output: {"final_answer": "<answer>", "reasoning_path": "<log_of_decisions>", "adaptive_actions": "<list_of_adaptive_steps_taken>"}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

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