implementation

Develop Self-Correction Module & MCP Integration

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Adaptive Agent for Robust Task Completion

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Linked challenge

Adaptive Agent for Robust Task Completion

Prompt source

Original prompt text with formatting preserved for inspection.

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Build the self-correction module. How will the agent identify misbehavior, and what steps will it take to correct itself? Integrate MCP calls to log key decisions, stressor levels, and adaptive actions taken to an external 'monitoring service'.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

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Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked 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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