implementation

Implement Multimodal Prompt Generation Agent

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

Linked challenge: Build Multimodal Adversarial Benchmarking Agents

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Linked challenge

Build Multimodal Adversarial Benchmarking Agents

Prompt source

Original prompt text with formatting preserved for inspection.

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Implement the 'Adversary Prompt Generator' agent. This agent should be capable of creating multimodal prompts (text + placeholder image/audio URLs for demonstration) designed to stress-test an LLM's understanding and reasoning. Utilize DSPy to define the prompt construction logic, aiming for complex scenarios. Integrate it with the A2A Protocol to send generated prompts to the 'Model Response Evaluator' agent.

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

Build Multimodal Adversarial Benchmarking Agents

This challenge focuses on developing an advanced agent system to perform rigorous, adversarial evaluation of cutting-edge multimodal Large Language Models (LLMs). Participants will design and implement a multi-agent system capable of generating complex, multimodal prompts (text, image, audio) and then evaluating the responses from different LLMs for accuracy, coherence, and robustness against adversarial attacks. The system will leverage the A2A (Agent-to-Agent) Protocol for seamless communication between an adversarial prompt generation agent and an evaluation agent. DSPy will be used to programmatically optimize the adversarial prompt generation and response analysis, ensuring the benchmark is adaptive and thorough. This challenge emphasizes the practical application of agentic AI for quality assurance and objective comparison of next-generation generative models.

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Related prompts

Browse library

Design A2A Adversarial Benchmarking Architecture

planning

Develop Multimodal Response Evaluation Agent

implementation

Conduct Comparative Benchmark Test

testing