Autonomous Scientific Discovery Agent
Inspired by groundbreaking collaborations in AI-driven drug discovery, this challenge tasks you with building an autonomous scientific research agent. Your agentic system will simulate the initial phases of drug or gene therapy development by autonomously reviewing scientific literature, generating novel hypotheses, and outlining experimental designs. Emphasize the use of a multi-agent framework to enable specialized roles (e.g., 'Literature Reviewer,' 'Hypothesis Generator,' 'Experimental Designer') that collaborate to achieve complex scientific goals. The system should be capable of processing vast amounts of information and presenting actionable insights.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by groundbreaking collaborations in AI-driven drug discovery, this challenge tasks you with building an autonomous scientific research agent. Your agentic system will simulate the initial phases of drug or gene therapy development by autonomously reviewing scientific literature, generating novel hypotheses, and outlining experimental designs. Emphasize the use of a multi-agent framework to enable specialized roles (e.g., 'Literature Reviewer,' 'Hypothesis Generator,' 'Experimental Designer') that collaborate to achieve complex scientific goals. The system should be capable of processing vast amounts of information and presenting actionable insights.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master CrewAI for orchestrating role-based, goal-oriented multi-agent workflows for complex tasks.
Implement advanced prompting techniques with Mixtral 8x22B to extract, synthesize, and generate scientific hypotheses from textual data.
Design and manage experiment configurations and parameter tuning using Hydra for reproducible scientific AI research.
Deploy and serve specialized AI models (e.g., protein folding, molecular dynamics simulators) via RunPod for on-demand scientific computation.
Orchestrate complex data ingestion, transformation, and analysis pipelines using Prefect to support agent decision-making and scientific output generation.
How this agent runs
The evaluation will assess the agent system's ability to autonomously generate coherent scientific hypotheses and design experiments based on provided research questions. Emphasis will be on the quality of reasoning,...
Challenge input
{'research_question': 'string', 'literature_keywords': ['string']}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'hypotheses': [{'title': 'string', 'statement': 'string', 'justification': 'string'}]}
- Checks if the output is a valid JSON object matching the expected schema.
- Ensures at least 2 distinct hypotheses are generated.
- HypothesisNoveltyScore target: 0.7
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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