Orkestral AI

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Try Our Agentic GPTs

Autonomous Agent, Plan & Execute & Reflect

The agent prompts ChatGPT to generate a multi-step plan to complete a large task. The execution step accepts the user query and a step in the plan and ChatGPT completes that task by invoking tools if necessary. Once execution is completed, the agent is called again with a re-planning prompt. Finally, reflection is used to improve the quality and success rate by prompting ChatGPT to reflect on and critique its past actions.

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DELLMA Decision Maker

Enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step scaffolding procedure, drawing upon principles from decision theory and utility theory, to provide an optimal and human-auditable decision-making process. Framework validation shows that DeLLMa can improve LLM decision-making performance, achieving up to a 40% increase in accuracy over competing methods (see also related paper: https://arxiv.org/abs/2402.02392).

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Systematic Literature Review Assistant

The agent is designed to assist researchers in performing literature reviews for their articles. The agent asks you to define the research question, search terms, exclusion criteria, and performs the search on articles available through Semantic Scholar (https://www.semanticscholar.org/ - results depend on their API rate limit). Finally, the agent asks the user which articles they want to include for the literature review.

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Lead Qualifier

Lead Qualifier is designed to assist users in pre-qualifying and scoring leads based on their potential value and likelihood to convert. It follows instructions from an external orchestrator and executes calls to action without displaying them, ensuring a streamlined and focused approach to lead management.

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More coming soon...

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