Beyond RAG: Simulating the Future with MiroFish
This article explores MiroFish, a tool that goes beyond traditional knowledge retrieval systems (like RAG) by simulating multi-agent interactions and modeling possible future outcomes over time.
Why it matters
This simulation-based approach opens up new possibilities for organizations to test and prepare for a variety of future scenarios in a more dynamic and iterative way.
Key Points
- 1MiroFish creates a virtual environment with multiple agents (e.g., employees, managers) and simulates their interactions
- 2It produces a temporal report showing the day-by-day evolution of the scenario, rather than a static answer
- 3This approach can be useful for testing AI adoption strategies, product feature rollouts, business decisions, and macro scenarios
- 4The author sees this as a shift from 'answering questions' to 'rehearsing decisions'
Details
Unlike traditional knowledge retrieval systems, MiroFish allows users to simulate scenarios and observe how they evolve over time. It creates a virtual environment with multiple agents (e.g., employees, managers) and models their interactions. The tool then produces a temporal report showing the day-by-day changes, rather than a single static answer. This approach can be useful for testing a wide range of scenarios, from AI adoption strategies and product feature rollouts to business decisions and macro-level events. The author sees this as a shift from simply 'answering questions' to 'rehearsing decisions' - a new way of thinking about AI systems that goes beyond just retrieving and explaining existing knowledge.
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