Astronomy has always relied on sharp minds to connect the dots between observations and the physics of the cosmos. Imagine having to explain the light from a distant galaxy, a faint smudge in an image, using only its color as measured through a few filters. This is the puzzle astronomers tackle when interpreting spectral energy distributions (SEDs). Now, a new AI agent called "mephisto" is learning to play the same game. Mephisto, a multi-agent collaboration framework, tackles this challenge by mimicking the reasoning process of human astronomers. The AI agent uses a technique similar to a scientist formulating hypotheses. It interacts with an existing codebase called CIGALE, proposing different models to explain the observed light from galaxies. It even learns from its mistakes, refining its understanding of galaxy physics through a continuous feedback loop. The researchers tested mephisto on real data from the James Webb Space Telescope (JWST), including a recently discovered population of galaxies called "Little Red Dots." These galaxies, only visible with JWST’s infrared vision, are so faint and distant that their nature remains a mystery. Remarkably, mephisto matched human-level reasoning about these enigmatic galaxies, proposing two likely scenarios: they could be either dusty star-forming galaxies or galaxies with active supermassive black holes. This AI-driven approach is a giant leap towards streamlining complex astronomical analyses, particularly as new telescopes like JWST generate mountains of data ripe for exploration. While the computational cost of running mephisto is currently a limitation, the potential for automated analysis of billions of galaxies opens exciting doors for future discoveries. Mephisto's clever ability to combine existing knowledge with its learning process represents a powerful new tool for astronomers, paving the way towards unraveling the universe's mysteries on an unprecedented scale.