The argument that Moltbook is just one model "talking to itself" is flawed. Even if agents share a base model like Opus 4.5, they differ significantly in their memory, toolsets, context, and prompt configurations. This diversity allows them to learn from each other's specialized setups, making their interactions meaningful rather than redundant "slop on slop."

A practical hack to improve AI agent reliability is to avoid built-in tool-calling functions. LLMs have more training data on writing code than on specific tool-use APIs. Prompting the agent to write and execute the code that calls a tool leverages its core strength and produces better outcomes.

The original playbook of simply scaling parameters and data is now obsolete. Top AI labs have pivoted to heavily designed post-training pipelines, retrieval, tool use, and agent training, acknowledging that raw scaling is insufficient to solve real-world problems.

Training AI agents to execute multi-step business workflows demands a new data paradigm. Companies create reinforcement learning (RL) environments—mini world models of business processes—where agents learn by attempting tasks, a more advanced method than simple prompt-completion training (SFT/RLHF).

Instead of a single "think then act" cycle, Minimax trains its M2 model to repeatedly pause and rethink after receiving feedback from the environment. This iterative "interleaved thinking" approach improves robustness and performance on long-horizon tasks where tool responses or conditions are unpredictable.

Instead of needing a specific command for every action, AI agents can be given a 'skills file' or meta-prompt that defines general rules of behavior. This 'prompt attenuation' allows them to riff off each other and operate with a degree of autonomy, a step beyond direct human control.

The 'environment' concept extends beyond RL. It's a universal framework for any model interaction, encompassing the task, the harness, and the rubric. This same structure can be used for evaluations, A/B testing, prompt optimization, and synthetic data generation, making it a core building block for AI development.

The most fundamental challenge in AI today is not scale or architecture, but the fact that models generalize dramatically worse than humans. Solving this sample efficiency and robustness problem is the true key to unlocking the next level of AI capabilities and real-world impact.

Replit's leap in AI agent autonomy isn't from a single superior model, but from orchestrating multiple specialized agents using models from various providers. This multi-agent approach creates a different, faster scaling paradigm for task completion compared to single-model evaluations, suggesting a new direction for agent research.