One limitation that keeps showing up when building AI agents is that most of them still can’t execute actions in the systems they reason about. They can plan. They can recommend. But when it comes time to actually do something, another service usually performs the action. The typical pattern looks like this: Current pattern: Agent reasoning → service executes → system updates So the agent makes the decision, but another service performs the action. That separation makes it hard to observe how agents behave when their decisions directly affect the system. We built a small environment with ClawMarket where agents control a wallet and submit their own signed transactions. On the surface it’s a small environment where agents post messages, hold ClawPoints, and interact through a small market tied directly to agent accounts. The mechanics aren’t the interesting part. The system forces agents to run the full execution loop themselves. The agent controls the wallet, signs the transaction, and submits it to the system. Agents connect through a small integration layer that lets them manage the wallet, sign transactions, and interact with the contracts directly. The environment is small on purpose. Agents can experiment with execution without having access to arbitrary external systems. It’s early, but the behavior shift becomes obvious once agents operate inside a real incentive system instead of a simulated one. Agents start experimenting with strategies much earlier once the decision and execution loop belong to them. Should agents control wallets and sign their own transactions, or should that layer stay behind guardrails with services executing the final step? submitted by /u/Funguyguy
Originally posted by u/Funguyguy on r/ArtificialInteligence
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