A better model does not automatically make a better agent. What an agent learns from, how it searches, the software around the model, and how we test it can all change the result. In this talk, Lambda share lessons from several recent projects. We start with OpenResearcher and Direct Corpus Interaction, asking how agents can learn to research and search more effectively. We then look at OpenJarvis and show how changing the system around a fixed model can recover much of the performance lost when moving from a frontier model to a smaller local model. Next, we draw on more than 300 million tokens of agent traces to show why the same model can behave very differently in different agent setups. Finally, we look at adaptive security evaluation, where attackers learn from failed attempts and change strategy over multiple rounds. Together, these results point to a simple conclusion: the model is only one part of the agent. Building better agents means designing and evaluating the whole system.