AI tools have made it easy to vibe code agents. Building and maintaining an enterprise agent in production, however, remains significantly more challenging. In particular, developers are often forced to make a trade-off between quality (getting good answers, especially on long-tail tasks) and efficiency (keeping cost and latency in check at scale). Oftentimes, techniques bring gains in one dimension at the cost of another.

Memory is a promising way to improve both at the same time. It can help agents carry useful context across tasks, such as user preferences, prior corrections, metric definitions, and successful workflows. In principle, this can improve accuracy, reduce repeated errors, and avoid unnecessary rediscovery. But more history is not necessarily better memory. Supplying every past interaction is costly and can introduce stale, conflicting, or irrelevant context. The central challenge is deciding what to retain, how to maintain it, and how to measure usefulness.

In this post, we introduceMemCurrent, a persistent memory architecture for production agents, which now powers the newAgent Bricks Memory andGenie. Agents read and write memory as they work, and an offline “dreaming” pipeline consolidates memory to keep it consistent and fresh. On our internal benchmark, built from realistic enterprise tasks, MemCurrent triples the agent's pass rate over having no memory, while using 31% fewer tokens and running 26% faster. The rest of this post covers how we designed and evaluated it, and what we learned along the way.