Peter Smith
⬤ Google just dropped a major upgrade to how its AI agents work, rolling out the Agent Development Kit to fix one of the biggest problems in AI right now: bloated context. Instead of shoving everything into increasingly huge context windows, the new framework works more like a compiler, building lean, targeted context for each task. It's a practical shift that could change how production AI systems actually run.
⬤ The kit treats context as something you build, not something you dump. It separates storage from delivery—meaning information sits in structured memory, event logs, and artifact references until it's actually needed. When an agent gets called, the system assembles just the relevant pieces through a pipeline of processors. Think of it like compiling code: you don't run the entire codebase every time, you run what's necessary. Google says this makes agents handle complex workflows without drowning in irrelevant data or hitting token limits that slow everything down.
⬤ The real advantage here is speed and resource efficiency. By cutting out redundant inputs and keeping memory clean, the system delivers production-level performance without requiring models to sift through mountains of context. The framework also supports multi-agent coordination, letting different agents hand off tasks while staying in sync. Google's visuals show modular workflows with interconnected components—basically, agents talking to each other without getting lost in the noise.
⬤ This move matters because the industry has been hitting a wall with context windows. Bigger isn't always better when it slows systems down or burns through compute. Google's approach suggests the next wave of AI progress won't come from just scaling up models, but from smarter orchestration and tighter context control. For teams building real-world AI applications, this signals a shift toward engineering discipline over raw horsepower—and that could reshape how enterprise AI gets built from here on out.
Peter Smith
