Agents need workstations, not just models
The shift from LLM inference to autonomous agents demands purpose-built development environments, not just better models.
We’ve been building agents wrong. The industry keeps obsessing over model capabilities while ignoring the environments these agents need to actually work. It’s like trying to hire a developer but only caring about their IQ while giving them a broken laptop and no internet connection.
The environment problem
Most agent frameworks are just fancy prompt chains with APIs bolted on. They can’t remember what happened yesterday. They can’t learn from mistakes. They restart fresh every time like digital goldfish. Real work requires persistence, shared context, and the ability to pick up where you left off.
The smart teams are realising this. They’re building agent workstations with proper memory systems, tool persistence, and multi-agent coordination. These aren’t just better prompts, they’re operating environments designed for autonomous work.
Beyond the inference trap
The model is the easy part now. Getting decent reasoning from an LLM is solved. The hard part is everything else: managing long-running tasks, coordinating between agents, handling failures gracefully, maintaining context across sessions.
We need to stop thinking about agents as smart chatbots and start thinking about them as digital employees. Employees need desks, tools, and workflows. They need to collaborate and hand off work to each other.
The future of AI isn’t better models. It’s better workplaces for those models to operate in.