DeepAgents excels with open-weights GLM-5.2

While agent harnesses are typically optimized for proprietary frontier models, DeepAgents proves that open-weights alternatives like GLM-5.2 are now viable engines for long-horizon planning tasks when properly tuned.

• –Harness profiles under the hood enable model-specific tuning of system prompts, tools, and memory management, mitigating the drop-off in agent performance when swapping out closed APIs.
• –GLM-5.2's massive 753B-parameter Mixture-of-Experts (MoE) architecture and 1M-token context window provide the reasoning depth required for DeepAgents' multi-step execution loop.
• –This combination lowers the barrier to hosting local, private coding agents, reducing dependency on OpenAI or Anthropic for enterprise environments.