Perplexity shipped Portable Computer this week, the local-first version of its Computer agent running on an Nvidia DGX Spark workstation, and the bill is steep: A DGX Spark starts at $4,700, and even an aging 24GB RTX 3090 sells well above $1,500.The architectural choice beneath the surface deserves as much attention as the price tag: Most platforms building agents address reliability with greater intelligence. That typically includes a larger orchestrator model, a planner model, or a critic model reviewing the work. Probabilistic reasoning proposes the next action, and deterministic software decides whether to execute it.Perplexity separated the two jobs instead of stacking them. Probabilistic reasoning proposes the next action, and deterministic software decides whether to execute it.The loop controller is code, and the decisions are still a modelPerplexity uses the term “orchestrator” to refer to the runtime controller rather than the planning model. That controller assembles context, enforces policy, and executes approved tool calls inside an OS-level sandbox. The company says it is deterministic code, not yet another model. The local model proposes the next action, including which tool to call and when to ask a cloud advisor for help. The split resembles a control plane architecture, where reasoning suggests, and inspectable software retains authority. Nate Kupp, Perplexity’s vice president of Computer Enterprise and Infrastructure, tells The New Stack that the harness accounted for most of the engineering work.Same weights, better scoresPerplexity kept the base model and the silicon constant, putting Qwen3.8-27B on the same DGX Spark across three agent stacks. On its internal Local Knowledge Work Bench, a held-out set of 53 tasks, the company reported 82.6% for Computer. Pi scored 77.6% and Hermes 74%.On ParseBench-100, a subset covering charts, layouts, tables, and formatting, the gap widened considerably. Perplexity reported 65.1% for Computer, while Hermes reported 34.6% and Pi reported 13.9%.Because the base weights were constant, the gap measures the system around the model rather than a better model. That does not mean weights stopped mattering. Perplexity post-trained Qwen into PPLX 27B and reported 85.4%, above its own base-model number. Harness engineering and post-training are significant factors of this approach.The word harness also covers a lot of ground, including prompts, tool schemas, context management, verification hooks, and document processing. Orchestration code is one part of that surface.The security boundary lives outside the modelThe sandbox is the boundary, not the determinism. Perplexity says the sandbox restricts processes, filesystem paths, and network access. If the sandbox is unavailable, the harness disables itself before making any tool call. Deterministic code is valuable here because it makes policy easy to inspect and helps the system fail safely. Deterministic code can still ship a vulnerability or faithfully execute a permitted mistake.The key distinction is not between model orchestration and code orchestration. It is whether permission is enforced by something other than asking a model in plain English to comply.The key distinction is not between model orchestration and code orchestration. It is whether permission is enforced by something other than asking a model in plain English to comply.The same discipline shapes how context gets spent. Perplexity reports that Qwen3.8-27B advertises a 260,000-token context window but begins to struggle beyond 100,000 tokens. The harness therefore keeps the core prompt and toolset small and loads skills on demand. Commonly used connectors became command-line tools rather than full Model Context Protocol definitions sitting permanently in context.Deterministic execution cannot rescue reasoning that exceeds the local model. On Terminal Bench 2.1, Perplexity reported 59.6% running locally. Letting the local agent consult Claude Opus 5 raised it to 73.0%, compared with 82.4% when Opus 5 worked alone. All of this remains vendor-reported evidence on a bench that the company has yet to open-source.For enterprises evaluating local agents, the component to scrutinize is the layer that grants and denies authority, because that is where the platform’s engineering is most evident.The post Perplexity just separated reasoning from authority. Here’s why it matters for enterprises. appeared first on The New Stack.