Agentic Compute

Agentic Compute describes the full execution load created when AI agents do more than generate a single answer and instead carry out multi-step work on their own. That load includes model calls, tool calling, browser or API access, code execution, memory reads and writes, retries, and long-running sessions. The term matters because cost and operational risk behave differently for agents than for standard chat interactions. In a normal chat workflow, usage scales mostly with prompt and completion tokens. In agentic compute, it also scales with step count, concurrency, tool usage, loops, tracing, and safety controls. A coding agent that reads files, runs tests, checks logs, and iterates through fixes can consume far more resources than a one-shot model response. For architecture and pricing, that means teams cannot look at token prices alone. They need workflow budgets, runtime limits, concurrency caps, observability, stop conditions, and human approval gates. Agentic Compute is therefore best understood as an operating model for autonomous AI systems, not just as a model-performance metric.