2.3 Characteristics of AI Coding, Agents, and High-Frequency Scenarios

AI Coding (code generation, testing, review) and multi-agent coordination tasks share several common traits:

• High frequency and multi-turn interactions:Continuous dialogues and iterative generation trigger a large number of inference calls. McKinsey’s 2025 Global AI Survey indicates that 62% of surveyed organizations are experimenting with AI agents, and curiosity-driven adoption leads to far higher call frequencies than traditional applications.

• Strong context dependence:Maintaining long-term session states and project-level context is essential. According to Index.dev, 85% of organizations have adopted AI agents in at least one workflow, highlighting the critical role of contextual continuity for efficiency.

• High repetitiveness:Large portions of logic or stylistic patterns can be reused, yet current systems often recompute them. The Anthropic Economic Index reports that in lower-adoption countries (e.g., India), over 50% of AI usage focuses on coding tasks, underscoring the optimization potential in repetitive computation.

These traits make efficiency and cost optimization in the execution phase more urgent than model improvement itself. Research by METR shows that although AI tools improved developer productivity in early 2025, without optimization, actual completion times may extend by up to 19%, further underscoring the pain points in high-frequency AI usage scenarios.