Beyond Code Generation: Reliability, Verification, and Cost Economics in the Agentic Software Development Lifecycle

As AI coding systems evolve from completion tools to intelligent agents with autonomous execution capabilities, they demonstrate significant abilities in tasks such as checking repositories, editing multiple files, running tools, and submitting pull requests. However, recent research indicates that the benefits of code generation and delivering reliable software are declining, and stages such as review, integration, testing, security, and operation still pose major bottlenecks. The economic model is shifting from traditional seat-based licensing to dynamic billing based on tokens, tool calls, sandbox environments, continuous integration (CI), and rework costs. Based on software engineering research, benchmark audits, and production reports from 2024 to September 2026, the current software development lifecycle of intelligent agents faces a throughput paradox, and it is necessary to establish a control plane that includes verification taxes, production qualified changes (PQC), and constraints based on cost and reliability.