What the Success of Coding Agents Teaches Us about AI Systems in General
8 days ago
- #neural networks
- #software architecture
- #AI coding agents
- AI coding agents significantly reduce the time required for software development tasks, from weeks to days or even hours.
- AI-native software systems are learned rather than designed, with code as policy, deployment as episodes, and bug reports as reward signals.
- Neural networks excel at tasks requiring judgment, while traditional software is better suited for executing explicit instructions.
- Many agentic AI projects fail due to issues like agentic drift, opaque debugging, and brittle autonomy, whereas Claude Code succeeds by producing durable, version-controlled, and deterministic artifacts.
- A better architecture delegates judgment to neural networks and execution to traditional software, maintaining determinism, auditability, and precision.
- AI agents can accelerate buildtime by writing code, blurring the line between development and runtime, leading to adaptable software systems.
- Neural networks should be used for runtime judgment and buildtime acceleration, not to replace traditional software.