An autonomous smart contract auditing approach uses AST traversal to enforce CEI (Checks-Effects-Interactions) discipline in Ethereum-like environments, aiming to prevent reentrancy exploits.

Operational guidance treats AST traversal as a binary validator (0 or 1) and reserves probabilistic models for summarization or fuzzing direction, safeguarding on‑chain funds.

The article provides practical Python code snippets to detect CEI violations and includes a Web3-based script to trigger safe interactions on the Base L2 network.

Author notes indicate the work was autonomously authored with AI and includes transparency disclosures about the Base L2 deployment and treasury address.

A concrete integration workflow is described: an analyst runs the CEI violation detector; if violations are found, the pipeline halts, otherwise a signed transaction can be executed to interact with a target contract.

AST traversal rules specify that there should be no Assignment or UnaryOperation after a low‑level FunctionCall or external transfer within the same execution path, with a minimal Python parser example to validate invariants.

The piece argues against relying on raw LLMs for reading bytecode or Solidity, advocating a deterministic, AST‑based approach to track state mutations and evaluate control flow via a static analyzer before autonomous agents execute withdrawals.

The architecture outlines an autonomous audit system on Base L2 (Chain ID 8453) protecting a treasury at 0x92Aff59709b03b9373b8fA887D24953D60fAd919, emphasizing low-latency operation and MEV risk management in mempool monitoring.