agentic-engineering

Eval-first execution ensures AI-assisted engineering tasks are completed against predefined criteria. It integrates thorough evaluations and regressions directly into the workflow to maintain quality and risk control before finalizing code.

You decompose complex software development tasks by breaking them into manageable units. This process ensures clear verification boundaries and targeted risk management for each specific AI engineering session.

Yes, you can dynamically route AI model tiers based on task complexity. Model routing assigns tasks to different AI models to optimize execution speed and accuracy across diverse engineering workflows.

Session management for AI engineering workflows manages session lifecycles effectively. It maintains consistency and control throughout task decomposition and model routing operations during complex software development.

The best way to focus AI-generated code reviews is to direct them toward critical aspects. Directing the review focus ensures AI agents evaluate invariants, error handling, and security vulnerabilities efficiently.

You should not use agentic workflows for software development when tasks lack clear verification criteria. Without predefined evaluation criteria, task decomposition and eval-first execution cannot effectively manage risk or ensure code quality.