agentic-engineering

Agentic engineering workflows orchestrate AI agents by enforcing eval-first execution, breaking goals into 15-minute verifiable units, and matching task complexity to appropriate model tiers like Haiku, Sonnet, or Opus.

Decomposing software development goals involves breaking large engineering tasks into 15-minute, independently verifiable units to satisfy strict requirements for verifiable completion criteria and regression testing.

Eval-first execution in AI code generation ensures every task is measured against baseline and regression evaluations before and after implementation to maintain high-quality output and rigorous risk management.

Optimizing model routing costs for software development lifecycles involves matching specific task complexities to the appropriate model tier, such as Haiku, Sonnet, or Opus, balancing performance and expenditure.

Agentic engineering works for multi-step tasks by satisfying explicit requirements for verifiable completion criteria and regression testing, ensuring high-quality code generation across complex software development lifecycles.

Task decomposition for AI agents may not suit simple, single-step engineering goals that lack strict requirements for verifiable completion criteria, baseline evaluations, and rigorous risk management.