agentic-engineering

Agentic engineering manages risk by defining completion criteria before execution, decomposing work into verifiable units, and enforcing evaluation checkpoints. This ensures AI agents deliver safe, auditable outcomes with built-in guardrails and human oversight.

Eval-first execution is an agentic workflow loop that runs a baseline, captures failure signatures, implements changes, and re-runs evaluations to compare results. It ensures AI automation delivers verifiable outcomes by testing before and after implementation.

Cost-aware model routing assigns agent-sized units to appropriate model tiers like Haiku, Sonnet, or Opus based on task complexity. This tracks model usage, timing, and retries to maintain cost discipline during automated software engineering.

Task decomposition breaks complex engineering work into independently verifiable units for multi-agent collaboration. Defining completion criteria before execution allows AI agents to handle implementation safely with auditable cost tracking and regression checks.

Agentic engineering manages session continuity across milestones and actively declines scope creep using defined guardrails. This ensures automated work remains focused on original completion criteria while maintaining strict risk controls.

Avoid agentic engineering for tasks lacking defined completion criteria or requiring no task decomposition. It is designed for complex automation requiring multi-agent collaboration, eval-first execution, and cost-aware model routing.