agentic-engineering-workflow

An agentic software engineering workflow is a structured, end-to-end process for building software with AI agents. It maintains human control by applying tight context management, iterative review loops, and security guardrails to reduce drift and misalignment.

To build an MVP with AI agents, you select a core execution harness, saturate context discipline, and implement iterative reviews. The workflow ensures you build a smallest working feature, perform a cleanup pass, and execute verification steps before release.

Context management prevents AI coding drift by constraining the execution environment and enforcing tight feedback loops. This discipline keeps the AI agent aligned with human intent, ensuring iterative reviews and security guardrails catch misalignment early.

You need an AI harness for the agentic engineering workflow, but it is not strictly predefined. The workflow's first step is to identify the strongest AI harness available and establish it as your core execution environment for reliable coding sessions.

The best way to structure iterative reviews for AI-assisted software development is to break the work into small, reviewable units. The workflow includes a cleanup pass and built-in verification steps before release to ensure security guardrails and risk mitigation are maintained.

You should reconsider an agentic workflow if your project lacks clear scope for a smallest working feature or cannot support iterative reviews. Without strict context discipline and security guardrails, the risk of AI drift and misalignment in the development process increases significantly.