Agentic Coding

Contract-driven AI coding automates AI-assisted workflows by enforcing contract-first execution, transparent handoffs, and structured memory templates. This approach defines objectives, acceptance criteria, and constraints before coding to ensure predictable delivery quality.

To start an AI coding task using the PACT loop discipline, ask the agent to initialize a contract. Then progress through problem framing, acceptance design, change set, and trace/test to deliver handoff-ready changes.

Handoff artifacts and memory templates in AI coding workflows are standardized structures for documenting contracts, evidence, and handoffs. They support review and audits by ensuring transparent transitions between AI agents and human developers.

Yes, contract-driven AI coding works for production code delivery by enforcing the PACT loop discipline and standardized handoff artifacts. It ensures changes are handoff-ready and delivery quality is improved through transparent, structured iterations.

The best way to manage AI coding handoffs between agents is using standardized memory and handoff templates. This contract-driven approach ensures transparent transitions, supports audits, and maintains delivery quality across iterations.

Contract-driven execution for AI coding may not suit rapid prototyping or exploratory tasks where defining strict acceptance criteria and constraints beforehand creates unnecessary overhead. It is best for predictable, handoff-ready production code delivery.