agentic-engineering

Multi-agent orchestration works by spawning specialized named agents—architect, engineer, skeptic, and qa-engineer—to plan, implement, review, and verify software changes through a structured sequence. This reduces coordination overhead while increasing outcome quality across development sessions.

Adversarial review in AI-driven engineering is a risk governance mechanism where a dedicated Skeptic agent challenges proposed software changes. It is needed when performing risk classification to ensure safe shipping and verify that quality gates are met before delivery.

You coordinate multiple AI agents by triggering an activation preflight to start a session, prompting the architect to draft a plan, and then spawning an engineer to implement the change. The workflow generates briefs, plans, and telemetry artifacts for traceability.

Yes, you can resume an AI engineering workflow across different sessions. The orchestration supports cross-session traceability by generating persistent artifacts like briefs, plans, and telemetry, enabling loop resumes and continuous auditability throughout the software delivery process.

The best way to ensure safe software changes during automated AI development is to enforce risk-aware governance controls. This approach uses risk classification, adversarial Skeptic reviews, and quality gates to verify correctness and prevent unsafe modifications before shipping.

No specific external dependencies are required to run this multi-agent software engineering workflow. It operates autonomously using internal scripts and references to manage the orchestration sequence, artifact generation, and quality-gate enforcement for your development tasks.