I designed and engineered a hierarchical multi-agent orchestration framework for coordinating complex AI-assisted engineering and analytical work.

A central orchestrator assigns bounded tasks to specialized builder, analyst, reviewer, and verification agents. Execution follows an explicit dependency graph, while shared evidence, acceptance gates, negative-path testing, and checkpointed state prevent agents from declaring work complete without proof.

The framework supports targeted retries, rollback, and recovery from the first incomplete node instead of restarting an entire workflow. It also separates implementation from independent review, reducing the risk of an agent validating its own unsupported conclusions.

This independent engineering project demonstrates how agentic systems can be made more controlled, observable, and recoverable. The architecture and control interface are representative views of the framework rather than screenshots containing private execution data.