I built the Baza Dashboard, a custom React control center to manage, optimize, and orchestrate a fleet of specialized AI agents using BPMN workflow models. As a solo operator, this infrastructure delivers agency-level operational throughput by directing a coordinated digital workforce.
Scaling personal throughput across executive consulting, operations, and design requires more hours than exist in a day. The traditional answer is to hire more headcount. My answer was to engineer a command center for managing AI agents as a coordinated, accountable digital team.
The Baza Dashboard is a custom-built React application that tracks active workflows, transcript logs, and agent alignment across multiple specialized AI personas. It uses formal BPMN workflow models to orchestrate complex multi-step processes, with a Python daemon handling background tasks and agent coordination. The result is extreme operational leverage: agency-level output from a solo operator.
Not just using AI tools. Building the operational management layer for AI agents.
Three core architectural modules engineered to direct, monitor, and coordinate autonomous agents.
The main dashboard interface displays active workflows, real-time agent status, transcript logs, and BPMN process state. Each AI persona features tracked alignment metrics, task queues, and output quality indicators, enabling live supervision of a distributed digital workforce.
Complex multi-step processes are modeled as formal BPMN diagrams with defined inputs, decision gates, parallel execution branches, and agent assignments. Workflows can be paused, inspected, and debugged directly from the visual interface.
Specialized AI personas operate as a coordinated team, each with defined domain boundaries, system instructions, and structured output schemas. The system tracks persona performance and manages handoffs between research, strategy, and execution agents.
Each AI agent receives a specialized role, system instructions, and defined output schemas aligned to a specific domain.
Complex processes are mapped as BPMN diagrams with decision gates, parallel branches, and agent handoff points.
The dashboard dispatches tasks to the right agent, monitors execution, and manages coordination between personas.
Agent outputs are tracked against quality standards. Drift detection flags when an agent requires prompt recalibration.
Completed workflows produce agency-quality deliverables: strategic frameworks, code architectures, and operational documentation.
The modern operational challenge is not adopting another disconnected AI tool. It is engineering the governance, coordination, and visibility layers required to manage multiple AI systems as an accountable, aligned workforce.
By designing and building the Baza Dashboard, I demonstrated that an executive operator can build custom software that multiplies personal output by an order of magnitude. This architecture confirms that I do not merely consume AI, I architect the operating systems that make autonomous agents reliable at scale.
I bring an Olympic athletic background to fuel the relentless discipline required to execute, a deep research background focused on hyper-targeted strategy, and the grit of the immigrant experience. I rely on hard analytics and behavioral strategy to build resilient operational systems that help businesses actually run.