--- title: "Baza Multi-Agent Fleet Control Center" author: "Kamilla Gafurzianova" category: "AI Systems & Multi-Agent Operations" kicker: "Baza Multi-Agent Fleet Control Center" headline: "Orchestrating an AI agent fleet from a custom control center." summary: "Custom React control center managing, optimizing, and orchestrating a fleet of specialized AI agents using BPMN workflow models to achieve agency-level operational throughput." tools: - "React.js" - "BPMN.io" - "Python Daemon" - "Antigravity CLI" - "Multi-Agent Fleets" metrics: - label: "Output Level" value: "Agency-Scale" detail: "Solo operator producing the throughput of a full agency through multi-agent orchestration." - label: "Workflow Models" value: "BPMN 2.0" detail: "Formal Business Process Model and Notation for deterministic task routing." - label: "Frontend Architecture" value: "React Command Center" detail: "Custom-built control plane with real-time agent tracking and workflow inspection." - label: "Coordination Backend" value: "Python Daemon" detail: "Asynchronous background process managing task execution, transcripts, and handoffs." ogImage: "https://kamillek.com/og/og-case-study-baza.png" canonicalUrl: "https://kamillek.com/case-study-baza.html" contact: "https://kamillek.com/contact.html#inquiry-form" --- # Orchestrating an AI agent fleet from a custom control center. **AI Systems & Multi-Agent Operations** *By Kamilla Gafurzianova · Systems Architecture & Executive Operations* 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. --- ## Strategic Leadership Context: The AI-Native Operator Who Builds the Infrastructure 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." ### Key Operational Telemetry * **Output Level:** Agency-Scale (Solo operator producing the throughput of a full agency through multi-agent orchestration). * **Workflow Models:** BPMN 2.0 (Formal Business Process Model and Notation for deterministic task routing). * **Frontend Architecture:** React Command Center (Custom-built control plane with real-time agent tracking and workflow inspection). * **Coordination Backend:** Python Daemon (Asynchronous background process managing task execution, transcripts, and handoffs). --- ## Architecture & Core Deliverables Three core architectural modules engineered to direct, monitor, and coordinate autonomous agents. ### 1. Baza Dashboard Command Center * **Discipline:** Control Plane * **Scope:** 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. * **Core Capabilities:** * Real-time agent status and queue telemetry * Unified transcript logging and inspection * Drift detection and prompt calibration controls ### 2. BPMN Workflow Orchestration Engine * **Discipline:** Process Modeling * **Scope:** 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. * **Core Capabilities:** * Deterministic decision gates and branching logic * Automated agent-to-agent task handoffs * Visual process monitoring and state persistence ### 3. Specialized Multi-Agent Fleet Architecture * **Discipline:** Agent Personas * **Scope:** 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. * **Core Capabilities:** * Domain-isolated agent roles and system boundaries * Standardized inter-agent message protocols * Output validation and quality benchmarking ### Strategic Tools * **React.js:** Control center frontend & reactive UI state * **BPMN.io:** Visual process modeling & workflow orchestration * **Python Daemon:** Background task execution & queue coordination * **Antigravity CLI:** Autonomous agent execution & runtime harness --- ## Operational Workflow Pipeline 1. **Step 01 · Define Personas:** Each AI agent receives a specialized role, system instructions, and defined output schemas aligned to a specific domain. 2. **Step 02 · Model Workflows:** Complex processes are mapped as BPMN diagrams with decision gates, parallel branches, and agent handoff points. 3. **Step 03 · Orchestrate Fleet:** The dashboard dispatches tasks to the right agent, monitors execution, and manages coordination between personas. 4. **Step 04 · Track Alignment:** Agent outputs are tracked against quality standards. Drift detection flags when an agent requires prompt recalibration. 5. **Step 05 · Ship Deliverables:** Completed workflows produce agency-quality deliverables: strategic frameworks, code architectures, and operational documentation. --- ## Strategic Executive Takeaway: The Future of Operations Is Orchestrating Intelligence 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. --- ## Certified Digital Expertise & The Discipline to Scale * Salesforce Certified Administrator * Salesforce Certified Business Analyst * Radical Product Thinking Vision Setting * Google AI Professional Certificate * Google AI for Data Analysis * Project Management Institute Member * Product Management Basics Certification * AI for Product Management 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. --- ## Omnidirectional Navigation (GLBL-PRTCL-036 §3.4) * [← Previous Case: ArtDesignStory GTM Strategy & North American Market Entry](case-study-artdesign-story.md) * [All 25 Production Case Folios](work.md) * [Next Case: Creator Business Infrastructure & Media Ops →](case-study-creator-ecosystem.md) * [Root Discovery Index](index.md)