# Maestro — Multi-Agent Orchestration Platform

A vendor-neutral runtime for composing multi-step AI workflows. Visual designer, pluggable integrations, real-time monitoring.

## Key facts

- **Domain:** AI INFRASTRUCTURE
- **Status:** Internal accelerator, deployable in client engagements
- **Use when:** Clients want to compose multi-step AI workflows without committing to a single vendor's agent runtime.
- **Reference engagement:** Available for new engagements

## The problem

The agent runtime market in 2026 is a moving target. Every vendor has a story; few have a portable workflow surface. Customers building multi-step agentic processes find themselves either coupled to a single runtime or stuck wiring their own orchestration layer from scratch.

## The approach

MADs is a runtime-neutral orchestration platform. Workflows are designed visually and stored as portable graphs. Each step in the graph can be a built-in agent, an external LLM call (OpenAI, Anthropic, Vertex AI), a custom HTTP or gRPC endpoint, a message-queue dispatcher, or a webhook trigger. The orchestrator coordinates execution and emits real-time monitoring data to the dashboard.

## The architecture

A Flask backend exposes the workflow API and runtime. A React 18 frontend hosts the visual workflow designer and the live monitoring dashboard. The agent registry holds built-in agents (Echo, Research, Data Analysis, Content Generation, Workflow Automation) plus pluggable adapters for external services. Integration support spans OpenAI GPT-4 and 3.5, Anthropic Claude (REST), Google Vertex AI (Cloud SDK), custom HTTP/gRPC endpoints, Redis/RabbitMQ/Kafka message queues, and webhook triggers. Workflows persist as JSON graphs that can be exported, versioned, and replayed.

## The outcome

Customers compose multi-step workflows without committing to a single agent runtime. The same workflow definition can route LLM calls to different vendors per step, depending on cost, latency, or capability. When the runtime market shifts (and it will), the workflow does not have to.

## ATI shape

- Predictive intelligence — ML modules attach to workflow steps via tool calls; predictive components stay first-class
- Agentic execution — Multi-agent runtime with workflow orchestration, agent registry, and built-in coordination
- Secure data — Integration layer respects the client data perimeter; runtime can run inside the customer cloud
- Actionable outcomes — Workflow execution against client systems via HTTP, gRPC, queues, and webhooks

## Tech stack

- Flask
- Python 3.11
- React 18
- Vendor-neutral LLM adapters
- Redis
- RabbitMQ
- Kafka
- Webhooks

## Impact

- Vendor-neutral — No agent runtime lock-in
- Visual — Drag-and-drop workflow designer
- Real-time — Live monitoring of every workflow step
- Pluggable — OpenAI, Anthropic, Vertex AI, custom HTTP/gRPC

## Related

- [All accelerators](https://levent.ai/accelerators/)
- [Next: Helios](https://levent.ai/accelerators/helios/)

Metrics on this page are estimated and expected improvements describing the design intent of the accelerator. Real-client delivered metrics stay in private decks; see https://levent.ai/ai-content-policy/ for the abstraction policy.

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**Canonical URL:** https://levent.ai/accelerators/maestro/