icon: “comments” The core of the MindLab platform is the Orchestrator. It is not a single, monolithic AI, but a runtime Mixture-of-Experts (MoE) engine that intelligently coordinates the work of specialized agents to achieve complex business objectives. The primary way you will interact with the MindLab platform is through a sophisticated chat interface. This is your direct line to the Orchestrator, the powerful intelligence layer that coordinates the work of all your provisioned AI agents. You do not need to know which agent is best for a particular task. Your role is simply to state your objective in natural language. The Orchestrator will handle the rest.

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From Intent to Audited Outcome

When you submit a request, the Orchestrator initiates a multi-step process that is designed to transform your intent into a reliable, auditable outcome.
  1. Understanding Your Goal: The Orchestrator first analyzes your request to understand your underlying business objective.
  2. Selecting the Right Team: It then queries the Expert Registry to assemble the perfect team of specialist agents for the job.
  3. Coordinating the Work: The Orchestrator manages the entire workflow, breaking your request down into sub-tasks, routing them to the appropriate agents, and synthesizing their work into a final, cohesive result.
  4. Delivering the Results: The final output is delivered to you in the chat interface, complete with source-linked citations and a full audit trail.

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The Power of Natural Language

Because you are interacting with a sophisticated orchestration engine, you can express your requests in high-level, outcome-oriented language. Instead of saying: “Summarize the text at URL X, then analyze its sentiment.” You can simply say: “Tell me what’s important about this article and how I should feel about it.” The Orchestrator understands the intent behind your words and will automatically engage the right agents to get the job done. This is the power of a true intelligence layer.

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Runtime MoE vs. Training-time MoE

Traditional AI development has focused on “training-time MoE,” where massive, sparse models are created. While powerful, these models are rigid and expensive to change. MindLab’s “runtime MoE” is a more agile and adaptable paradigm. Instead of relying on a single, static model, the Orchestrator dynamically routes tasks to a diverse registry of specialized agents.

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The Orchestration Process

The Orchestrator’s primary function is to factorize user intent into a series of atomic, testable sub-tasks and then route those tasks to the most appropriate specialist agent.
1

Factorization

The Orchestrator receives a high-level objective and breaks it down into a directed acyclic graph (DAG) of smaller, well-defined sub-tasks.
2

Routing

For each sub-task, the Orchestrator queries the Expert Registry to find the optimal agent.
3

Execution

The Orchestrator dispatches the sub-tasks and monitors their execution via the Flow Engine.
A common and powerful topology for this process is a Planner → Solver → Reviewer → Verifier chain.