Feb 25, 2025
6 Min
Aistra team
Let’s talk shop. When we say “multi-agent architecture,” we’re referring to a setup where multiple AI “agents” each own a specific task. Maybe one handles performance marketing campaigns, another generates eye-popping visuals based on that campaign data, and a third integrates with your CRM to sync leads in real time. This approach lets you parallelize tasks, make more robust decisions, and scale effortlessly as new capabilities come online—all of which is a major win for enterprises looking to stay ahead of the curve.
The Tech Stack Breakdown
Data & Integration Layer
This is home turf for Data Ninjas (or “Integrators”). They’re the backstage crew, making sure raw and structured data flows smoothly into the system. If data is the lifeblood of AI, these folks ensure every agent—whether it’s focused on analytics, marketing, or user interactions—gets the data it needs, exactly when it needs it.
Model Management Layer
This layer houses your machine learning models and handles the logistics of training, deployment, and continuous monitoring. Data scientists and AI engineers operate here, fine-tuning parameters to keep models not just accurate, but aligned with overarching business objectives.Orchestration Layer
Don’t mistake the Orchestration Layer (the tech) for Orchs (the people). Technically, the layer functions like a grand central station for agent communication, managing who hands off tasks to whom. Meanwhile, Orchs (the specialists) design workflows, set priorities, and prevent conflicts so the entire system operates in harmony. And it is the Orchs who have their neck first in line when things go wrong in execution. It is their job to ensure perfection.
Evaluation & Feedback Layer
Enter the Evals (Evaluators)—the system’s watchdogs or the checkers of the makers. They track agent performance, check for bias, and catch model drift before it morphs into a large-scale problem. If one agent starts serving the wrong offers to the wrong segment of users, Evals step in to diagnose, escalate, and help fix the issue.Interface & Experience Layer
This is where AI agents interact with the outside world—whether that means your employees, your customers, or even other AI modules. From conversation flows in a chatbot to intuitive dashboards for data visualization, the system feels human-friendly and brand-consistent. This part of the tech-stack is not an AI-special role and might not need specialists.
Cutting across all these layers is the Agent Manager—the role that ensures each part of this tech stack comes together in a way that meets real business needs. They’re not a separate layer in the architecture, but rather the strategic glue holding it all together. By defining requirements with Data Ninjas, coordinating with Orchs on workflow priorities, reviewing performance insights from Evals, and collaborating with Agent Designers on user experience, the Agent Product Manager keeps the entire multi-agent ecosystem aligned with company goals and customer expectations.
Why It Matters for Jobs
In simpler AI setups, a single data science team might do it all. But as systems get more modular and complex, each layer demands specialized roles—hello, Data Ninjas, Orchs, Evals, and Agent Designers. Companies are no longer just hiring “an AI person”; they’re building dedicated teams to handle the orchestration and evaluation of these multi-agent systems.
