Platform Architecture
Magent is engineered as a full-stack, modular intelligence infrastructure capable of managing autonomous AI agents at scale. Its architecture is designed to balance complexity under the hood with usability on the surface.
Core Layer – Agent Runtime & Shared Memory
At the heart of Magent lies the Agent Runtime Engine—a lightweight, high-throughput execution system optimized for concurrent agent activity. This layer manages agent instantiation, context handling, and behavior state.
In parallel, Magent maintains a Shared Memory Fabric, which functions as a distributed, persistent knowledge layer. It allows agents to:
Retain event history across sessions
Query shared experiences and decision trees
Store user preferences and long-term feedback
Access real-time signals from multiple workflows
This memory model enables agents to evolve, reference prior knowledge, and coordinate across domains.
Engine Layer – Logic Orchestration
This layer hosts Magent’s Dynamic Workflow Engine, responsible for sequencing agent behavior based on real-time triggers and environmental signals.
Key components include:
Flow Mapper: Translates natural language goals into operational logic
Behavioral Router: Routes decisions based on internal agent state and external conditions
Policy Enforcer: Ensures all actions align with business constraints, compliance rules, or brand guidelines
All logic here is reconfigurable in real time without redeployment, supporting agility and responsiveness.
Interface Layer – Visual Builder & Integration Connectors
The interface layer makes the complexity of the underlying system accessible to non-technical users.
Components include:
No-Code Builder: Drag-and-drop canvas with prompt-injection overlays
Prompt Library: Reusable prompt components with versioning and meta tags
Template Store: Prebuilt agents and scenarios for marketing, HR, sales, finance, ops
App Connectors: Native connections to SaaS apps and custom APIs via secure OAuth or key-based access
This is where ideas become agents—without code.
AI Layer – Model Abstraction & Thinking Profiles
This layer provides access to various LLMs via a standardized interface, allowing Magent agents to:
Switch models per task (e.g., Claude for analysis, OpenAI for chat)
Define thinking profiles (e.g., conservative, curious, skeptical)
Inject context variables into prompts automatically
Choose optimal temperature, token limits, retry strategies
It decouples reasoning logic from infrastructure, ensuring flexibility and future-proofing.
Security Layer – Control, Audit, Privacy
Security is embedded at every level:
Role-based access control (RBAC) for user and agent permissions
Granular audit logs with replayable workflows
End-to-end encryption of sensitive data
On-premise agent support for enterprise compliance
Data locality governance per regulatory region (GDPR, SOC 2, etc.)
Magent is enterprise-ready out of the box.
Last updated