Platform Overview

This page gives you a bird’s-eye view of what powers qRaptor. You don’t need to understand this to use the platform — but it helps you appreciate the capabilities at your disposal.

The Full-Intelligence Stack

qRaptor is built as a layered platform where each layer provides capabilities to the layers above:


┌──────────────────────────────────────────────────────────────────────────┐
│              HUMAN-IN-THE-LOOP GOVERNANCE & OVERSIGHT                     │
├──────────────────────────────────────────────────────────────────────────┤
│                                                                          │
│   Application Dev Engine (Powered by App Context Graph)                  │
│   Prompt/Docs → Req. Spec → App Graph → Code → Auto-Fix → Iterate      │
│                                                                          │
├────────────────────────────────┬─────────────────────────────────────────┤
│   A2A Protocol (Agent Comm)    │   AG-UI Protocol (Conversational UI)    │
├────────────────────────────────┴─────────────────────────────────────────┤
│                                                                          │
│   AGENTIC OS                                                             │
│   ┌─────────────┐ ┌──────────────────┐ ┌────────────────────────┐       │
│   │ Input/Output│ │ Kernel Services  │ │ Tools Configuration    │       │
│   │ I/O Handler │ │ Session Mgmt     │ │ HTTP Tools             │       │
│   ├─────────────┤ │ Indexing Services│ │ Python (Local/Remote)  │       │
│   │ Processing  │ │ System Tools     │ │ MCP Connector          │       │
│   │ AI Agent    │ ├──────────────────┤ └────────────────────────┘       │
│   │ Task Agent  │ │ Memory           │                                   │
│   └─────────────┘ │ Semantic|Profile │                                   │
│                    │ Episodic         │                                   │
│                    └──────────────────┘                                   │
├──────────────────────────────────────────────────────────────────────────┤
│                     MCP SERVERS (Model Context Protocol)                  │
└──────────────────────────────────────────────────────────────────────────┘

Layer by layer

Human-in-the-Loop Governance & Oversight

Every action in qRaptor is governed. You set policies, approval gates, and oversight rules — the platform enforces them:

Approval gates — Pause agent execution for human review before critical actions.
Content policies — Control what LLMs can generate and respond with.
Budget enforcement — Cap spending per agent, per project, or per subscription.
Audit trails — Every action, every decision, every change is logged with full traceability.

Application Dev Engine

The code generation layer that turns natural language into production-ready applications:

Powered by the App Context Graph — a structured representation of your app’s entities, relationships, and files.
Generates complete web, mobile, and copilot applications.
Self-correcting: if the build fails, the engine automatically diagnoses and fixes issues.
Iterative: describe changes in natural language and the engine updates your app.

Protocols

Two communication protocols connect your agents to the outside world:

Protocol	Purpose
A2A (Agent-to-Agent)	Lets agents call other agents. Expose your agent as a service that other systems can invoke programmatically.
AG-UI (Conversational UI)	Powers the real-time streaming chat experience. Shows thinking, planning, and responses as they happen.

Agentic OS

The execution engine that runs your agents:

Component	What it does
I/O Handler	Receives requests from any channel and formats responses
AI Agent Execution	Runs conversational agents with reasoning, planning, and tool use
Task Agent Execution	Runs workflow graphs node-by-node
Session Management	Maintains conversation state across messages
Indexing Services	Keeps content stores and search indexes up to date
Memory	Three-tier memory: Semantic (learned knowledge), Profile (user preferences), Episodic (session history)
Tools Configuration	Manages tool access: HTTP tools, Python execution (local/remote), and MCP connectors

MCP Servers (Model Context Protocol)

qRaptor integrates with the MCP ecosystem bidirectionally:

Consume — Connect to any MCP server to give your agents access to external tools and data.
Publish — Expose your agents as MCP endpoints that other AI systems can use.

Common Services

Available across the entire platform:

Service	What you get
Auth	Secure sign-in, SSO, API keys, and session management
Governance	RBAC roles, custom permissions, content policies, spending limits
Security	Data encryption, network isolation, vulnerability scanning
Audit	Complete history of who did what, when, with full traceability
Observability	Real-time traces, execution monitoring, cost tracking, and performance metrics
Data Vault	Secure storage for tables, documents, vector stores, and agent memory

Integrations

External APIs

Connect your agents to third-party services:

Communication: Exotel (telephony), email providers
Productivity: Google Meet, calendar integrations
Any REST API via custom HTTP tool definitions

Channels

Deploy your agents to reach users wherever they are:

Web Interface — Chat widget for websites and apps
Voice / Telephony — Phone calls with AI-powered voice interaction
WhatsApp, Teams, Slack — Messaging platform integrations
API — Direct programmatic access for custom applications

What this means for you

You focus on what your agents should do and what your apps should look like. qRaptor handles:

Executing agent logic reliably at scale
Managing sessions, memory, and state
Connecting to tools and external services
Securing everything with governance and audit
Deploying to multiple channels simultaneously

Next steps

Glossary — Definitions of platform terms.
Create your account — Start building.