Platform Overview
AgenticWork handles the infrastructure so you can ship AI agents without building everything from scratch. This page covers how the pieces fit together.
How it works
Requests come in through clients (web UI, CLI, SDK, or direct API calls), hit the gateway for auth and rate limiting, then flow through an 11-stage pipeline that handles everything from RAG to tool execution. The pipeline routes to the right LLM provider based on the Intelligence Slider setting.
Tip: Click on diagram nodes to jump to relevant documentation. Click anywhere on the diagram to expand it.
The Chat Pipeline
Every request goes through 11 stages. This isn't arbitrary—each stage handles a specific concern, and the order matters.
What each stage does:
1. Authentication
Validates JWT, Azure AD tokens, or API keys
2. Validation
Sanitizes input, checks user permissions
3. RAG
Pulls relevant context from the vector database
4. Memory
Loads conversation history
5. Prompt Engineering
Applies system prompts and techniques
6. MCP Tools
Finds relevant tools via semantic search
7. Message Preparation
Formats messages for the target LLM
8. Completion
Calls the LLM provider
9. Tool Execution
Runs any tools the model requested
10. Multi-Model
Routes to specialized models if needed
11. Response
Streams results back to the client
Intelligence Slider
The slider (0-100%) controls the cost/quality tradeoff. Lower values use cheaper, faster models. Higher values bring in the heavy hitters with extended thinking.
Rough cost comparison:
Economy
0-40%
Simple Q&A, formatting, high volume
$1
Balanced
41-60%
Code gen, analysis, research
$3-5
Premium
61-100%
Architecture, debugging, critical decisions
$10-50
Auth Flow
We use Azure AD for enterprise SSO. Here's the typical login flow:
Supported auth methods:
Azure AD SSO - Enterprise single sign-on with MFA
JWT Tokens - Stateless auth for API calls
API Keys - For programmatic access (prefixed
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MCP Tools
The platform includes 13 MCP servers with 150+ tools. The proxy handles discovery, routing, and per-tool auth.
Data Flow
Here's how a typical request flows through the system:
Deployment
Production (Kubernetes)
Development (Docker Compose)
Security
Security features:
Azure AD SSO
Enterprise identity with MFA support
RBAC
Role-based access control
API Scopes
Per-key permission limits
Rate Limiting
Tier-based request limits
Audit Logging
Complete activity tracking
Encryption
TLS 1.3, encrypted at rest
Next steps
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