Magister :
AI Agents Execution Platform

Enterprise focused platform for "Building" & "Running" AI agents for Business.

Magister is a Enterprise ready Operating System for AI agents - with a Low code IDE for developing Agents with Natural Language + embedded Integration tooklit.

Any LLM / Custom LLM High Data Volume & Performance
Deploy
a fraud detection enginea clinical triage systeman AI support routera RAG knowledge basea real-time monitoring pipelinean invoice processor
in minutes.
Book a Demo Browse Blueprints

Sub-10ms
latency
120+
blueprints
300+
integrations
14
AI-native nodes
100K
events/sec
MACH 41 Magister platform architecture - agent types, core services, and infrastructure

How it Works?

Enterprise-Ready AI Agent Runtime Layer

Magister Runtime is a DAG-based execution runtime that orchestrates LLMs, workflows, and data pipelines as a production-grade AI agent.

Magister is to AI systems what Airflow is to data pipelines - and what Kubernetes is to containers.

Typical SDLC

You design on Magister Studio

Visual drag-and-drop Agent Studio (or) Build with Natural language on our "AI Assistant". 14+ AI Native Nodes, 300+ Integration nodes & Declarative vectorization for Data handling.

Compile & Deploy on Magister Runtime

Click Deploy to compile workflows to a distributed in-memory DAG. Versioned, Validated, Optimised, Deterministic, Performance Optimized. Deploy via Enterprise CI-CD tools (or) natively to Test & Deploy to Production to execute, govern and measure

Magister Engine Executes

Streaming runtime with Sub-10ms latency, 100K+ events/sec. Real-time Monitoring & Governance built in, on different Agent types - RAG (or) Conversational AI (or) Event Consumer or Event driven actions. Ideal for all Enterprise Use cases.

Magister as the central runtime layer connecting users, AI agents, and data systems

Platform Architecture

Control Layer Monitoring, governance, live metrics, audit
Data Layer In-memory state, streaming, vector stores, IMap
Integration Layer 300+ connectors - Kafka, databases, APIs, files, cloud
Agent Layer 14 AI-native nodes - LLM, RAG, vector search, agentic reasoning
Execution Layer Compiled DAG runtime - sub-10ms, 100K+ events/sec, exactly-once

Not

A prompt chain tool

Compiled execution, not fragile chains

Not

A webhook glue layer

In-memory streaming, not HTTP hops

Not

Another AI framework

A production runtime, not a library

CHOOSING YOUR AGENT PLATFORM

What Can You Build?

Start with 120+ solution blueprints across 15 industries (or)
design your own AI Agent from scratch. Use our "AI Assistant" to build your Agent with Natural Language.

Automation driven AI Agents

Autonomous agents that reason, decide, and act on live data

Fraud detection engines
Invoice processing & approval
Drug interaction monitoring
Product recommendations

HITL driven AI Agents

Classify, route, and escalate in milliseconds. Have a Human in the loop to review and approve the actions.

Support ticket routing
Content moderation
Clinical triage
Log anomaly detection

Conversational Chatbot / RAG

Ingest Data, embed & Search, Maintain previous chat Context & answer with citations

FAQ chatbot
Legal document search
Clinical trial Q&A
Company handbook assistant

The Studio

The Magister Studio

Describe what you want in natural language. The Magister AI Assistant generates production-ready pipelines - then deploy with one click.

app.mach41.com/studio
Nodes
kafka-source
prompt-template
llm-router
if-node
imap-sink
json-extract
file-source tickets.csv llm-router GPT-4o classify billing imap-sink technical imap-sink escalation imap-sink
Magister AI Assistant
Build a support ticket router that classifies incoming tickets and sends them to billing, technical, or escalation queues.
I've created a pipeline with a file-source reading tickets, an LLM router for classification, and three imap-sink nodes for each queue. Ready to deploy.
Add a filter to only process high-priority tickets first.
Ask AI to build a pipeline...

Solution Blueprints

120+ Deployable AI Systems

Not demos - production-grade solution blueprints. Pick an industry, deploy in hours, customise to your business.

Financial Services

Fraud detection, KYC, trade surveillance

blueprints →

Healthcare

Clinical triage, drug monitoring, claims

blueprints →

Customer Operations

Ticket routing, SLA, sentiment analysis

blueprints →

Retail

Sentiment, inventory, recommendations

blueprints →

Technology

Log anomaly, API health, security

blueprints →

Legal & Compliance

Contracts, GDPR, regulatory screening

blueprints →

Media & Content

Moderation, SEO, categorisation

blueprints →

Supply Chain

Shipment prediction, quality, BOM

blueprints →

AI-Native Nodes

14 Native AI Nodes.
Not HTTP Wrappers.

Every AI node is a first-class pipeline citizen - compiled, optimized, and distributed across the cluster. From LLM routing to RAG with vector search, AI is woven into the streaming fabric.

Prompt Template
LLM Router
JSON Extract
AI Decision
LLM Agent
Vector Search
RAG Builder
Text Splitter
LLM Embed
Sentiment
Vector Store
Tool Node
Supported Models
O
OpenAI
GPT-4o, GPT-4, GPT-3.5 Turbo
A
Anthropic
Claude Opus, Sonnet, Haiku
Az
Azure OpenAI
Enterprise compliance & data residency
L
... Any Local LLMs
Ollama, vLLM, custom endpoints

How Magister Compares ?

One Platform, Multiple Problems Solved

Most tools solve one or two of these. Magister handles all these key ones a single runtime.

AI Workflow Orchestration

Traditional Prompt chains - fragile, no error handling, no observability
Magister Compiled DAG execution - deterministic, debuggable, with live metrics

Real-Time Data Processing

Traditional Kafka + Flink + custom code - months of engineering
Magister Visual pipeline, one-click deploy, sub-10ms latency out of the box

Enterprise Integration

Traditional iPaaS tools + glue scripts - webhook-based, slow, brittle
Magister 300+ connectors built in - Kafka, databases, APIs, files, all streaming

Production Monitoring

Traditional Build your own dashboards, logging, and alerting from scratch
Magister Live per-node metrics, throughput graphs, error tracking - all built in
Capability Magister n8n / Zapier Flink / Kafka Dify / LangChain
Visual builder + AI assistant ✓ ✓ ✕ ✓
Sub-10ms compiled execution ✓ ✕ ✓ ✕
AI-native nodes (LLM, RAG, vector) ✓ ~ ✕ ✓
Self-hosted / air-gapped ✓ ~ ✓ ~
100K+ events/sec throughput ✓ ✕ ✓ ✕

Why Magister

Why Not the Alternatives?

Every Other option has trade-offs. Here's what you give up when you choose each one.

Instead of LangChain / Dify

You get a runtime,
not a library

  • x LangChain is a Python library - you still build the infrastructure, monitoring, and deployment yourself
  • x Prompt chains break silently. No compile-time validation, no cycle detection
  • + Magister compiles workflows to a distributed DAG with sub-10ms latency. Errors caught before deploy, not in production
Instead of n8n / Zapier / Make

You get AI-native execution,
not webhook glue

  • x Automation tools are webhook-based - every step is an HTTP hop. Slow, fragile, expensive at scale
  • x No native AI primitives - LLM integration is bolted on, not first-class
  • + Magister runs everything in-memory with 14 AI-native nodes. 100K+ events/sec, not 10 requests/min
Instead of Camunda / Temporal

You get AI + data + workflows
in one system

  • x BPM tools are workflow-only - no streaming data, no AI agents, no vector search
  • x Adding AI means integrating 3–4 more systems. More ops, more failure modes
  • + Magister is one runtime: AI agents, streaming data, 300+ integrations, visual builder, live monitoring. One deploy.

Quick Deep Dive

From Documents to Decisions in Hours

See how Magister turns a document processing challenge into a deployed AI system - with one blueprint.

The challenge

A financial services team processes 2,000+ invoices daily. Each invoice needs data extraction, validation against a rules engine, approval routing, and anomaly flagging. Their current BPM + manual review process takes 3–5 days per batch.

The Magister solution

One pipeline blueprint. Deploy in an afternoon. Process the full batch in minutes, not days.

File watcher ingests new invoices as they land
LLM call extracts vendor, amount, line items, dates
AI decision routes high-risk invoices for review, auto-approves the rest
IMap sink stores results for dashboard & audit trail
95%
less manual review
3 hrs
to deploy
<2 min
per batch (was 3 days)
Invoice Processing Blueprint
source
File Watcher
directory: /invoices
ai
LLM Extract
vendor, amount, items
ai
AI Decision
risk > threshold?
high risk
Review Queue
approved
Process & File
sink
IMap Sink
audit + dashboard
5 nodes • 1 blueprint • deploy in hours Try this blueprint →

Deployment

Your Infrastructure, Your Data

Multiple Deployment Options., fully secure & governed. All NFRs built-in. High Scalability and Performance Optimized.

Managed SaaS

We host it. You build pipelines. Zero ops overhead. Browser accessed IDE for Optimied platform

Self-Hosted

Hosted as Dockers on your servers. Full control, zero egress. Licenced and Governed by your team, for your agents

Mach41 Hosted

We host dedicated platform for you - all the benefits of self-hosted, but with the peace of mind of a managed service.

Ready to Build?

See Magister in action with your data. Book a 30-minute demo with our team.

Book a Demo Browse Blueprints