© 2025 Sazmir® Labs
© 2025 Sazmir® Labs
© 2025 Sazmir® Labs
Sazmir AI
A scalable AI platform that orchestrates agent swarms via reinforcement learning for autonomous business operations.
© 2025 Sazmir® Labs
Sazmir AI
Built on the blockchain.
Version 1.2 is now live.
Sazmir AI
Built on the blockchain.
Version 1.2 is now live.
A scalable AI platform that orchestrates agent swarms via reinforcement learning for autonomous business operations.
© 2025 Sazmir® Labs
Sazmir AI
Built on the blockchain.
Version 1.2 is now live.
Installation tools
Installation tools
Installation tools
Features
Sazmir
Core features
Core features
Dynamically coordinates thousands of AI agents into autonomous swarms, solving enterprise-scale challenges through AI-driven collaboration.
Features
Sazmir
Core features
Dynamically coordinates thousands of AI agents into autonomous swarms, solving enterprise-scale challenges through AI-driven collaboration.
Why choose us
Why choose us
Why choose us
Agent swarms dynamically scale complex enterprise tasks.
Agent swarms dynamically scale complex enterprise tasks.
The only framework combining RL-driven agent swarms with real-time orchestration to scale complex enterprise tasks.
01
30% higher resource utilization via dynamic resource pooling and priority scheduling, reducing hardware costs compared to legacy AI systems.
01
30% higher resource utilization via dynamic resource pooling and priority scheduling, reducing hardware costs compared to legacy AI systems.
01
30% higher resource utilization via dynamic resource pooling and priority scheduling, reducing hardware costs compared to legacy AI systems.
02
40% lower cloud costs through elastic agent scaling and hybrid edge-cloud deployment, minimizing over-provisioning in volatile workloads.
02
40% lower cloud costs through elastic agent scaling and hybrid edge-cloud deployment, minimizing over-provisioning in volatile workloads.
02
40% lower cloud costs through elastic agent scaling and hybrid edge-cloud deployment, minimizing over-provisioning in volatile workloads.
Technical architecture
Technical architecture
Technical architecture
Architecture
Architecture
(001)
Distributed Communication
Built on Apache Kafka (high-throughput messaging) and gRPC (low-latency RPC) to handle real-time data streams and asynchronous events, enabling million-level events per second (e.g., real-time financial order routing).
Topic
Apache Kafka
gRPC
Distributed Communication
(002)
Containerized Agent Orchestration Engine
Leverages Kubernetes for lifecycle management and Argo Workflows for DAG-based task scheduling, enabling instant scaling to tens of thousands of agents under burst workloads.
Topic
Kubernetes
Argo Workflows
DAG
Containerized Agent Orchestration Engine
(003)
Multi-Agent Reinforcement Learning Decision Layer
Combines PyTorch, Ray RLlib, and digital twins for distributed RL training, using game theory and collaborative Q-learning to optimize agent strategies (e.g., supplier-logistics agent).
Topic
PyTorch
Ray RLlib
Decision Layer
Multi-Agent Reinforcement Learning Decision Layer
(004)
Heterogeneous Integration Layer
Secures data via OAuth 2.0 and HashiCorp Vault, integrates legacy systems (SAP, Salesforce) through GraphQL and Apache Camel, enabling protocol translation (e.g., EDI-to-API).
Topic
OAuth 2.0
HashiCorp Vault
GraphQL
Apache Camel
Heterogeneous Integration Layer
(001)
Distributed Communication
Built on Apache Kafka (high-throughput messaging) and gRPC (low-latency RPC) to handle real-time data streams and asynchronous events, enabling million-level events per second (e.g., real-time financial order routing).
Topic
Apache Kafka
gRPC
Distributed Communication
(002)
Containerized Agent Orchestration Engine
Leverages Kubernetes for lifecycle management and Argo Workflows for DAG-based task scheduling, enabling instant scaling to tens of thousands of agents under burst workloads.
Topic
Kubernetes
Argo Workflows
DAG
Containerized Agent Orchestration Engine
(003)
Multi-Agent Reinforcement Learning Decision Layer
Combines PyTorch, Ray RLlib, and digital twins for distributed RL training, using game theory and collaborative Q-learning to optimize agent strategies (e.g., supplier-logistics agent).
Topic
PyTorch
Ray RLlib
Decision Layer
Multi-Agent Reinforcement Learning Decision Layer
(004)
Heterogeneous Integration Layer
Secures data via OAuth 2.0 and HashiCorp Vault, integrates legacy systems (SAP, Salesforce) through GraphQL and Apache Camel, enabling protocol translation (e.g., EDI-to-API).
Topic
OAuth 2.0
HashiCorp Vault
GraphQL
Apache Camel
Heterogeneous Integration Layer
(001)
Distributed Communication
Built on Apache Kafka (high-throughput messaging) and gRPC (low-latency RPC) to handle real-time data streams and asynchronous events, enabling million-level events per second (e.g., real-time financial order routing).
Topic
Apache Kafka
gRPC
Distributed Communication
(002)
Containerized Agent Orchestration Engine
Leverages Kubernetes for lifecycle management and Argo Workflows for DAG-based task scheduling, enabling instant scaling to tens of thousands of agents under burst workloads.
Topic
Kubernetes
Argo Workflows
DAG
Containerized Agent Orchestration Engine
(003)
Multi-Agent Reinforcement Learning Decision Layer
Combines PyTorch, Ray RLlib, and digital twins for distributed RL training, using game theory and collaborative Q-learning to optimize agent strategies (e.g., supplier-logistics agent).
Topic
PyTorch
Ray RLlib
Decision Layer
Multi-Agent Reinforcement Learning Decision Layer
(004)
Heterogeneous Integration Layer
Secures data via OAuth 2.0 and HashiCorp Vault, integrates legacy systems (SAP, Salesforce) through GraphQL and Apache Camel, enabling protocol translation (e.g., EDI-to-API).
Topic
OAuth 2.0
HashiCorp Vault
GraphQL
Apache Camel
Heterogeneous Integration Layer
Modules
Modules
Modules
Sazmir
Sazmir
Sazmir
Functional Modules — Four Core Engines Powering Enterprise Agent Swarms
Functional Modules — Four Core Engines Powering Enterprise Agent Swarms
From Dynamic Orchestration to Secure Integration, Modular Design Empowers Complex Business Scenarios.
01
Agent Orchestration Hub
01
Agent Orchestration Hub
01
Agent Orchestration Hub
02
Real-Time Collaboration & Game Network
02
Real-Time Collaboration & Game Network
02
Real-Time Collaboration & Game Network
03
Decision Simulation & Knowledge Core
03
Decision Simulation & Knowledge Core
03
Decision Simulation & Knowledge Core
04
Enterprise Integration & Security Gateway
04
Enterprise Integration & Security Gateway
04
Enterprise Integration & Security Gateway
Reduction in Automated Decision Errors
Reduction in Automated Decision Errors
Reduction in Automated Decision Errors
Cloud cost reduction
Cloud cost reduction
Cloud cost reduction
Improved resource utilization
Improved resource utilization
Improved resource utilization
Single-cluster agent support
Single-cluster agent support
Single-cluster agent support
Sazmir
Sazmir
Sazmir
Build a cross-industry agent network as the decision engine for next-gen enterprise OS.
To redefine enterprise-scale AI capabilities through dynamic orchestration and swarm intelligence, enabling autonomous decisions, efficient collaboration, and cost-effective operations.
To redefine enterprise-scale AI capabilities through dynamic orchestration and swarm intelligence, enabling autonomous decisions, efficient collaboration, and cost-effective operations.
Where every complex business scenario is powered by self-evolving AI agent swarms, fostering seamless human-AI collaboration.
Where every complex business scenario is powered by self-evolving AI agent swarms, fostering seamless human-AI collaboration.
Deployment Guide
Deployment Guide
Deployment Guide
Sazmir docs
Sazmir AI leverages Kubernetes and reinforcement learning for rapid deployment and efficient management of enterprise agent swarms, launching 1000+ agent networks in 5 minutes to tackle complex business challenges.
Recommended
Kubernetes cluster: 3+ nodes, each with 16-core CPU / 64 GB RAM / 1 TB SSD
Auto-scaling support (e.g., AWS EC2 Auto Scaling)
Container Runtime: Docker 20.10+ or containerd 1.5+
Database: PostgreSQL 14+ (with replication recommended)
Message Queue: Apache Kafka 3.2+ (Zookeeper 3.7+ or KRaft mode)
Monitoring: Prometheus 2.35+, Grafana 9.0+
Sazmir AI leverages Kubernetes and reinforcement learning for rapid deployment and efficient management of enterprise agent swarms, launching 1000+ agent networks in 5 minutes to tackle complex business challenges.
Recommended
Kubernetes cluster: 3+ nodes, each with 16-core CPU / 64 GB RAM / 1 TB SSD
Auto-scaling support (e.g., AWS EC2 Auto Scaling)
Container Runtime: Docker 20.10+ or containerd 1.5+
Database: PostgreSQL 14+ (with replication recommended)
Message Queue: Apache Kafka 3.2+ (Zookeeper 3.7+ or KRaft mode)
Monitoring: Prometheus 2.35+, Grafana 9.0+
Sazmir AI leverages Kubernetes and reinforcement learning for rapid deployment and efficient management of enterprise agent swarms, launching 1000+ agent networks in 5 minutes to tackle complex business challenges.
Recommended
Kubernetes cluster: 3+ nodes, each with 16-core CPU / 64 GB RAM / 1 TB SSD
Auto-scaling support (e.g., AWS EC2 Auto Scaling)
Container Runtime: Docker 20.10+ or containerd 1.5+
Database: PostgreSQL 14+ (with replication recommended)
Message Queue: Apache Kafka 3.2+ (Zookeeper 3.7+ or KRaft mode)
Monitoring: Prometheus 2.35+, Grafana 9.0+
FAQ.
FAQ.
Got questions? We’ve got answers. Here’s everything you need to know about working with us.
Sazmir AI requires a Kubernetes cluster (minimum 3 nodes with 16-core/64GB RAM) and supports hybrid cloud (e.g., AWS EKS or on-premises). Edge deployments can use K3s.
Sazmir AI uniquely integrates RL with dynamic resource pooling, scaling to 10,000+ agents with 30% higher resource efficiency and <50ms latency.
Apache Kafka (1M+ events/sec) and gRPC enable low-latency communication, while edge nodes process IoT data locally.
Yes. GraphQL and Apache Camel convert legacy data (e.g., SAP work orders) into agent tasks without system modifications.
Zero-trust architecture (mTLS), HashiCorp Vault, and GDPR-compliant audit logs guarantee data sovereignty.
No. Distributed priority scheduling ensures linear task completion time growth (not exponential) from 100 to 10,000 agents, with auto-balanced resource pools.
Sazmir AI requires a Kubernetes cluster (minimum 3 nodes with 16-core/64GB RAM) and supports hybrid cloud (e.g., AWS EKS or on-premises). Edge deployments can use K3s.
Sazmir AI uniquely integrates RL with dynamic resource pooling, scaling to 10,000+ agents with 30% higher resource efficiency and <50ms latency.
Apache Kafka (1M+ events/sec) and gRPC enable low-latency communication, while edge nodes process IoT data locally.
Yes. GraphQL and Apache Camel convert legacy data (e.g., SAP work orders) into agent tasks without system modifications.
Zero-trust architecture (mTLS), HashiCorp Vault, and GDPR-compliant audit logs guarantee data sovereignty.
No. Distributed priority scheduling ensures linear task completion time growth (not exponential) from 100 to 10,000 agents, with auto-balanced resource pools.
Sazmir AI requires a Kubernetes cluster (minimum 3 nodes with 16-core/64GB RAM) and supports hybrid cloud (e.g., AWS EKS or on-premises). Edge deployments can use K3s.
Sazmir AI uniquely integrates RL with dynamic resource pooling, scaling to 10,000+ agents with 30% higher resource efficiency and <50ms latency.
Apache Kafka (1M+ events/sec) and gRPC enable low-latency communication, while edge nodes process IoT data locally.
Yes. GraphQL and Apache Camel convert legacy data (e.g., SAP work orders) into agent tasks without system modifications.
Zero-trust architecture (mTLS), HashiCorp Vault, and GDPR-compliant audit logs guarantee data sovereignty.
No. Distributed priority scheduling ensures linear task completion time growth (not exponential) from 100 to 10,000 agents, with auto-balanced resource pools.
Let’s talk.
For any inquiries, don't hesitate to reach out to us.
Deployment
5-minute agent swarm setup, hybrid cloud & edge-ready.
Collaboration
Flexible models to integrate with your legacy systems and data platforms.
Let’s talk.
For any inquiries, don't hesitate to reach out to us.
Deployment
5-minute agent swarm setup, hybrid cloud & edge-ready.
Collaboration
Flexible models to integrate with your legacy systems and data platforms.
Let’s talk.
For any inquiries, don't hesitate to reach out to us.
Deployment
5-minute agent swarm setup, hybrid cloud & edge-ready.
Collaboration
Flexible models to integrate with your legacy systems and data platforms.