The AI Agent OS

Build AI agents.
Deploy them anywhere.

The first operating system for AI agents — build with a full-stack IDE, deploy as portable containers to any cloud, monitor with a real-time command center, and orchestrate your entire AI workforce from a live agent graph.

Visual Builder
🔬 Build your agent in the UI
📚 Add knowledge, tools, triggers
Deploy → AWS one click
Or use the CLI
# Build & deploy in two commands
kinvex build ./my-agent
kinvex deploy --target aws
# or: gcp | azure | k8s | edge
Self-Hosted
Real-Time Command Center
MCP + A2A + OCI
Zero Lock-In
The Problem

Every platform forces you to choose.

Want power? Give up the UI. Want ease? Give up your infrastructure. Want flexibility? Accept that agents are second-class citizens. Want portability? Settle for YAML files with no intelligence layer.

Choose Power
CrewAI / LangGraph
Multi-agent, memory, evals, deep customization
Give up: Visual builder, chat widget, managed integrations, portable deployment
Choose Ease
Relevance AI / Lindy
Great UX, AI creation, 2,000+ integrations
Give up: Self-hosting, BYOK, data control, deploy anywhere, monitoring
Choose Flexibility
n8n / Dify
Self-hosted, visual builder, growing AI features
Give up: Agents are inside workflow nodes. No portable deployment, limited integrations
Choose Portability
Docker cagent
OCI artifacts, push/pull agents like containers
Give up: Just YAML + CLI. No knowledge, no memory, no evals, no visual builder

What if you didn't have to choose?

Full-stack IDE + portable containers + deploy anywhere + real-time command center + open standards. That's the AI Agent OS.

How It Works

Build. Package. Deploy anywhere.

Docker changed software by making it portable. We're doing the same for AI agents — but with the full development environment built in.

1
Build in the UI or with code
Drag-and-drop visual builder for business users. Code and CLI for developers. Add knowledge bases, configure memory, connect 2,500+ integrations, set up eval suites, create workflows — all in one system.
2
Package as a portable container
Your agent, its tools, knowledge references, memory config, eval history, triggers, and deployment manifest — packaged as an OCI-compatible artifact. Push to any registry.
3
Deploy to any target — one click or one command
Pick your target in the UI and hit Deploy, or use kinvex deploy from the CLI. Kinvex Cloud, AWS, GCP, Azure, Kubernetes, Docker, edge devices. Auto-generated infrastructure.
4
Connect across environments
Agents deployed to different clouds communicate via A2A protocol. Your sales agent on AWS talks to your support agent on GCP. One mesh, zero lock-in.

Deploy targets

One agent. Any infrastructure. Your choice.

Kinvex Cloud
Managed. Zero config.
AWS / GCP / Azure
Auto-generated Terraform.
Kubernetes
Helm charts. Any cluster.
Docker / Edge
Self-hosted. Air-gapped.
Command Center

Don't just deploy agents.
Command them.

A living, breathing command center for your entire AI workforce. Watch agents think, collaborate, and deliver — with a real-time graph that makes your agent network feel alive.

Live Agent Graph

Your AI workforce, visualized as a living network.

See every agent, every connection, every message flowing between them — in real time. Not a static diagram. A live, interactive graph that pulses with every action your agents take. Drag nodes, zoom into conversations, trace decisions as they happen.

  • Interactive node graph with live status indicators
  • Watch agent-to-agent messages flow in real time
  • Trace decision chains across multi-agent workflows
  • Zoom from fleet overview to individual agent conversations
Agent Network 4 agents connected · Live
Sales Agent
142 runs · 99.8%
Research Agent
89 runs · 98.2%
Support Agent
234 runs · 97.1%
Analytics Agent
567 runs · 99.9%
Live Connections via A2A
Sales Research 23 delegations
Support Sales 12 escalations
Analytics All agents 567 reports
12.8K
tokens/min
99.7%
success rate
$0.42
avg cost/run
Intelligent Alerts

Know before things break. Not after.

Your agents don't operate in the dark. Neither should you. Kinvex watches every heartbeat — latency spikes, error cascades, budget overruns, anomaly patterns — and alerts you in milliseconds, not minutes.

  • Latency, error rate, cost, and token usage monitoring
  • Anomaly detection with pattern-based intelligence
  • Route alerts to Slack, PagerDuty, email, or webhooks
  • Auto-remediation: fallback models, rate limiting, escalation
Alert Feed 3 active
CRITICAL 2 min ago
Latency spike on Support Agent: 2.1s → 4.3s avg
Auto-switching to fallback model
WARNING 8 min ago
Research Agent at 80% of daily budget ($34.20 / $42.00)
Slack notification sent
ANOMALY 12 min ago
Sales Agent success rate dropped 2.1% — pattern detected
Investigating · auto-escalation in 5 min

Other platforms deploy agents into a black box. We give you the control room.

Real-time graph + intelligent alerts + agent-to-agent communication + live telemetry. That's the AI Agent OS.

The Platform

Not just a platform. An operating system.

Docker cagent gives you portability but no intelligence layer. Kinvex is the complete operating system for AI agents — nine layers sharing one data model — build, deploy, monitor, and command.

Layer 9 — Deploy Anywhere
Portable agent containers OCI registry push/pull Multi-cloud deploy Edge runtime A2A agent mesh
Layer 8 — Command Center (NEW)
Live agent graph Real-time telemetry Smart alerts Agent communication logs Cost & token tracking
Layer 7 — Marketplace & Community
Publish agents Clone workflows Share tools Community library Cross-org sharing
Layer 6 — Evaluation & Quality
Eval suites LLM judges Scenario testing Score comparison Regression detection
Layer 5 — Knowledge & Memory
RAG pipeline Vector embeddings Semantic search Long-term memory Short-term context
Layer 4 — Multi-Agent Workflows
Sequential Parallel Hierarchical Conditional Human gates DAG orchestration
Layer 3 — AI Agents
Multi-LLM (Claude, GPT, Gemini) Fallback models Tool calling Versioning AI-powered creation
Layer 2 — Integrations & Triggers
2,500+ apps (Pipedream) MCP protocol Cron schedules Webhooks API triggers
Layer 1 — Enterprise Infrastructure
RBAC Org/workspace model Audit logs Encrypted secrets (AES-256) BYOK
What Makes This Extraordinary

Portable agent containers. The full stack travels with them.

Docker cagent packages agents as OCI artifacts — but they're just YAML files. Our agent containers carry the full stack: the agent spec, tool bindings, knowledge base references, memory configuration, eval history, trigger definitions, and deployment manifest. Everything needed to run the agent in any environment, packaged as one artifact.

  • Agent spec + tool configs + knowledge refs in one package
  • OCI compatible — push to Docker Hub, ECR, GCR, any registry
  • Version-tagged deployments with rollback
  • Environment-aware: same container, different configs per target
Agent Container Contents
🔬
agent.spec
System prompt, model config, fallbacks
🔧
tools/
Tool definitions, MCP bindings, integrations
📚
knowledge/
RAG config, embedding model, vector refs
🧠
memory.config
Short-term + long-term memory settings
deploy.manifest
Targets, scaling, triggers, env vars
Open Standards Native

MCP. A2A. OCI. Built on the standards the industry is adopting.

In December 2025, the Linux Foundation created the Agentic AI Foundation with Anthropic, OpenAI, Google, Microsoft, and AWS. The future of AI agents is open protocols. Kinvex AI is built on these standards from day one — not retrofitting them later.

  • MCP — Model Context Protocol for tool integration
  • A2A — Agent-to-Agent protocol for cross-environment communication
  • OCI — Open Container Initiative for agent distribution
  • 2,500+ apps — Pipedream Connect managed OAuth on your infrastructure
Protocol Stack
A2A Protocol
Agent-to-Agent communication across clouds
Native
MCP (Model Context Protocol)
Universal tool integration standard
Native
OCI Artifacts
Push/pull agents like container images
Native
Pipedream Connect
2,500+ managed OAuth integrations
Integrated
Proof Point

No one else does all of this.

We researched each platform as of February 2026. The new columns — portable deployment, multi-cloud, agent mesh — are where the gap is widest.

Capability Kinvex AI
(Planned)
n8n Dify CrewAI Docker
cagent
LangGraph
Portable agent containers
Deploy to any cloud Self-host Self-host Self-host Manual AWS only
A2A agent mesh A2A
Real-time monitoring & alerts Basic AMP Traces
Live agent graph Studio
Full-stack IDE (visual + code) Visual Visual Code YAML Code
Autonomous AI agents
Knowledge / RAG Partial Partial
Agent memory (persistent) Partial
Built-in eval / testing Basic Partial
2,500+ app integrations 400+ Limited Limited Limited
MCP native
Self-hosted + BYOK
Embeddable chat widget
Community marketplace Registry
Agent-to-agent communication A2A
= Planned = Available Partial = Limited = Not available

Based on publicly available documentation as of February 2026.

The gap no one has filled

Platforms with full development environments can't deploy portably. Portable tools have no development environment. Nobody offers real-time monitoring with a live agent graph. Kinvex AI is the AI Agent OS: build, deploy, monitor, and command — all in one.

The AI Agent OS

Build. Deploy. Monitor. Command. The operating system for your entire AI workforce. Zero lock-in.

hello@kinvexai.com