What Is Agyn?

Agyn is not another agent framework. It is a Kubernetes-native runtime purpose-built for running AI agents in production. Where frameworks like LangGraph, CrewAI, and AutoGen focus on how agents reason, plan, and collaborate, Agyn focuses on where and how those agents execute — with proper isolation, governance, and cost control.

The project was introduced on May 20, 2026, through a blog post on agyn.io, accompanied by an academic paper on arXiv (arXiv:2605.27575) published on May 26, 2026, by Nikita Benkovich and Vitalii Valkov. The platform is a complete rewrite from earlier research (arXiv:2602.01465), reflecting lessons learned from real-world agent deployment challenges.

Agyn is licensed under AGPL-3.0, written in TypeScript, and available on GitHub with 217 stars, 5 forks, and 0 open issues as of July 2026. It offers a free Community Edition for self-hosting and enterprise managed services for organizations that want a fully managed experience.

Key Features Deep Dive

Agent Definition as Code via Terraform

The most distinctive feature of Agyn is that agents are defined as Terraform resources. This means your agent configuration — model selection, tool access, environment variables, scaling parameters, and security policies — lives in version-controlled .tf files alongside the rest of your infrastructure.

resource "agyn_agent" "code_reviewer" {
  name    = "code-reviewer"
  image   = "claude-code"
  model   = "claude-sonnet-4"
  tools   = ["github", "slack", "jira"]
  scaling = {
    min_instances = 0
    max_instances = 10
  }
}

This approach brings GitOps workflows to AI agent management. Teams can review agent configuration changes through pull requests, roll back problematic updates, and maintain a complete audit trail of who changed what and when. For organizations already using Terraform for cloud infrastructure, adding agent definitions is a natural extension of existing workflows.

MCP Tool Isolation and Credential Security

Agyn implements a security architecture that separates credentials from agent reasoning. Instead of injecting API keys and service tokens into the agent’s environment — where a compromised or hallucinating agent could expose them — Agyn injects credentials into isolated MCP (Model Context Protocol) containers.

The agent communicates with tools through MCP containers that hold the actual credentials. The model never sees the raw API key or token in its context window. This means:

• A compromised agent cannot leak credentials — it only has access to tool responses, not the keys themselves
• Credential rotation is centralized — update credentials in one place, and all agents using that tool inherit the change
• Least-privilege by default — each tool gets only the permissions it needs, and each agent gets only the tools it needs

This is a significant security differentiator compared to traditional agent deployments where API keys are passed as environment variables directly into the agent process.

Serverless Agent Runtime Model

Agyn uses a serverless execution model where each agent invocation spins a fresh container. There is no warm pool management, no idle compute costs, and no risk of state leaking between invocations.

For bursty workloads — the most common pattern for AI agents — this model is significantly more cost-effective. A code review agent that runs 50 times per day costs only the compute time of those 50 invocations, not a 24/7 pod.

Zero-Trust Networking with OpenZiti

Agyn integrates OpenZiti, an open-source zero-trust networking overlay, to handle agent-to-service communication. Instead of opening firewall ports, configuring VPNs, or managing complex network policies, Agyn creates a secure overlay network where agents and services authenticate each other before any traffic flows.

Key benefits:

• No VPN required — agents running on any Kubernetes cluster can reach internal services securely
• Mutual TLS — both the agent and the service verify each other’s identity
• Least-privilege network access — agents can only reach the specific services they are authorized to use
• No public exposure — services never need a public IP or open port

This is particularly valuable for organizations running agents that need to access internal databases, APIs, or on-premise systems without exposing those systems to the public internet.

Per-Agent and Per-Org Observability

Agyn provides built-in observability for token usage, tool activity, and agent performance at both the individual agent and organizational level. Teams can track:

• Token consumption per agent, per model, and per time period
• Tool invocation frequency and success rates
• Agent execution duration and error rates
• Cost attribution by team, project, or agent

This observability layer is critical for organizations that need to understand their agent infrastructure costs and usage patterns, especially as the number of agents in an organization grows.

Architecture and Design Principles

Signal-Driven Stateful Serverless Runtime

Agyn’s runtime is built on three architectural principles that address the fundamental challenges of running AI agents at scale.

Signal-driven execution means agents are triggered by events — webhooks, schedule-based cron jobs, API calls, or messages from other agents. This event-driven model aligns naturally with how agents are used in practice: they respond to inputs rather than running continuously.

Stateful serverless is the key innovation. Agyn maintains persistent context that survives container restarts, scaling events, and even multi-region deployments. An agent can be interrupted mid-task, have its container recycled, and resume exactly where it left off. This is achieved through a state store that is decoupled from the compute layer, allowing the stateless container to access persistent state when needed.

Fresh container per invocation ensures that no state leaks between invocations. Each agent call starts with a clean environment, loads the necessary state from the persistent store, executes, and saves any state changes before the container is destroyed.

Agent-Agnostic, Model-Agnostic, Cloud-Agnostic

Agyn is designed to work with any agent, any model, and any cloud. The platform ships with three pre-built agent init images — Claude Code, Codex, and the agn (Agyn’s own agent loop) — but teams can bring their own agent image.

The model-agnostic design means you can switch between OpenAI, Anthropic, Google, or open-source models without changing your agent infrastructure. The cloud-agnostic design means Agyn runs on any Kubernetes cluster — AWS EKS, Google GKE, Azure AKS, on-premise, or edge.

Supported Agents — Claude Code, Codex, and Custom

Agyn ships with three pre-built agent init images out of the box:

Teams can also build custom agent images by extending the base Agyn runtime. This allows organizations to integrate their existing agent frameworks — LangGraph, CrewAI, AutoGen — into Agyn’s infrastructure layer, getting the best of both worlds: the orchestration logic from the framework and the production infrastructure from Agyn.

Pricing Model — Community Edition vs Enterprise

Agyn follows an open-core business model with two tiers:

The Community Edition is free and fully functional for self-hosted deployments. The Enterprise tier adds managed hosting, advanced access control, audit logging, and support SLAs for organizations that need them.

Agyn vs Competitors — How It Differs from LangGraph, CrewAI, AutoGen

Agyn occupies a different layer of the stack than popular agent frameworks. The comparison is not about which is better — it is about which layer of the problem each tool solves.

The two approaches are complementary. Teams can use LangGraph or CrewAI for agent orchestration logic and deploy those agents on Agyn for infrastructure management. Agyn’s custom agent image support makes this integration straightforward.

Use Cases and Real-World Applications

Autonomous Customer Support Swarms

Organizations running customer support at scale can deploy swarms of specialized agents on Agyn — one for billing inquiries, one for technical support, one for account management — each with its own tool access and security boundaries. The serverless model means costs scale with ticket volume, not agent count.

Complex Data Analysis Pipelines

Data analysis agents that process large datasets benefit from Agyn’s stateful serverless model. An agent can be triggered by a new data upload, process the data across multiple invocations (each spinning a fresh container), and persist intermediate results in the state store. The zero-trust networking ensures the agent can access internal databases without exposing them.

CI/CD Code Review Automation

Code review agents running Claude Code or Codex can be integrated into CI/CD pipelines. Each pull request triggers an agent invocation that reviews the code, posts comments, and updates the PR status — all within a fresh, isolated container that is destroyed after the review completes.

Internal Tool Access for Non-Technical Teams

Agyn’s Terraform-based agent definitions make it possible for platform teams to define agents that internal teams can use without writing code. A marketing team might have an agent that can access the CMS, analytics dashboard, and social media scheduler — all secured through MCP tool isolation and zero-trust networking.

Roadmap — Fine-Grained Access Control and Audit Logs

Agyn’s public roadmap includes two major features:

Fine-grained access control will allow organizations to define granular permissions for who can create, modify, and deploy agents. This is essential for enterprises that need to separate development, staging, and production agent environments with different access levels.

Audit logs will provide a complete record of every agent action — what tools were called, what data was accessed, what decisions were made. For regulated industries (finance, healthcare, legal), audit logs are not optional; they are a compliance requirement.

Both features are listed as “coming soon” on the Agyn blog and are expected to ship in late 2026.

Getting Started — Local Bootstrap and Demo Setup

Getting started with Agyn requires a Kubernetes cluster and Terraform installed. The quickstart guide on agyn.io walks through:

1. Installing the Agyn operator on your Kubernetes cluster
2. Configuring the Agyn Terraform provider
3. Defining your first agent as a Terraform resource
4. Running the agent and observing its output

The Community Edition supports local development with kind (Kubernetes in Docker) or minikube, making it possible to evaluate Agyn on a laptop before deploying to production.

Verdict — Who Should Use Agyn in 2026?

Agyn is a strong choice for organizations that are already running Kubernetes and need to deploy AI agents at scale with proper security, governance, and cost control. It is particularly well-suited for:

• Platform engineering teams that manage infrastructure for multiple agent workloads
• Organizations in regulated industries that need audit trails and access control (once roadmap features ship)
• Teams running bursty agent workloads where always-on agents would waste compute
• Enterprises with existing Terraform workflows who want to extend GitOps to agent management

Agyn is less suitable for teams that are still experimenting with agents and do not yet need production infrastructure. For those teams, starting with a framework like LangGraph or CrewAI and migrating to Agyn when scale demands it is a reasonable path.

The platform’s AGPL-3.0 license means organizations that want to embed Agyn in proprietary products need to consider the licensing implications, though the Community Edition is free for internal use.

Bottom line: Agyn fills a genuine gap in the AI agent ecosystem. It is not another framework — it is the infrastructure layer that frameworks have been missing. For teams that have outgrown notebook experiments and need to run agents like production services, Agyn is worth a serious look in 2026.

FAQ

What is Agyn and how does it work?

Agyn is an open-source, Kubernetes-native runtime for AI agents that launched in May 2026. It treats agents as infrastructure defined through Terraform resources, runs each agent invocation in a fresh container, isolates credentials at the MCP container level, and uses OpenZiti zero-trust networking for secure agent-to-service communication.

How is Agyn different from LangGraph or CrewAI?

Agyn operates at the infrastructure layer while LangGraph and CrewAI operate at the orchestration layer. Agyn handles deployment, scaling, security, and observability on Kubernetes. LangGraph and CrewAI handle agent reasoning, planning, and collaboration. The two approaches are complementary and can be used together.

Is Agyn free to use?

Yes, Agyn offers a free Community Edition under AGPL-3.0 that is fully functional for self-hosted deployments. Enterprise managed services with additional features like fine-grained access control, audit logs, and dedicated support are available for organizations that need them.

What agents does Agyn support?

Agyn ships with three pre-built agent init images: Claude Code (for complex reasoning and code generation), Codex (for fast code generation), and agn (Agyn’s own customizable agent loop). Teams can also build and deploy custom agent images.

Does Agyn work with any Kubernetes cluster?

Yes, Agyn is cloud-agnostic and runs on any Kubernetes cluster including AWS EKS, Google GKE, Azure AKS, on-premise deployments, and local development clusters like kind or minikube.