Agyn

Agyn is an open-source, Kubernetes-native management layer designed for organizations that are transitioning from experimental AI agent projects to production-grade, enterprise-wide deployments. It addresses the critical security, governance, and operational challenges that arise when AI agents, such as Claude Code or Codex, need to interact with sensitive production data, internal networks, and real business workflows. The platform acts as a centralized control plane, lifting agents off individual employee laptops and running them in isolated, secure sandboxes across the entire organization. For non-technical teams, Agyn provides ready-to-use agents with role-based access and spend caps, making AI tools accessible without compromising security. For engineering, IT, and finance departments, it offers granular oversight, including per-agent budget tracking, complete audit trails, secret vaults that keep credentials hidden from the model, and static policy enforcement that reviews every tool call before execution. Agyn works with any agent framework and any large language model (LLM), supporting both self-hosted deployments within a company's own VPC and a cloud-based offering. Its design is specifically built for the moment when AI stops being a side project and begins touching production data, providing the security, financial, and IT controls necessary for enterprise approval. By offering features like least privilege access, prompt-injection defense, multi-environment support, and GitOps-driven agent definitions, Agyn enables companies to safely ship AI agents to any team, ensuring that adoption scales without introducing shadow AI or uncontrolled costs.

Multi-Environment and Private Network Support

Agyn allows organizations to deploy AI agents into any environment, including private networks, VPNs, VPCs, and behind firewalls. This feature is critical for agents that need to access internal databases, corporate APIs, or other sensitive services that are not exposed to the public internet. Deployment is streamlined and can be completed in minutes, with support for instant rollback, ensuring that agents operate securely within the company's existing network infrastructure.

Least Privilege and Policy Enforcement

Every agent running on Agyn operates under a strict least-privilege model. Static policies and a dedicated policy agent inspect every tool call an agent makes before it is executed. Secrets and credentials are stored in a secure vault and are never exposed to the underlying model, defending against prompt injection attacks and accidental data leaks. Actions that fall outside the predefined scope of an agent are automatically blocked, and high-risk or ambiguous actions can be escalated for human review.

Per-Agent Budget Tracking and Cost Control

Agyn provides detailed cost attribution for every agent, team, and workflow. Finance teams and engineering managers can set budget limits for individual agents or groups of agents, receive real-time usage alerts, and track token spend across the entire organization. This granular financial oversight prevents runaway costs and ensures that AI adoption remains within budget, addressing a primary concern for CFOs and IT leaders.

Role-Based Access Control and Audit Logs

The platform enables secure team sharing of agents through role-based access control. Administrators can define who has access to which agents, assign different permission levels, and maintain a complete audit trail of all agent activity and user actions. This governance framework is essential as agent adoption grows across different departments, ensuring that sensitive agents are only accessible to authorized personnel and that all usage is fully transparent.

Secure Data Analysis Across Corporate Networks

A data science team needs to deploy an agent that can query a production database located within a corporate VPC to generate quarterly sales reports. Using Agyn, they can deploy the agent into the private network, granting it read-only access to the specific database tables. The policy gate ensures the agent cannot execute write commands or send data to external services. Non-technical stakeholders can then interact with the agent through a secure interface, receiving analysis without ever needing direct database credentials.

Automated Code Review with Controlled Permissions

An engineering team wants to use an AI agent to automatically review pull requests on their private GitHub repositories. With Agyn, they define an agent with a scope limited to reading repositories and posting comments. The agent's secret vault holds the GitHub token, which is never visible to the model. A static policy blocks any attempt by the agent to merge code, send emails, or access external websites. The entire review process is logged, providing a clear audit trail for compliance.

Multi-Department Customer Support Agent

A customer support team wants an AI agent that can read support tickets, query a knowledge base, and draft email replies. Agyn allows the support manager to deploy this agent with a specific budget cap and role-based access, so only support staff can use it. The policy gate ensures the agent can only send emails to internal company domains and cannot query user records from the HR database. Finance receives automated alerts when the agent approaches its monthly token limit.

Controlled Web Research for Strategic Planning

A strategy team needs an agent that can browse the web to research market trends and summarize findings. Agyn deploys this agent with a scope that permits web browsing and writing summaries, but explicitly blocks it from sending emails or accessing internal databases. The policy agent sanitizes feedback from web pages to strip out any embedded instructions, preventing prompt injection attacks. The team can share the agent across the department, with each member having their own usage logs and the department having a shared spend cap.

How does Agyn protect against prompt injection attacks?

Agyn employs multiple layers of defense against prompt injection. First, all secrets and credentials are stored in a secure vault and are never passed to the model, so even if an injection occurs, credentials remain safe. Second, a policy agent inspects every tool call before execution, blocking any action that falls outside the defined scope. Third, the platform sanitizes feedback from external sources, stripping out potentially malicious instructions embedded in web pages or documents before they reach the agent's context.

Can Agyn work with any AI agent framework or model?

Yes, Agyn is designed to be model-agnostic and framework-agnostic. It works with popular agents like Claude Code and Codex, as well as custom-built agents using any framework. The platform supports any large language model, including GPT-5, Claude Opus, Gemini, and open-source alternatives. This flexibility allows organizations to standardize on a single management layer while using the best models for each specific task.

What does it mean that Agyn is Kubernetes-native?

Being Kubernetes-native means Agyn is built from the ground up to run on Kubernetes clusters, leveraging its orchestration, scaling, and networking capabilities. This allows organizations to deploy agents using their existing Kubernetes infrastructure, integrate with their CI/CD pipelines, and manage agents as part of their standard DevOps workflows. Deployment is simplified through a Git-based workflow (GitOps), where agents, sandboxes, tools, and policies are all defined in code.

How does Agyn handle budgeting and cost control for AI agents?

Agyn provides per-agent and per-team budget tracking. Administrators can set hard budget limits for individual agents or groups of agents, and the platform will automatically block execution once the limit is reached. Real-time usage alerts can be configured to notify finance or engineering teams when spending approaches a threshold. All costs are attributed to specific agents, users, and workflows, providing a complete picture of AI spending across the organization.