Agyn

Agyn is the open-source management layer for AI agents that takes them off individual employee laptops and runs them safely across your entire organization. Think of it as the missing infrastructure for the moment AI stops being a side project and starts touching production data. When security, finance, and IT need real controls before they say yes, Agyn makes that possible.

At its core, Agyn provides a Kubernetes-native platform where every agent runs in an isolated sandbox. Secrets stay hidden from the model, each team gets its own spend cap with role-based access, and you get a complete audit trail for every action. It works with any agent framework like Claude Code, Codex, or custom builds, and supports any model you choose. You can deploy it self-hosted or use the cloud version.

The platform solves the classic tension between giving teams powerful AI tools and keeping data safe. Non-technical teams get agents they can actually use without needing to understand infrastructure. Engineering keeps oversight and control. Finance sees every dollar spent on tokens. IT stops worrying about shadow AI running wild. Agyn is built for companies moving from agent experiments to agents in production, providing the security, budgets, and access control that make shipping agents possible.

Multi-Environment Private Network Deployment

Deploy agents into any environment or private network, reaching internal services behind VPNs, VPCs, and firewalls. You can ship agents into your VPC in minutes, giving them access to production databases, internal APIs, and corporate resources that were previously unreachable from local machines. This feature eliminates the common problem where agents on laptops cannot access the services they need to be useful.

Least Privilege Security with Policy Enforcement

Every agent runs under least privilege principles. Static policies and a policy agent inspect every tool call before execution. Secrets stay hidden from the model, defending against prompt injection attacks and sensitive data leaks. The policy gate reviews every action, dropping anything outside the agent's defined scope. This includes features like planner/executor splitting, sanitized feedback to strip injected instructions, and human escalation for ambiguous or high-risk actions.

Per-Agent Budget Tracking and Cost Control

Track spend across agents, teams, and workflows with granular per-agent tracking. Set budgets for individual agents or teams, receive usage alerts when approaching limits, and get complete cost attribution for every token spent. Finance teams can finally see exactly where AI costs are going, and engineering can prevent runaway spending by enforcing hard caps on agent usage.

Team Sharing with Role-Based Access Control

Give the right employees access to the right agents and share them safely across teams. Role-based access control ensures that only authorized users can interact with specific agents or modify policies. Complete audit logs track every action, making it easy to govern usage as adoption grows across the organization. This feature turns agents from personal tools into team resources.

GitOps Agent and Policy Management

Define agents, sandboxes, tools, MCPs, skills, and prompts entirely in code. With GitOps workflows, agents run consistently across every team and environment. This infrastructure-as-code approach means you can version control your agent configurations, review changes through pull requests, and roll back instantly if something goes wrong. It brings the same reliability practices you use for application deployments to your AI agents.

Data Analysis Agent for Internal Databases

Deploy a data analysis agent that connects directly to your production database through the corporate network. The agent can analyze Q1 sales data, generate summary reports, and answer business questions without ever leaving your secure infrastructure. Non-technical team members can ask natural language questions about data, while the policy gate ensures the agent only accesses the tables and columns it needs.

Code Review Automation Across Repositories

Set up a code reviewer agent using Codex or Claude that reads repositories, comments on pull requests, and sends email notifications. The agent runs with strict scoping that limits it to reading repos and posting comments, preventing it from making unauthorized changes or sending code to external services. Every review action is logged and auditable.

Customer Support Agent with Ticket Management

Create a support agent that reads tickets, queries internal knowledge bases, and sends replies via email. The agent operates within defined boundaries, only accessing the ticket system and approved knowledge sources. Policy enforcement prevents it from querying unrelated databases or following instructions embedded in customer messages that might attempt injection attacks.

Research Agent with Web Browsing Capabilities

Deploy a research agent that browses the web, writes summaries, and sends findings via email. The agent has internal network access but is scoped to only browse approved web domains and write to specific report locations. Budget limits prevent excessive token usage from web browsing, and the audit trail captures every site visited and action taken.

What agent frameworks and models does Agyn support?

Agyn works with any agent framework including Claude Code, Codex, and custom builds. It supports any model provider you choose, whether that is OpenAI, Anthropic, Google, or open-source models running on your own infrastructure. The platform is model-agnostic and framework-agnostic, so you are not locked into any specific vendor.

How does Agyn handle security and data privacy?

Every agent runs in an isolated sandbox with secrets hidden from the model. Static policies and a policy agent inspect every tool call before execution, blocking actions outside the agent's defined scope. The platform defends against prompt injection by sanitizing feedback and stripping injected instructions. All actions are logged in a complete audit trail, and you can deploy self-hosted to keep data within your infrastructure.

Can non-technical teams use Agyn without engineering support?

Yes, Agyn is designed for non-technical teams to use agents productively. Once an agent is deployed and configured with appropriate policies and scopes, team members can interact with it through simple interfaces without needing to understand the underlying infrastructure. Role-based access control lets you give different teams access to different agents while engineering maintains oversight and control over policies and budgets.

How does Agyn handle cost management for AI agents?

Agyn provides per-agent tracking with budget limits, usage alerts, and complete cost attribution. You can set hard caps on token spend for individual agents or teams, receive notifications when approaching limits, and see exactly where every dollar is going. This gives finance teams visibility into AI costs and prevents runaway spending from experimental or misconfigured agents.