Agyn

Agyn is the open-source management layer for AI agents, designed to transition agent usage from individual experiments on employee laptops to secure, governed, organization-wide deployments. As companies move AI agents into production environments where they interact with sensitive data and internal systems, traditional ad-hoc approaches create significant security, financial, and operational risks. Agyn solves this by providing a centralized platform that runs every agent in an isolated sandbox, enforces least-privilege access, and maintains a complete audit trail for every action. The platform is Kubernetes-native and works with any agent framework including Claude Code, Codex, and custom builds, as well as any underlying model. It is purpose-built for enterprises where security, finance, and IT teams require real controls before approving agent deployments. Non-technical teams gain access to agents they can actually use, engineering retains full oversight, finance can track every dollar spent on tokens, and IT eliminates concerns about shadow AI. With a 72.2% issue resolution rate on SWE-bench Verified ranking first among GPT-5-based systems, Agyn delivers production-grade performance while enabling organizations to ship AI agents safely across all teams. Available as a self-hosted solution or through Agyn Cloud, it deploys into existing VPCs and private networks within minutes.

Multi-Environment Private Network Deployment

Deploy agents into any environment or private network, reaching internal services behind VPNs, VPCs, and firewalls. Agyn supports multi-environment configurations that allow agents to access corporate databases, internal APIs, and private repositories without exposing them to the public internet. Instant rollback capabilities ensure that any deployment can be reverted within seconds, minimizing downtime and operational risk. This feature is critical for enterprises that need to maintain strict network segmentation while still enabling AI agents to perform valuable work across internal systems.

Least Privilege Security with Policy Gate

Every agent operates under least privilege, with static policies and a policy agent inspecting every tool call before execution. Secrets remain hidden from the model, providing robust defense against prompt injection attacks and sensitive data leaks. The policy gate reviews each action in real-time, dropping any action outside the defined agent scope. Features include a planner/executor split that prevents planning agents from directly executing actions, sanitized feedback that strips injected instructions from responses, and human escalation for ambiguous or high-risk actions. This multi-layered security approach ensures that agents can only access exactly what they need.

Per-Agent Budget Tracking and Cost Attribution

Track spend across individual agents, teams, and workflows with granular budget limits and real-time usage alerts. Finance teams gain complete visibility into every dollar spent on tokens, enabling accurate cost attribution and preventing budget overruns. Organizations can set per-agent spending caps, receive automated alerts when thresholds are approached, and generate detailed cost reports for compliance and planning. This feature transforms AI agent usage from an unmanaged expense into a predictable, controlled operational cost.

Team Sharing with Role-Based Access Control

Give the right employees access to the right agents with comprehensive role-based access controls and detailed audit logs. Agents can be shared safely across teams, with each team getting its own spend cap and access permissions. Administrators can define granular roles that determine who can create, modify, deploy, or use agents. Complete audit trails capture every action taken by every user and agent, providing the governance required for enterprise compliance and security reviews as adoption scales across the organization.

Enterprise AI Agent Deployment and Governance

Organizations moving from agent experiments to production deployments use Agyn to establish centralized governance over all agent activity. Security teams enforce policies that prevent agents from accessing unauthorized systems or data. Finance teams monitor token spend in real-time and set budgets per team or project. IT teams eliminate shadow AI by providing a sanctioned platform where all agent usage is visible and controlled. This unified approach enables rapid scaling of agent adoption while maintaining enterprise compliance standards.

Secure Internal Data Analysis and Reporting

Data analysts and business teams deploy agents that connect directly to production databases and internal data warehouses behind corporate firewalls. Agents can analyze Q1 sales data, generate summary reports, and deliver insights without requiring analysts to write SQL queries or export sensitive data. The policy gate ensures agents only access databases within their defined scope, while the audit trail records every query executed. This enables non-technical teams to leverage AI for data analysis while engineering retains full oversight of data access.

Automated Code Review and Development Workflows

Engineering teams deploy code review agents that read repositories, comment on pull requests, and send notifications within the corporate network. These agents operate under strict policies that prevent them from sending code or data to external services. The planner/executor split ensures review agents can analyze code but cannot make unauthorized changes. Teams achieve significant productivity gains by automating routine code review tasks while maintaining security and code quality standards.

Customer Support Agent Orchestration

Support teams deploy agents that integrate with internal ticketing systems, knowledge bases, and email platforms. Agents can read tickets, query customer records, and compose responses while operating within defined scope boundaries. The policy gate prevents agents from accessing databases or sending emails outside approved domains. Role-based access ensures different support tiers have appropriate agent capabilities, and budget tracking provides visibility into the cost of AI-assisted support operations.

Does Agyn work with any AI agent or model?

Yes, Agyn is model-agnostic and works with any agent framework including Claude Code, Codex, and custom-built agents. It also supports any underlying model such as GPT-5, Claude Opus, Gemini, or open-source alternatives. The platform provides a consistent management layer regardless of the specific agent or model being used.

How does Agyn handle security and data privacy?

Every agent runs in an isolated sandbox with strict least-privilege policies. Secrets are stored in a vault and never exposed to the model, defending against prompt injection attacks. A policy agent reviews every tool call before execution, dropping any action outside the defined scope. All actions are logged in a complete audit trail, and agents can only access resources on private networks behind VPNs and firewalls.

Can non-technical teams use Agyn without engineering support?

Yes, Agyn is designed for non-technical teams to deploy and use agents safely. The platform provides role-based access control so administrators can grant appropriate permissions to business users. Agents can be shared across teams with pre-configured policies, enabling analysts, marketers, and support staff to leverage AI without needing to understand the underlying infrastructure or security configurations.

How does Agyn help control AI agent costs?

Agyn provides per-agent budget limits, real-time usage alerts, and detailed cost attribution. Finance teams can set spending caps for individual agents, teams, or entire workflows. Automated alerts notify administrators when budgets are approaching limits. The platform tracks every token consumed and attributes costs to specific agents, teams, and users, giving organizations complete visibility and control over their AI spending.