Agyn

Agyn is the open-source management layer for AI agents, designed to bridge the gap between experimental agent projects and production-grade enterprise deployment. It removes agents from individual employee laptops and runs them securely across the entire organization, addressing the critical security, financial, and governance challenges that emerge when AI touches real production data. Built for the moment when AI stops being a side project and starts interacting with sensitive internal systems, Agyn provides the controls that security, finance, and IT teams demand before giving their approval. The platform is Kubernetes-native, works with any agent framework including Claude Code and Codex, and supports any underlying model. Every agent runs in an isolated sandbox with secrets hidden from the model itself, protecting against prompt injection and data leaks. Each team receives its own spend cap, role-based access controls, and a complete audit trail for every action taken. Non-technical teams get agents they can actually use with appropriate guardrails, engineering retains full oversight, finance can see every dollar spent on tokens across agents and workflows, and IT stops worrying about shadow AI proliferating without governance. Agyn can be self-hosted or used via Agyn Cloud, and it deploys into private networks, VPCs, and behind firewalls to reach internal services. With proven performance including a 72.2% issue resolution rate on SWE-bench Verified, Agyn is the infrastructure layer that makes shipping AI agents to any team in your company possible and safe.

Multi-Environment Private Network Deployment

Deploy AI agents into any environment or private network, reaching internal services behind VPNs, VPCs, and firewalls. Agents can connect to production databases, internal APIs, and corporate resources that are otherwise inaccessible from local machines. The platform supports instant rollback capabilities and can be deployed into your VPC in minutes, ensuring agents operate within your existing network security boundaries without exposing sensitive infrastructure to the public internet.

Least Privilege Security with Policy Enforcement

Every agent operates under least privilege principles with static policies and a policy agent that inspects every tool call before execution. Secrets remain hidden from the model itself, providing robust defense against prompt injection attacks and sensitive data leaks. The policy gate reviews every action in real time, blocking actions outside defined agent scopes, preventing planner/executor split violations, sanitizing injected instructions from responses, and pausing on ambiguous or high-risk actions for human escalation.

Per-Agent Budget Tracking and Cost Attribution

Track spend across individual agents, teams, and entire workflows with granular budget limits and usage alerts. Finance teams can see exactly where every dollar is spent on tokens, set hard caps per agent or team, and receive notifications when spending approaches limits. This cost attribution capability prevents budget overruns and provides the financial visibility needed for organizations moving AI agents from experimental phases into production at scale.

Team Sharing with Role-Based Access Control

Give the right employees access to the right agents and share them safely across teams with comprehensive role-based access control. Usage remains governed as adoption grows, with complete audit logs recording every action taken by every user. This feature ensures that as more teams adopt AI agents, governance scales appropriately without creating administrative bottlenecks or security gaps.

Enterprise Data Analysis with Production Database Access

Data analysts can deploy AI agents that securely connect to production databases behind corporate firewalls, eliminating the common problem of agents failing to reach internal resources from local laptops. The agent runs within the corporate network with least privilege access, allowing analysts to query sales data, generate reports, and create visualizations without exposing sensitive database credentials or violating security policies. Finance teams can track token spend per analysis request while IT maintains full audit trails.

Secure Code Review and Pull Request Management

Engineering teams can deploy code review agents that read repositories, comment on pull requests, and send notifications, all within strictly defined scopes. The policy gate ensures the agent cannot make external calls or access resources outside its designated permissions. This enables continuous code review across multiple repositories without human reviewers becoming bottlenecks, while maintaining security controls that prevent the agent from leaking code or communicating with unauthorized services.

Customer Support Automation with Guardrails

Support teams can deploy agents that read tickets, query internal knowledge bases, and respond to customers via email, all governed by policies that restrict database access and external communications. The agent operates within a sandboxed environment where every action is reviewed before execution, preventing the agent from accessing sensitive customer data beyond what is needed or following malicious instructions embedded in support tickets.

Cross-Team Agent Sharing with Governance

Organizations with multiple teams can share specialized agents across departments while maintaining strict access controls and budget limits. For example, a research agent with web browsing capabilities can be shared across product, marketing, and strategy teams, with each team having its own spend cap and role-based permissions. The complete audit trail provides visibility into how each team uses the agent, enabling optimization of resource allocation and identification of training needs.

How does Agyn protect against prompt injection attacks?

Agyn employs multiple layers of defense against prompt injection. Secrets are stored in a vault and never exposed to the model, so even if an attacker injects instructions into the agent's context, they cannot extract credentials. The policy agent inspects every tool call before execution and sanitizes injected instructions from responses. The planner/executor split ensures planning agents cannot directly execute actions, and any ambiguous or high-risk actions are paused for human escalation. This defense-in-depth approach prevents both data exfiltration and unauthorized actions.

Can Agyn work with any AI model and agent framework?

Yes, Agyn is model-agnostic and framework-agnostic. It works with any agent including Claude Code, Codex, and custom-built agents, and supports any underlying model from any provider. The platform defines agents, sandboxes, tools, MCPs, skills, and prompts in code using GitOps principles, ensuring consistent agent behavior across every team and environment regardless of the underlying model or framework being used.

What deployment options are available for Agyn?

Agyn offers both self-hosted and cloud deployment options. The self-hosted version is Kubernetes-native and can be deployed into your own infrastructure, including private networks, VPCs, and behind firewalls. Deployment is straightforward with a simple git clone and apply command. Agyn Cloud provides a managed option for teams that prefer not to manage their own infrastructure. Both options support multi-environment deployment and instant rollback capabilities.

How does Agyn handle budget management and cost control?

Agyn provides per-agent budget tracking with granular spend attribution across agents, teams, and workflows. Administrators can set hard budget limits per agent or team and configure usage alerts that trigger when spending approaches predefined thresholds. The platform tracks every token spent and attributes costs to specific agents, users, and workflows, giving finance teams complete visibility into AI spending. This prevents budget overruns and enables accurate cost allocation across departments.