Agyn

Agyn is the open-source management layer that takes AI agents off employee laptops and deploys them safely across the entire organization. It is the missing infrastructure layer for companies moving from AI experiments to production-grade agent deployments. Every agent runs in an isolated sandbox with secrets hidden from the model, ensuring sensitive data never leaks. Each team gets its own spend cap, role-based access, and a complete audit trail. Agyn works with any agent including Claude Code, Codex, and custom builds, and supports any model. It is Kubernetes-native and can be self-hosted or run in the cloud.

The platform solves the core friction points that stop enterprises from shipping AI agents today. Non-technical teams get agents they can actually use without security risks. Engineering maintains full oversight and control. Finance sees every dollar spent on tokens with per-agent tracking. IT stops worrying about shadow AI and unauthorized agent deployments. Agyn is built 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. With a 72.2% issue resolution rate on SWE-bench Verified and a #1 ranking among GPT-5-based systems, Agyn delivers production-ready agent management that scales across teams, environments, and compliance requirements.

Multi-Environment Deployment with Private Network Support

Deploy agents into any environment or private network, reaching internal services behind VPNs, VPCs, and firewalls. Agyn ships directly into your VPC in minutes, giving agents access to production databases, internal APIs, and corporate resources that were previously unreachable from local machines. This eliminates the common failure point where agents cannot connect to internal services because they run on employee laptops. Instant rollback capabilities ensure you can revert deployments instantly if issues arise.

Least Privilege Security with Policy Enforcement

Every agent runs under least privilege principles with static policies and a policy agent that inspects every tool call before execution. Secrets stay completely hidden from the model, defending against prompt injection attacks and sensitive data leaks. The policy gate reviews every action before it runs, with allowed, blocked, and escalated actions tracked in real time. Policies include least privilege enforcement, planner and executor split, sanitized feedback to strip injected instructions, and human escalation for ambiguous or high-risk actions.

Per-Agent Budget Tracking and Cost Attribution

Track spend across agents, teams, and workflows with granular per-agent tracking. Set budget limits, receive usage alerts, and keep AI costs under complete control. Finance teams get full visibility into every dollar spent on tokens, with cost attribution tied to specific agents, teams, and workflows. This eliminates the problem of runaway AI costs and gives organizations the ability to enforce financial governance without slowing down innovation.

Team Sharing with Role-Based Access Control

Give the right employees access to the right agents, share them safely across teams, and keep usage governed as adoption grows. Role-based access control ensures that only authorized users can deploy, modify, or interact with agents. Complete audit logs track every action taken by every user, providing full traceability for compliance and security reviews. As agent adoption scales across the organization, Agyn ensures governance keeps pace.

GitOps-Driven Agent and Policy Management

Declare agents, sandboxes, tools, MCPs, skills, and prompts entirely in code. Agents run consistently across every team and environment because their definitions are version-controlled and deployed through GitOps workflows. This infrastructure-as-code approach enables reproducibility, peer review, and automated deployment pipelines. Changes to agent configurations go through the same review and approval processes as any other production infrastructure change.

Enterprise Data Analysis Agents

Deploy data analysis agents that can access production databases behind corporate firewalls. A data analyst agent can connect to internal databases, run SQL queries, analyze Q1 sales data, and generate summary reports without ever exposing database credentials to the model. The agent runs inside the corporate network with least privilege access, ensuring it only reads the specific tables and columns it needs. Finance teams get automated reporting while security teams maintain full control.

Secure Code Review and Pull Request Management

Deploy a code reviewer agent that reads repositories, comments on pull requests, and sends email notifications, all within strict security boundaries. The policy gate ensures the agent never makes external calls or accesses resources outside its defined scope. When a code reviewer agent attempts to email a review summary to an external partner, the policy gate blocks it because sending email to external domains is not in scope. This keeps code review automation safe and controlled.

Customer Support Automation with Guardrails

Deploy support agents that read tickets, query internal databases, and send email replies within strict policy boundaries. The support agent can access customer records from internal databases and respond to tickets, but the policy gate blocks any attempt to query user records from a database that is not explicitly authorized. Human escalation triggers on ambiguous requests, ensuring complex or high-risk customer interactions receive human oversight.

Research and Intelligence Gathering with Controlled Web Access

Deploy research agents that browse the web, write summaries, and access internal knowledge bases. The policy gate enforces a planner and executor split, meaning the planning agent cannot directly execute actions. When an agent follows embedded instructions from a web page that attempt to escalate privileges or access restricted resources, the policy gate sanitizes the feedback and blocks the unauthorized action. This enables safe web research without exposing internal systems to external manipulation.

How does Agyn protect against prompt injection attacks?

Agyn defends against prompt injection by keeping secrets hidden from the model entirely. The policy agent inspects every tool call before execution, and the sanitize feedback policy strips injected instructions from model responses. If an attacker embeds instructions in web content that an agent reads, those instructions are removed before they can influence the agent's behavior. The planner and executor split ensures that even if a planning agent is compromised, it cannot directly execute actions.

Can Agyn work with any AI agent or model?

Yes, Agyn works with any agent including Claude Code, Codex, and custom builds. It also supports any model from any provider. The platform is model-agnostic and agent-agnostic, meaning you can use your preferred AI tools without being locked into a specific vendor. This flexibility allows organizations to standardize on a single management layer while using different agents and models for different use cases.

How does Agyn handle cost tracking and budgeting?

Agyn provides per-agent tracking with budget limits, usage alerts, and cost attribution. Each agent has its own spend cap that can be set by administrators. When an agent approaches its budget limit, alerts are triggered. Finance teams get full visibility into every dollar spent on tokens, with costs attributed to specific agents, teams, and workflows. This granular tracking prevents runaway costs and enables financial governance across the organization.

What deployment options does Agyn support?

Agyn supports both self-hosted and cloud deployments. It is Kubernetes-native and can be deployed into your VPC in minutes using a simple bootstrap script. For self-hosted deployments, you clone the repository, run the apply script, and Agyn deploys into your Kubernetes cluster. Cloud deployment options are also available for organizations that prefer a managed service. Both options support private networks, VPNs, and VPCs for reaching internal services.