01
Native access to infrastructure
Connect databases, servers, cloud, and Kubernetes through typed tools. Calls run in your environment and credentials never appear in agent commands.
Secure AI for disparate systems.
Connect AI agents securely to VMs, databases, Kubernetes, RDP, legacy apps, SaaS, and other non-standard systems, with governed access to each one.
Every action is just-in-time, least-privilege access, checked against policy, and recorded end to end.
Connect AI agents securely at scale to every business system.
One catalog for databases, cloud, Kubernetes, SaaS, and legacy apps, with execution in your environment and credentials out of commands.
02
MCP for modern applications
Give agents access to 350+ modern tools, including Salesforce, Slack, HubSpot, and Google, through a standard interface with permissions scoped tool by tool.
03
Browser agents for disconnected apps
Complete tasks in vendor portals, legacy admin tools, and internal applications through a secure browser runtime, with no API or custom integration required.
One governed access layer for every agent, across databases, SaaS, and browser-only legacy systems.
Govern every AI agent at runtime.
Enforce access, approvals, and operating limits outside the model. Every action is evaluated before execution, across every harness, tool, and environment.
- Policy outside the model
- Checked before execution
- Auditable by default
Specify exactly which systems, resources, and actions each agent can access. Policies are written in Rego, evaluated by Open Policy Agent, and versioned in Git alongside the rest of your infrastructure.
Every action is checked against policy before it executes. The model cannot bypass these rules, so prompt injection, goal drift, or a misunderstood task cannot take an agent beyond its granted permissions.
Require approval before sensitive or irreversible actions execute, while routine work continues automatically. Start agents in read-only mode, then progressively increase autonomy as you build confidence.
Capture the actor, request, policy revision, decision, and outcome for every action. Give security and compliance teams an evidence trail they can query, export, and retain on their terms.
Run thousands of agents within your infrastructure.
ONE-TIME · SCHEDULED · LONG-HORIZON
From one-off tasks to long-running jobs
Run one-time tasks, recurring schedules, and long-horizon operations remotely, independent of the person who started them.
MANAGED · SELF-HOSTED · KUBERNETES
Deploy where your data lives
Start in Industric's managed environment or run inside your own Kubernetes clusters with data kept in your infrastructure.
Questions, answered
Production AI agent infrastructure, explained.
What is Industric?
Industric is infrastructure for building, running, and governing fleets of production AI agents across critical business systems.
How does Industric connect AI agents to business systems?
Industric provides typed native tools for databases, servers, cloud, and Kubernetes; MCP access to modern applications; and browser agents for systems without APIs.
How does Industric govern AI agent actions?
Every action is checked against policy before execution. Teams can enforce scoped access, require human approval for sensitive actions, and retain an audit trail of decisions and outcomes.
Where do Industric agents run?
Agents can run in an Industric-managed environment or inside a customer's Kubernetes clusters so data stays in the customer's infrastructure.
Which agent harnesses does Industric support?
Industric supports OpenAI Codex, Claude Code, and custom agent harnesses under the same tools, policies, approval gates, and audit controls.