Securing Enterprise AI Agent Actions
Arcade raised $60 million to build a secure action layer, helping enterprises control and audit autonomous AI agents.
On June 19, 2026, Arcade announced a $60 million Series A funding round, led by SYN Ventures, to build a secure action layer for enterprise AI agents. This funding brings their total capital to $72 million. This investment directly addresses a major roadblock for businesses: the AI control gap. As companies transition autonomous agents from harmless test environments to real-world business operations, security must change. Giving an LLM access to your database or internal tools without a strict governance layer is a massive business risk.
Why enterprise AI agents need a secure action layer
AI agents have moved past basic support questions. Now they update databases and execute tasks across separate software systems. This shift from reading data to taking action changes security requirements completely. When an agent acts on behalf of a human user, it needs clear boundaries.
If an agent has access to a customer service tool, can it also access the payment processor connected to it? Without a dedicated security layer, the answer is often yes, because permissions are handled at the system level rather than the agent level. Most current setups rely on developer-written code inside individual applications to manage these permissions. This approach fails quickly as you add more agents and more integrations. A secure action layer acts as a gatekeeper. It ensures that whenever an agent wants to run a task, it has the explicit authority to do so. This layer checks permissions, limits what the agent can touch, and keeps a detailed log of every step.
Centralizing agent permissions over ad-hoc code
Arcade aims to solve this by centralizing permissions. Instead of writing custom security logic for every single tool or API connection, developers can manage access in one place. This centralization is what makes production deployments manageable for large enterprises.
And the benefits go beyond security. Centralized controls give security teams a clear view of what agents are doing across the entire company. Arcade's platform provides fine-grained authorization policies and detailed audit trails. If an agent makes an error or attempts an unauthorized action, the system flags it immediately. Security teams can see exactly which agent triggered the action, what prompt caused it, and what data was involved. This level of visibility is necessary for compliance and risk management in regulated industries. It also takes the burden off individual developers, who no longer have to design security frameworks from scratch for every new feature.
Moving AI systems from experimental pilots to production
At Algo & Art, we see this transition happening daily. Companies come to us with impressive pilot projects that they cannot deploy to production because their security teams block them. The security teams are right to do so. Without centralized governance, an autonomous agent is a liability.
We help enterprises build the operational plumbing that makes these systems reliable at scale. That means integrating tools like Arcade's secure action layer directly into the core architecture of your agentic workflows. By separating the intelligence of the LLM from the execution of the action, we create a safer system. The model can propose an action, but the security layer determines if that action is allowed to run. This separation of concerns is fundamental to building reliable enterprise software.
How we build secure agentic workflows at Algo and Art
Building production-grade AI systems requires more than just connecting APIs. It requires a deep understanding of how autonomous agents fail and how to protect against those failures. We design agent orchestration pipelines with security built into the foundation.
First, we enforce least-privilege access. An agent designed to update CRM records should never have access to billing systems, even if the underlying API key does. Second, we build human-in-the-loop triggers for high-risk actions. If an agent wants to refund a customer or delete a record, the system pauses and asks for human approval.
We also focus on reliability controls. Autonomous agents can get stuck in infinite loops or make repetitive API calls that drain budgets. We build guardrails that monitor agent behavior in real-time, shutting down runaway processes before they cause damage. Integrating a secure action layer is a key part of this strategy. It provides the infrastructure needed to enforce these rules consistently across every agent in your organization.
Managing the AI control gap in enterprise operations
The term "AI control gap" refers to the space between what an AI model can do and what an enterprise can safely allow it to do. Bridging this gap is not just about writing better prompts. It is about building infrastructure that treats AI models as untrusted users.
When we design agentic systems for our clients, we treat the AI as an external entity that requires continuous authorization. We do not assume the agent will behave perfectly. Instead, we build the system with the assumption that the agent will eventually make a mistake or receive a malicious prompt. By establishing a secure action layer, we ensure that even if the agent is compromised, the damage it can cause is limited. This approach protects sensitive company data while allowing businesses to use the full capabilities of autonomous workflows.
Frequently asked questions
What is a secure action layer for AI agents? A secure action layer is a dedicated infrastructure platform that manages permissions, authorization, and audit trails for autonomous AI agents. Instead of hardcoding security rules inside individual applications, it centralizes control to ensure agents only perform approved actions.
Why is ad-hoc security code a problem for enterprise AI? Writing custom security logic for each individual AI application is difficult to scale and hard to audit. It creates fragmented systems where security teams cannot easily track what permissions agents have or what actions they are taking across the organization.
How does Algo and Art help companies secure their AI workflows? We build the integration pipelines, guardrails, and orchestration layers that connect your AI agents to secure action systems like Arcade. Our team designs the security architecture so your autonomous workflows meet strict enterprise compliance standards.