Build, Secure, and Monitor your AI Workforce. The centralized platform for managing autonomous agents at scale.
Works with your existing stack
An Agent Control Layer is infrastructure that provides governance, identity, and policy enforcement for AI agents in production. It operates as a control plane—owning configuration, permissions, and observability—while execution remains in your existing runtime (LangGraph, CrewAI, or custom code).
Unlike agent frameworks that handle how agents run, a control layer manages what agents are allowed to do, which resources they can access, and how their behavior is monitored and audited.
Agent-as-Principal is a security architecture that treats AI agents as first-class principals in identity and access management systems—alongside users and services.
Each agent has a unique, verifiable identity using standards like SPIFFE.
Fine-grained RBAC per agent. Control which tools, APIs, and data each agent can access.
Every agent action is logged with unforgeable attribution.
AgentControlLayer is a control plane, not a runtime. This architectural separation is fundamental.
| Aspect | Runtime | Control Plane |
|---|---|---|
| Primary Function | Execute agent logic | Govern agent behavior |
| Owns | Prompts, tools, orchestration | Permissions, policies, audit logs |
| Answers | "How does the agent run?" | "What can the agent do?" |
| Analogy | Docker | Kubernetes |
Don't start from scratch. We have pre-built, enterprise-grade architectures for the most common high-value use cases.
Autonomous prospecting. It scrapes leads, researches their recent news, and drafts hyper-personalized emails that actually get replies.
Stop drowning in tickets. This agent reads every incoming request, tags it by urgency, and drafts a reply for your team to one-click approve.
Your 24/7 researcher. Give it a topic or a competitor, and it scrapes the web, summarizes findings, and updates your internal databases.
You don't need another "Agent Builder" tool. You need a partner who understands operational complexity.
Most internal demos look great but break instantly in the real world.
We implement permissions, audit logs, and PII redaction.
Agents drift over time. We provide weekly optimization loops.
We analyze your workflows and identify high-ROI opportunities.
Our architects build your agents on the platform.
We deploy to production and train your team.
We stay on as your AgentOps partner.
Teams adding agents into their SaaS products.
Central teams supporting multiple agent use cases.
Shops building agents for clients.
Straightforward answers about agent control planes and how AgentControlLayer fits into your stack.
An Agent Control Layer is infrastructure that provides governance, identity, and policy enforcement for AI agents in production. It operates as a control plane—owning configuration, permissions, and observability—while execution remains in your existing runtime like LangGraph, CrewAI, or custom code.
A runtime (like LangGraph or CrewAI) handles how agents execute—managing prompts, tool calls, and orchestration logic. A control plane manages what agents are allowed to do, which resources they can access, and how their behavior is audited. This separation mirrors how Kubernetes orchestrates containers without replacing Docker.
Traditional IAM systems handle users (humans) and services (deterministic code). Agents are a new principal type—they make autonomous decisions at machine speed with probabilistic behavior. They need cryptographic identity, granular permissions per agent, and complete audit trails of every action they take.
Human-in-the-Loop is an architectural pattern where agent workflows can pause execution to request human approval before taking sensitive actions. A proper HITL implementation includes approval queues, reviewer routing, state persistence during the pause, and timeout handling.
LangChain and CrewAI are agent frameworks—they help you build and run agents. AgentControlLayer is a control plane—it governs, secures, and observes agents built with any framework. You use both together: your framework for execution, ACL for enterprise-grade management.
Prompt versioning treats your agent's instructions like code—with version control, history, rollback capability, and environment promotion (dev → staging → production). This eliminates the chaos of prompt changes breaking production agents and enables systematic debugging when behavior changes.
Agent-as-Principal is a security model that treats AI agents as first-class principals in identity systems, alongside users and services. Each agent gets cryptographic identity, granular RBAC permissions, and complete audit trails—enabling enterprise-grade security for autonomous AI systems.
One AgentOps control plane to build, secure, and observe your agent fleet.
Stop pasting strings into code. Our visual Prompt Builder UI allows you to design, test, and version complex prompts with variables, conditional logic, and model comparisons side-by-side.
Treat agents as first-class citizens with their own IAM roles. Manage permissions, enforce budget limits, and maintain complete audit trails of every decision your AI makes.
Bring DevOps discipline to LLMs. Version control your entire agent configuration—workflows, prompts, and RAG settings. Implement Human-in-the-Loop (HITL) checkpoints before critical actions.
Ready to deploy agents that actually work? We are accepting a limited number of enterprise clients for our Managed Agent Program. Get a custom roadmap, a dedicated AI Architect, and access to the AgentControlLayer platform.