Secure the agents your teams build
Lunar governs custom AI agents built by your organization, from internal chatbots and search agents to enterprise-grade agentic products.
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The Problem
Where custom agents break down at scale
Teams quickly move from demos to production when building internal chatbots, search agents or customer-facing agentic workflows. But once agents start accessing real data and real tools, new problems emerge.
Unbounded tool access
Agents often see too many tools. Overlapping capabilities confuse models, degrade accuracy and waste context. Tool exposure is rarely revisited after the first prototype.
Tool behavior you don’t fully control
Third-party and open-source MCP tools return verbose responses, expose unsafe parameters or evolve over time. Forking them breaks upgrade paths, but trusting them as-is introduces risk.
No unified view of agent behavior
Once multiple agents are deployed, activity is scattered across logs, tools and environments. There’s no single place to understand what agents are actually doing.
Governance bolted on too late
Security and compliance checks are usually added after agents are already live. By then, permissions are broad, behavior is inconsistent and changes are risky.
Lunars’s Approach
Where governance belongs: the agent boundary
Lunar operates as a runtime governance layer for custom agents built by your organization. Every tool call, MCP interaction and external action is evaluated against policy before execution.
How Lunar works, high-level Agent boundary enforcement
Lunar sits between agents and MCP servers, APIs and internal systems. It inspects intent, permissions and context before allowing actions to proceed.
User-delegated and autonomous agents
Whether an agent acts for a user or as a standalone service, Lunar applies the correct controls automatically
Policy-driven agent behavior
Agent behavior can be adjusted live through policies, from tool usage to access scope, without changing agent code.
Capabilities
Operational controls for agent builders
Once agents are live, builders need operational controls, not more prompts. Lunar gives agent teams the primitives to shape behavior, observe execution and enforce reliability at runtime.
Purpose-built tool groups
Create dedicated tool groups for specific agent actions, exposing only what each agent needs.
Native metrics and tracing (OTEL)
Collect OTEL-compatible metrics and traces for agent actions, tool calls and execution paths.
Deterministic tool execution
Chain tools into fixed, policy-controlled flows to ensure predictable agent behavior.
Runtime tool customization
Shape tool inputs, outputs and parameters through policy, without changing code.
Govern agents wherever they’re built
Explore capabilities built for teams shipping real agents.
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