Coverage for Leading AI Coding Agents
One runtime for supported AI coding agents and development workflows, without modifying the agent or changing source code.
Kona for Agents
VS Code
Claude Code
GitHub Copilot
Cursor
CodexCodex CLI
Real-Time Governance for AI Coding Agents
Your developers are using AI agents to write, run, and ship code. Kona for Agents inspects every action before execution, filters sensitive data before it reaches the model, and signs every event for audit.
VS CodeClaude CodeGitHub CopilotCursorCodexCodex CLIGovern agents before execution, not after the damage
KonaSense sits between the agent and your development environment. Every action can be inspected, signed, and evaluated against policy in real time.
Real-Time Policy Enforcement
Shell commands, file operations, web fetches, and MCP calls are inspected before execution. Allow, deny, or redact. Tool responses are filtered before they reach model context.
Cryptographic Integrity
Every governed event is wrapped in an ECDSA-signed envelope so you get tamper-resistant evidence, device identity, and non-repudiation.
Multi-Layer Detection
Combined pattern, semantic, and classifier-based detection running in parallel. AI arbitration activates only when signals conflict, eliminating false positives without inflating inference cost.
Built into the agent, not around it
Every action a coding agent wants to take passes through your company's policy before it happens, and gets recorded. Kona for Agents does not replace the agent or sit in the network path. It plugs into the official extension points that Claude Code, Cursor, GitHub Copilot and Codex already expose, so each command, file edit, web request and prompt can be approved or blocked by your rules. Works on macOS, Windows, Linux and ChromeOS.
The path of an actionThe agent wants to act
Before running a command, editing a file or fetching a URL, the agent checks with Kona for Agents.
Kona packages and signs it
A small helper describes the action, signs it with the identity of that machine and sends it to the control plane.
Your policies decide
The same rules you wrote in the dashboard evaluate the action. Deterministic rules, not a model guessing, so the answer is predictable and explainable in an audit.
Allow or block, with the reason
The agent gets a clear yes or no. If blocked, the developer sees which policy stopped it, right inside the agent. The event lands in your dashboard and your SIEM.
No proxy, no interception
Kona for Agents uses the hooks each agent provides for exactly this purpose. No man-in-the-middle, no network rewriting, no forked agent.
Nothing running in the background
There is no resident process on the developer's machine. The helper is called by the agent only at the moment of an action, answers, and exits. When nobody is using AI, it uses nothing.
One rule, every supported agent
Policies live in the control plane, never on the workstation. Change a rule in the dashboard and it applies to every developer and every supported agent within minutes, with no reinstall. Developers cannot edit rules locally.
Fail-open by design. If the control plane cannot be reached, the developer is never blocked by us: the action proceeds and the agent shows a visible notice that policy was not checked.
See what governance looks like in practice
Open Claude Code and ask it to download and run an installer from outside your organization. Watch the agent stop with "blocked by policy: Unauthorized file transfer tools", then see the signed event appear in the dashboard seconds later. The dashboard is where the rule is written. The agent is where it lives.
Built for the enterprise rollout path
Visibility first, enforcement second. Start in observability mode to see what your developers and AI tools are actually doing. Layer in policy where the real risk lives. Most customers move from visibility to active enforcement within 60 days.
Govern CLI-native coding agents at the point where they invoke shell, file, web, and tool actions.
Extend governance into VS Code and Cursor workflows where developers accept completions, chats, and agent actions.
Apply the same policy model to automated agent pipelines and backend execution flows.
Use your own LLM keys for AI-driven governance features such as policy recommendations, classification, and alerting, while keeping agent enforcement and telemetry inside the KonaSense control plane.
Use Cases
Agent use cases
See how security, AI, and governance teams use Kona for Agents to govern coding agents, coding tool workflows, and agentic pipelines.
Book a demoGovern Coding Agents
Control what Claude Code, GitHub Copilot, Cursor, Codex, and Codex CLI can do before actions execute. Enforce policy over prompts, completions, tool calls, and agent-driven actions.
Pre-exec ControlSecure Tool Calls and MCP Actions
Apply governance to MCP tool invocations, shell commands, web requests, and sensitive agent actions. Escalate high-risk operations to a human reviewer when needed.
Granular PolicyPrevent Code and Secret Exposure
Stop agents from reading, editing, or exfiltrating sensitive files, credentials, and internal code without policy approval and audit visibility.
Zero Blind SpotsAudit Every Agent Session
Capture session lifecycle, command output, workspace context, and policy decisions in an investigation-grade trail so teams know exactly what the agent did.
Full ReplayCommon questions
We already have DLP, EDR, and a SIEM. Why do we need this?
None of those tools understand what an AI agent is about to do. By the time they see the action, the agent has already executed it. Kona for Agents inspects the action before execution, at the agent itself. It complements your existing stack, not replaces it.
Will this slow down our developers?
No. Drop-in runtime, no source code changes, no developer configuration. Start in observability mode to see real behavior first, then enforce only where it matters.
How do you handle our source code and secrets?
Zero-retention by default, configurable per tenant. Deploy in our SaaS, your AWS, or on-premises. Sensitive payloads are masked, transport is TLS-encrypted, and every governed event is cryptographically signed. BYOK is supported for AI-driven governance features so those calls run on your own LLM keys.
Does this run all the time on the developer's machine?
No. It is not a service, a daemon or an endpoint agent. A small signed executable sits on disk and is called by the coding agent only at the moment of an action. It evaluates that action, answers and exits. No CPU, memory or network use when AI is not in use.
What gets installed?
One signed executable for macOS, Windows, Linux or ChromeOS, and a one-line entry in the agent's configuration that says "before each action, check with Kona". It is distributed through the MDM you already use, such as Intune or JumpCloud, and is built for your organization, so it cannot be pointed at another tenant by editing a file.
Do you use AI to decide what to block?
Not for enforcement. Allow and block decisions come from deterministic rules, so the same input always gives the same answer and every decision can be explained in an audit. AI is used for insights and classification in the dashboard, not for the yes or no.
What data leaves the machine?
What is needed to evaluate the action: the command, the file path, the content the agent is writing, command output, the user's prompt and the relevant parts of the conversation. Everything is signed, encrypted in transit and isolated per tenant, and every event is delivered back to your SIEM, so you can see exactly what was sent.