Runtime Control and Runtime Authorization for AI Agents

Know what your AI agents do. Decide what they're allowed to.

Agents call tools, access data, and take actions across your systems. Kontext enforces your security policy on every action, before it executes. One command installs the daemon on the machines where agents run. No code changes, no gateway, and nothing slows down.

How it works

Kontext hooks every tool call from Claude Code, Cowork, and Codex before it runs. Deterministic policy plus a local judge score each action in milliseconds. Observe mode backtests your policy against real agent traffic, decision by decision, before anything is enforced. Flip to enforce and destructive commands never reach the shell, while risky actions wait on a human yes. Every decision is attributed and exportable: who ran what, where, and why it was allowed.

Control agents wherever they run

• Endpoint: coding agents and assistants on developer machines, every command, file edit, and tool call checked before it executes.
• Cloud: your own agent applications and cloud-hosted workloads, acting with real credentials against production systems.
• SaaS: agents acting for users inside the SaaS tools your teams live in, with the same policy and the same audit trail.

Start in observe mode, enforce when ready

Security that slows developers down gets uninstalled, so Kontext decides in milliseconds, on the machine. Every deployment starts in observe mode: the daemon records the decision each tool call would get without blocking anything, which means your policy is backtested against real agent traffic before it is ever enforced. When the would-deny log matches your intent, flip to enforce. From that point, destructive commands such as rm -rf on protected paths never reach the shell, force pushes to protected branches wait on a human yes, and credential file reads are flagged and captured with redaction. Policies are defined in layers across org, group, user, agent, repo, and branch, and payload capture is configurable as omitted, summary, or full.

Built for developers and for security teams

For developers: one command, zero code changes, and agents that keep their speed. Decisions are evaluated locally, and tool calls and payloads are redacted on the machine before anything streams to the dashboard. Kontext works with the agents your team already runs, and adding a new agent never means rebuilding your security layer.

For security teams: a versioned policy snapshot, payload capture controls, and an audit trail that answers who ran what, where, and why it was allowed, for every action. Sessions, decisions, and devices stream into one console where you can filter by agent, user, repo, or policy, and export the evidence for review.

Blogs

More coming soon

• Research: AgentIntentBench - A Runtime Authorization Benchmark for LLM Agents Coming soon
• Company: Building the Infrastructure Behind Kontext Coming soon
• Research: The impact of Dynamic Authorization on Token Consumption Coming soon

View all blogs

Guides to runtime agent security

• Runtime authorization for AI agents
• MCP security risks and best practices
• Agentic AI security guide

Pricing

Kontext includes a Starter plan at $0 for developers securing agents on their own machines, a Pro plan at $149 per month for teams rolling agents out across the org, and a Scale plan at $499 per month for higher volume and longer retention. Teams with custom identity, deployment, or volume requirements can contact Kontext for enterprise planning.