Axtary

Axtary is a security platform that governs AI agent actions before they execute, addressing a fundamental challenge that emerges when autonomous agents gain access to production systems. When development teams deploy agents that can interact with databases, cloud infrastructure, messaging platforms, or code repositories, those agents carry the ability to make consequential mistakes. An agent might hallucinate an instruction, misinterpret context, or have its credentials compromised by an attacker. Axtary intercepts every tool call an agent attempts to make, checks it against predefined security policies, and requires explicit human approval for risky actions before any external system receives the request. This creates a security layer that operates at the action level rather than relying on broad permission grants that agents might misuse.

The platform operates through three interconnected mechanisms that work together to create comprehensive authorization coverage. Payload binding forms the cryptographic foundation of the system. When a human approves an action, that approval is mathematically bound to the exact payload hash of what was approved. If an agent attempts to modify any parameter of the payload after receiving approval, the system detects the hash mismatch and rejects execution before the request reaches external systems. This prevents a specific class of attacks where compromised or malfunctioning agents try to slip different actions through under the guise of previously approved requests. The cryptographic binding creates an immutable, verifiable link between what was explicitly approved and what actually executes against production resources.

Policy enforcement runs through engines built on Cedar and OPA, two established policy languages that allow teams to define granular constraints on agent behavior. These policies can specify file size limits, blocked file paths, resource scoping rules, rate limits, and other constraints that apply uniformly across different tools and integrations. Teams can create policies that prevent agents from accessing certain directories, limit the number of database rows an agent can modify in a single operation, or require additional approval for actions that affect production environments versus development resources. The policy execution happens locally on the organization's own infrastructure, with only coordination traffic passing through Axtary's cloud services. This architecture means sensitive data and actual enforcement remain within the organization's security perimeter while still benefiting from centralized policy management capabilities.

Every action attempt flows into an audit ledger that records whether it was approved, denied, or detected as tampered. Each entry includes cryptographic verification proof that allows teams to reconstruct exactly what happened, what was requested, what was approved, and what actually executed. This creates a complete forensic trail suitable for compliance requirements, incident investigation, and security audits. The ledger provides evidence that can demonstrate to regulators or auditors that AI agents operated under appropriate human oversight and that unauthorized actions were detected and blocked.

The platform supports multiple connectors for common tools and services out of the box. GitHub integration covers code operations including commits, pull requests, and repository management. Slack connectivity handles messaging actions that agents might perform. Linear and Jira integrations address project management and issue tracking operations. PostgreSQL support covers database queries and modifications. Both AWS and Google Cloud connectors enable infrastructure operations across major cloud providers. The system also works with Model Context Protocol servers, extending coverage to any MCP-compatible tool that agents might use. This connector library means teams can deploy Axtary across their existing agent infrastructure without building custom integrations for each tool.

The primary audience includes development teams that have moved beyond experimental AI agent deployments into production use cases where agent mistakes carry real consequences. Security-conscious organizations deploying agents with access to customer data, financial systems, or production infrastructure will find the cryptographic authorization model particularly relevant. Teams working in regulated industries where auditability is mandatory can use the ledger to demonstrate compliance with oversight requirements. Organizations concerned about insider threats or credential compromise also benefit from the tamper detection capabilities. What distinguishes Axtary from alternative approaches is its focus on exact action authorization rather than categorical permissions. Many systems either block entire tool categories or grant broad access once an agent is authenticated. Axtary's payload binding model means approval covers the specific parameters of a specific action, not a permission to perform similar actions in the future. The platform actively demonstrates attack scenarios in its documentation where agents attempt post-approval payload modification, showing how the cryptographic binding catches these attempts. The system is currently in early access with availability through a waitlist signup on the main site.