AgentKey · Learn
A focused library on controlling what AI agents are allowed to do, evaluating agent actions before they reach external tools and APIs, and proving what happened. Each topic stands on its own, then shows where AgentKey fits.
By AgentKey · Last updated 2026-09-01
AgentKey is an authorization and evidence layer for AI agents. It evaluates every agent action against a policy before it reaches an external tool or API, records each decision to a tamper-evident audit trail, and produces verifiable proof of what the agent did.
ReadAI agent security is the set of runtime controls, policies, and observability practices that keep autonomous AI agents from taking harmful or unauthorized actions when they call tools and APIs.
ReadAgentic AI security is the discipline of safely deploying autonomous AI systems that plan, decide, and call tools by adding runtime controls, policy enforcement, and verifiable observability.
ReadAI agent authorization is the runtime decision process that determines whether an agent is permitted to take a specific action against a specific resource before the action reaches the external tool or API.
ReadAI agent permissions are the rules that define which resources and actions an agent may use, optionally constrained by parameter-level conditions such as amount limits or allowed domains.
ReadAI agent access control enforces least-privilege boundaries around an agent: which tools it can reach, which actions it can perform, and the parameter ranges it may use.
ReadAn AI agent policy engine stores, evaluates, and versions the rules governing what an agent may do, returning an allow, deny, or require-approval decision for each action.
ReadAI agent tool security is the practice of vetting, scoring, and controlling the external tools, MCP servers, APIs, and plugins an agent connects to.
ReadMCP security protects Model Context Protocol connections, covering tool vetting, authorization, credential handling, and audit logging for the tools a model discovers and calls.
ReadPrompt injection places hostile instructions in data an agent reads. Protection scans untrusted inputs before they reach the model and escalates or blocks actions that follow injected instructions.
ReadHuman approval (human-in-the-loop) pauses a risky agent action until a person explicitly approves or denies it, turning an autonomous decision into a supervised one.
ReadAI agent audit logs are tamper-evident records of every action an agent took and every authorization decision made, structured so a session can be replayed and verified without trusting the system that produced it.
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Install the SDK and wrap your tools in one line.