AgentGate sits between your AI agents and your systems — intercepting, scoring, and controlling every action in real time before it can cause harm.
As agents move beyond chat into production, issuing refunds, modifying accounts, and calling APIs, even small model errors can lead to costly, irreversible mistakes.
There is currently no reliable way to verify agent actions before they execute. You either block agents from doing anything useful, or you accept the risk.
Large unintended refunds — a support agent misreads context and issues a $4,000 refund on a $40 ticket.
Bulk destructive writes — a data-cleanup agent deletes 10,000 production records instead of 10.
Sensitive data exposure — an analytics agent exports full customer PII to an unintended endpoint.
Your AI agent attempts to call an API, modify data, or execute a system operation.
Every action is routed through AgentGate before it reaches your system. Nothing gets through unexamined.
Risk score computed. Decision issued: Allow, Block, or flag for human review.
Every decision is written to an immutable audit log. Blocked actions halt with reason.
Four stages. Zero bypass paths.
Autonomous LLM-powered worker. Issues structured action requests to downstream systems.
Intercepts every action. Evaluates against rules. Computes risk score in real time.
Real-time view of flagged actions. Approve or reject with one click. Full audit trail.
APIs, databases, cloud resources. Only verified, policy-approved actions reach here.
A complete runtime safeguard stack — not a wrapper, not a post-hoc logger.
Every request from every agent is routed through the Gate before touching your infrastructure. Zero bypass paths.
Numeric risk scores computed per action using configurable severity, impact, and confidence factors. No black boxes.
Define forbidden operations, rate limits, and threshold-based auto-block rules in JSON or YAML. Instant propagation.
High-risk actions are surfaced to operators for approval before execution. One-click approve or reject.
Real-time view of inbound actions, policy decisions, system health, and audit-log entries. No lag, no sampling.
Every decision — allow, block, escalate — is written to a tamper-proof log. Export to JSON or your SIEM.
Every action gets a numeric risk score before the policy engine issues a verdict. A transparent formula you can inspect, override, and tune.
Define per-resource thresholds. Adjust weights by agent role. Set hard limits — no code changes required.
Wherever an AI agent takes real-world action, AgentGate keeps the blast radius manageable.
Finance agents move fast. A single misclassified transaction can mean fraudulent transfers, erroneous refunds, or compliance violations.
AI support agents can resolve tickets, issue refunds, and modify subscriptions. Without a control layer, a model error becomes an incident.
Autonomous DevOps agents write code, open PRs, and run commands. An unchecked infra agent can deprovision resources in seconds.
Where data integrity and access control are regulatory requirements, every agent action must be justified, logged, and attributable.
AgentGate is in early access. We're working with a small number of teams deploying AI agents in production today.