Ship faster with guardrails security can verify.
Deploy policy, traceability, and agent controls at the gateway layer without reworking your application code.
AI agent guardrails are runtime controls that define what autonomous agents can do, which tools they can call, what data they can access, and when human approval is required.
Four problems Qadar solves at the infrastructure layer.
Technical capabilities for AI platform teams
One endpoint swap
Change the base URL in your configuration. No SDK changes, no new dependencies, no application code modifications. Your team ships nothing different.
YAML policy bundles
Version-controlled, reviewable in PR, CI/CD-deployable. Policy changes go through the same review process as your code. Treat governance as engineering, not an ops ticket.
Canonical trace IDs
Every LLM call gets a Qadar trace ID. Correlate across your observability stack. Full trace: model, token usage, latency, input classification, output summary, policy outcome.
Agent tool-use controls
Define which tools each agent can call, per environment. Allowlist, denylist, or approval-gate at the action level. No changes to the agent's code or the model.
Per-env policy rules
Dev permissive, staging checked, production strict. Same config, different rules per environment. No application code to update when policy changes.
Provider-agnostic
OpenAI, Anthropic, Azure OpenAI, Google Vertex, and self-hosted models. Switch providers without changing your governance layer. One config for your entire AI stack.
What engineering teams ask in technical evals
What are AI agent guardrails and how do they work?
AI agent guardrails are rules that govern what an autonomous agent is allowed to do at runtime — enforced at the infrastructure layer, not in the model's training. They intercept tool calls and API requests made by the agent, apply a policy check, and either allow, modify, require approval for, or block the action before it executes. This makes agent behavior predictable, auditable, and controllable without changing the agent's code.
How do you control what tools an AI agent can use?
Through a gateway layer that sits between the agent and its tools. You define a tool allowlist or denylist per agent, per environment. The gateway intercepts every tool call, checks it against the policy, and either lets it through or stops it. For high-risk tool calls (e.g., writing to a database, calling a payment API), you can require a human approval step before the action completes.
What is agentic AI risk?
Agentic AI risk is the category of security, compliance, and operational risks that arise when AI systems operate autonomously — taking actions, calling tools, and making decisions without human review for each step. As agents gain access to more tools and operate in longer chains, the potential impact of an out-of-policy action grows. The core mitigation is enforcing policy at the action layer, not relying on the model's judgment alone.
Does Qadar add latency to our LLM calls?
The Qadar gateway adds single-digit millisecond overhead to policy-checked requests — below the measurement threshold for most production AI workloads where model inference latency is 200ms–2s. Policy evaluation is synchronous and inline; logging is asynchronous and does not block the request path.