SafeRun sits inline between AI agents and the tools they use — validating tool calls, blocking runaway loops, pausing risky actions for approval, and giving engineers replayable incident timelines when something breaks.
from saferun import guard
@guard(policy="production")
def your_agent_action(args):
# SafeRun validates, blocks, and logs.
# You ship without losing sleep.
return tool.execute(args)Integrates in minutes. No agent rewrite required.
Your agent invented a customer ID and tried to call delete_customer on a record that does not exist.
Your sales agent got stuck in a loop and emailed the same lead twelve times in five minutes.
Your support agent attempted a $4,500 refund because a user asked nicely.
Observability tools tell you it happened. SafeRun stops it from happening.
Every agent action is validated against your policies before it executes. Tool calls with hallucinated arguments, out-of-policy parameters, malformed inputs, or unsafe patterns are caught inline.
Safe actions proceed. Risky actions are blocked. Ambiguous actions escalate to a human in your approval queue through Slack, email, or webhook.
Every decision is logged with full context. When something breaks, engineers can step through the agent run frame by frame and see exactly what happened.
Block hallucinated tool calls, malformed arguments, and unsafe parameters before they execute.
Step through every agent run. See model output, tool selected, arguments, policy decision, tool result, latency, and cost for each step.
Declarative guardrails for what your agents can and can’t do. Versioned, testable, and deployable with your application logic.
Route high-risk actions to a human. Approve, reject, or modify actions from Slack, email, or webhook.
Detect runaway agents and stop repeated calls before they burn through your API budget or annoy your customers.
Every agent action, policy decision, approval, and blocked call is logged for debugging, incident review, and compliance workflows.
LangSmith, Langfuse, Helicone, Sentry, Datadog, and Arize help teams observe, trace, and debug AI systems. That is useful. But by the time you read the log, the customer record may already be deleted, the email may already be sent, or the refund may already be processed.
SafeRun sits inline before tool execution. We do not just record what happened — we intercept risky actions before they happen, block bad calls, pause ambiguous actions for approval, and create replayable incident timelines for engineers.
Logs what happened after execution.
Validates, blocks, approves, and replays before production impact.
SafeRun integrates in three lines of code. No proxy, no infrastructure changes, no agent rewrites.
import { guard } from "@saferun/sdk";
const safeTool = guard(tool, {
policy: "production",
approval: "slack"
});support-agent-v2 attempted stripe.refund for $4,500
Join the waitlist. Be among the first teams using SafeRun to validate, block, approve, and replay AI agent actions in production.
No spam. No salesy emails. Updates only when there is something real to show.
Built for teams already testing agents in production.