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The Complete Platform for AI Agent Safety

RunLog AI provides the observability, control, and testing capabilities you need to deploy AI agents safely in production environments.

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Three Pillars of Agent Safety

Comprehensive capabilities that work together to ensure your agents are safe, reliable, and performant.

Complete Observability
Track every step of agent execution with detailed traces, cost analysis, and performance metrics.
  • Step-by-step execution traces
  • Cost and latency tracking
  • Tool call monitoring
  • LLM usage analytics
Runtime Policy Enforcement
Prevent unsafe actions before they happen with configurable policies and real-time enforcement.
  • Budget and cost controls
  • PII protection
  • Loop detection
  • Human approval workflows
Deterministic Replay
Test changes safely with deterministic replay of historical agent runs and what-if scenarios.
  • Historical run replay
  • A/B testing capabilities
  • Policy impact analysis
  • Safe experimentation

Why Choose RunLog AI?

Built specifically for production AI agent deployments with enterprise-grade security and reliability.

Lightning Fast
Minimal overhead with sub-10ms policy evaluation and real-time monitoring that doesn't slow down your agents.
Enterprise Security
SOC2 Type II compliant with end-to-end encryption, audit logs, and granular access controls for sensitive data.
5-Minute Setup
Get started in minutes with our lightweight SDKs. No complex infrastructure changes or lengthy integrations required.
Cost Optimization
Reduce LLM costs by up to 40% with intelligent caching, budget controls, and usage optimization insights.
Real-time Insights
Live dashboards with detailed metrics, alerts, and analytics to understand and optimize agent performance.
Safe Experimentation
Test policy changes and model updates safely with deterministic replay before deploying to production.
Ready to Deploy Safer AI Agents?
Join leading AI teams who trust RunLog AI for production agent safety and observability.
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RunLog AI

RunLog AI records every agent step, enforces policies before risky actions, and replays runs to fix issues—before users are impacted.

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