Trust, Observability & Runtime Control for AI Agents
Production-ready observability and control for AI agents. Monitor, debug, and control your AI systems with real-time insights and safety policies.
Trusted by AI teams worldwide
Minimally Invasive Integration
Add comprehensive observability to your existing agents with just 1-10 lines of code. Simple decorators for tracking, flexible policies, and experiment management.
from runlog import runlog
# Add observability with 1 decorator
@runlog(service="support", env="prod", experiment="refund_v2")
def handle_refund_request(customer_id: str, amount: float):
# Your existing agent code stays the same
docs = kb_search("refund policy")
if is_eligible_for_refund(customer_id, docs):
return process_refund(customer_id, amount)
return "Refund denied based on policy"
# Policies defined separately (YAML, GUI, or natural language)
# Experiments tracked automatically via decorator parametersOur Mission, Vision & Values
The principles and purpose that guide everything we do at RunLog AI.
As AI agents become more autonomous and powerful, the need for comprehensive observability and control becomes critical. We believe that every organization should be able to deploy AI agents with confidence, knowing exactly what they're doing and having the tools to prevent unsafe actions.
RunLog AI was born from our experience building and deploying AI systems at scale. We've seen firsthand the challenges of debugging agent failures, preventing costly mistakes, and ensuring compliance in production environments.
Frequently Asked Questions
Everything you need to know about RunLog AI.