Live Demo:
Agent Trust in Action
Most teams can ship an AI agent. Almost none of them can tell you, with confidence, whether it’s making good decisions once it’s live. See a live demo of Monte Carlo’s Agent Trust platform, built around four layers every production agent needs covered: context, performance, behavior, and output.
You can’t fix what you can’t see — and most teams still can’t see their agents.
Shipping an agent is the easy part. Knowing whether it’s making good decisions — from the data it’s reading to the answer it hands back — is where teams get stuck once they’re past one agent in a dev environment.
In this demo, you’ll see:
- Tracing a bad answer back to its source: mapping an agent’s output through its tools to the data feeding it.
- Performance monitoring built for agents: how breaking down latency by model surfaced a fallback model running 4x slower.
- Catching failures no one thought to monitor: Reinforcement Loop flagging a tool call that spiked Snowflake compute by looping the same query 20+ times.
- Grading output at scale: LLM-based evals checking accuracy, helpfulness, and custom criteria without manual review.
Agent Trust in Action · Session 1
“You cannot separate data observability from agent observability — because the data feeding the agent matters as much as the agent itself.”
JE
Jennifer Elkhouri
Senior Sales Engineer at Monte Carlo
See how enterprises put agent trust into production
Trusted by 400+ enterprises
