Agentagon
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Understand, improve and trust your AI agents

Self-improving production infra for AI Agents. Self-improving
production infra
for AI Agents.

Agentagon analyzes AI agent behavior and turns it into fixes, evals and features.

Agentagon analyzes AI agent behavior and turns it into fixes, evals, and features.

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Screenshot of the Agentagon overview dashboard with trace metrics, cost and latency charts, active users, issue counts, and recent issues.
Used by AI engineers at
Apple Google Mili
The production gap

AI agents are easy to demo, but impossible to trust in production.

Any change in your AI agent sends it into unknown territories.

Teams can see traces and dashboards. But are still blind to failures the users feel.

What Agentagon does

Know which failures to fix, features to build and scenarios to test next.

Agentagon gives you a clear order of operations to reduce churn, earn trust and win new users.

  • How big the agent failures really are.
    Known and unknown.
  • What is causing user churn.
  • What do your users really want.
  • Ship agents to production with confidence.
  • Complete fixes and evals backed by evidence.
  • Start offline, shadow online, then enter hot paths.
Continuous improvement loop

From production signals to safer and improved agents.

01

Detect

Surface hidden failures and user frustrations. In real time.

02

Mitigate

Take immediate action to mitigate the issue.

03

Fix

Ship targeted changes and evidence-backed evals.

04

Improve

Get better at it.