Causyx AI

safely increase autonomy of AI Agents

Explore

Causyx AI: Problem

Autonomous agents fail in ways we don’t see—until it’s too late.

As teams give AI agents more autonomy, failures rarely come from a single bad decision.
They emerge from chains of interactions—LLM reasoning, tool calls, permissions, and state transitions—that quietly drift toward irreversible outcomes.
Most tools catch policy violations after they happen, or rely on static checklists that miss how agents actually behave in the wild.

Causyx AI: Solution

Learn from real executions, not hypothetical disasters.

Causyx reconstructs agent executions end-to-end to identify:
Failure chains and near-misses
Latent risk pathways that almost triggered irreversible actions
The underlying conditions that made failure likely
Instead of guessing what might go wrong, teams learn from what almost did—and intervene earlier, with confidence.

Causyx AI

How it works

Step 1 — Observe
Capture real agent executions: decisions, tool calls, state transitions.
Step 2 — Reconstruct
Rebuild causal chains leading to failures and near-misses.
Step 3 — Surface Risk
Expose hidden pathways and explain why they occurred.
Step 4 — Act
Use insights to constrain, redesign, or safely expand agent autonomy.

Designed for autonomy, not just compliance.

Causyx is built on the insight that rare, high-impact failures dominate risk in autonomous systems—and that these failures are best understood through causal reconstruction, not surface-level monitoring.
This approach enables a path from reactive safety to predictive risk understanding as agent autonomy increases.

Causyx AI

Built for teams deploying agents in production.

1. AI platform teams building autonomous systems
2. Safety, reliability, and governance teams
3. Research groups studying agent behavior at scale

Interested in deploying agents more safely?

Request early access

We’re working with a small number of early teams.