Acceptance layer for enterprise agent work
Every output your agents produce, graded against your policy before it can ship.
Starts in shadow mode — prove what it catches before you enforce.
Claims decisionops agent
Quarantine
✕ blocked from production
Code and documents today; spreadsheets, slides, PDFs and email in pilot.
We expect ~40% upside over the plan, the strongest in the sectorno source. The base case assumes margin recovers by Q3.
Most AI just flatters you
Most models are trained to be agreeable, so they wave agent work through. OtterScore is the opposite: it assumes the output is flawed until it can't find a reason to stop it.
Same agent output · two reactions
Another AI reviewer
Ships it
“Looks good — a few small suggestions.”
Approves almost anything. The bad output reaches production.
OtterScore
Blocked
3 violations · routed back to the agent, not shipped.
Same output. One ships it. OtterScore stops it.
Runs across your estates
One policy gate across every cloud, framework, and runtime — coexists with what you run; on-prem or BYOC.
Your agents, any stack
Model-agnostic
One policy, any cloud or stack.
Inline enforcement
Stops work, routes fixes, signs the record.
Private deployment
Redacted traces; never trained on.
Signed audit trail
Hash-chained, exportable to SIEM/GRC.
Available for BYOC pilots. Built on AgentOS, our agent execution control plane.
Shadow on one workflow, prove it on your policy, then enforce and expand.
Shadow mode on one workflow. Observe-and-score, no blocking.
Start a pilotMost popular
Block-and-route in production, by your policy.
Talk to usAgentOS runs the agents: routing, retries, spend caps.
Talk to usSSO/SCIM, SIEM/GRC audit export, custom policies, support.
Talk to usOne standard across every cloud and runtime; sign-off.
Talk to us