The Problem
AI agents are moving from chat into action: sending messages, changing code, deploying software, modifying access, exporting data, and triggering financial workflows.
Current Approaches
Most organizations rely on IAM, policy engines, approval tickets, AI gateways, or manual review. These controls are useful, but often reduce action governance to allow, deny, or escalate.
Why Recoverability Matters
Not all mistakes are equal. Some actions can be reversed in seconds. Others create customer, financial, security, or operational damage that is hard to undo.
How SMERC Works
SMERC scores runtime action signals before execution, then recommends whether an agent should allow, throttle, freeze, deny, or escalate.
Example Scenarios
High-signal scenarios include Kubernetes routing changes, funds transfers, security log deletion, customer data exports, SIEM rule changes, and AI-generated production deployments.
Validation Results
The validation suite evaluated 250 scenarios. SMERC differed from traditional allow/deny policy in 82.8% of scenarios, and 69.6% were constrained rather than blocked.
Fit Score
Automatic assumption testing and design-partner ranking.
Awaiting response
Problem fit--
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Likely Objections
Recommended Questions
Meeting Preparation Brief
Submit the questionnaire to generate a pre-call brief.
Discovery Dashboard
Aggregate learning across submitted discovery responses.
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