The Policy Layer for AI Agents: How It Works.
Polidex sits between your AI agents and the systems they act on. Agents send a structured query with the relevant identifiers and request details. Polidex fetches authoritative context from source systems directly, evaluates the applicable rules, returns a resolved decision, and issues a signed token that scopes what the agent is permitted to do next.
This page is the access path to every mechanism that makes that work. The concept pages below explain each component. The comparison pages show why this approach differs from BRMS, hardcoded rules, and system prompts.
The mechanism, in one paragraph
An agent receives a request. It submits a query to Polidex over MCP with the relevant identifiers and request details. Polidex fetches authoritative context from source systems directly, evaluates the query against the current versioned ruleset, and returns a resolved decision: authorized, denied, or escalate. When the decision authorizes action, Polidex issues a signed decision token that the connector uses to enforce scope against the downstream system. The token is the authorization, and it is also the audit record.
For a step-by-step walkthrough of a single decision from query to enforcement, see the decision flow page.
Go deeper on specific mechanisms
Each component in the system has its own page. Start with the architecture decision if you are evaluating whether to build the policy layer in or bolt it on later.
The two agentic CS architecture paths: which one reaches 60%+ containment and which one builds in a ceiling.
What a policy engine is and why AI agents need a dedicated policy evaluation layer.
How Polidex issues cryptographically signed authorizations that serve as both enforcement and audit record.
How the Model Context Protocol makes Polidex callable from any AI agent.
How policy changes are versioned, approved, and published without touching agent code.
How escalations and exceptions are handled as infrastructure, not improvised side channels.
The interface for managing policy, reviewing decisions, and exporting audit records.
A step-by-step walkthrough of how an agent call becomes a resolved decision.
How Polidex compares
If you are evaluating Polidex against an existing approach, these pages walk through the structural differences.
How Polidex differs from enterprise business rules systems like FICO Blaze and IBM ODM.
Why hardcoded rules in application code have the same auditability problems as system prompts.
The structural argument for why system prompts cannot serve as policy infrastructure.
Working through how to deploy agentic CS?
If you're at a mobile operator or enterprise evaluating agentic AI for your operation, we'd welcome a conversation about what containment is realistic, what the policy layer needs to look like, and how to make the deployment defensible.
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