AI Agent Runtime Governance RFP Questions for Buyers
An effective RFP for AI agent runtime governance must test whether policy enforcement, identity scoping, and audit logging occur at the moment an agent acts, not just at configuration or deployment time. Questions should require live demonstration of runtime enforcement, verification of scoped and revocable agent credentials, exportable and tamper-evident audit logs, and explicit handling of Model Context Protocol trust boundaries, including tool server consent and post-approval behavior monitoring.
MCP Trust Boundaries and Tool-Call Governance
-
1
Server Trust and Consent
The Model Context Protocol introduces a client-server architecture where AI applications connect to external tool and data servers, and each connection creates a distinct trust and permission boundary. Anthropic's published MCP documentation is explicit that server trust cannot be assumed and recommends explicit user consent before tool invocation. Security researchers and protocol maintainers have documented MCP-specific risks including tool poisoning, where a malicious or misleading tool description influences agent behavior, and rug-pull scenarios, where a tool's behavior changes after it has already been approved.
Five RFP Categories That Reveal Real Runtime Enforcement
Vendor responses tend to diverge sharply across these categories. Use them to structure the evaluation rather than accepting general governance claims at face value.
Policy Enforcement
Runtime blocking versus static configuration.
Agent Identity
Scoped, short-lived credentials versus persistent access.
Tool-Call Governance
Validation of requests and returned outputs.
Auditability
Structured, exportable, tamper-evident logs.
MCP Security
Server trust boundaries and consent flows.
Core RFP Questions
These questions are designed to be answered with live demonstration rather than documentation alone. A vendor unable to answer any of them in a working environment has likely implemented governance at design time only.
- Can you demonstrate, in a live environment, an agent action being blocked by policy at the moment of execution rather than pre-filtered during configuration?
- What is the architectural component that performs this evaluation, and is it separate from the agent's own reasoning process?
- Do policy updates apply to already-running agent sessions, or do they require redeployment to take effect?
- Can policy changes be tested in a staging environment before production rollout?
- What latency does runtime policy evaluation add to a typical tool call?
- How does your platform handle Model Context Protocol server trust, including consent or validation steps before a new tool server is invoked?
- If a connected tool's behavior changes after initial approval, what runtime mechanism detects and responds to this change?
- Is tool output validated before it is passed back into the agent's reasoning process?
- How are credentials scoped and revoked for agents operating across multiple tool servers?
- Does your approach to tool approval require re-authorization when a tool's declared capabilities change?
Why Standard AI Governance RFPs Fall Short
Most procurement templates for AI governance were written for model risk management or data privacy review, not for autonomous agents that call tools, retrieve data, and take action in production systems. As a result, they ask about training data provenance, model cards, and bias testing, but rarely ask whether an agent's actions are actually checked before they execute. A vendor can score well on a conventional AI governance questionnaire while offering no real-time enforcement at all.
Testing Policy Enforcement at Runtime
The distinguishing question for any governance platform is whether policy is evaluated at the moment an agent attempts an action, or whether it is only encoded into the agent's initial configuration and prompts. Configuration-time governance can be bypassed by prompt injection, unexpected tool chaining, or simply by the agent reasoning its way around a soft instruction. Runtime enforcement requires a separate, independent component that intercepts the action and evaluates it against policy regardless of what the agent's own reasoning process concluded. Buyers should ask vendors to demonstrate this distinction live rather than relying on a written description.
Agent Identity and Least-Privilege Permissions
Agents that operate with standing, broad credentials create the same risk profile as a human employee who never has access reviewed or revoked. RFP questions should probe whether agent identities are scoped to specific tasks, time-bound, and revocable without redeploying the agent itself. This matters especially when a single agent orchestrates calls across multiple tool servers, each of which may require different levels of access.
Auditability and Log Verification
Audit logs are only useful if they are complete, exportable, and resistant to tampering after the fact. Buyers should ask whether logs capture the full context of an agent decision, including the policy evaluated and the outcome, and whether those logs can be exported into existing security information and event management tooling rather than being locked inside a vendor-specific dashboard.
Design-Time Governance vs. Runtime Governance
Design-time governance covers activities such as model selection, prompt design, and pre-deployment risk assessment. These are necessary but not sufficient. Runtime governance covers what happens after deployment, when the agent is actually interacting with tools, data, and users. An RFP that does not clearly separate these two categories will tend to reward vendors who are strong on documentation but weak on enforcement.
Structuring the Evaluation Without Overweighting Compliance Claims
Compliance attestations and certifications are useful signals but should not substitute for technical verification. Structuring the evaluation so that live demonstration and architectural questions carry real weight, alongside compliance documentation, produces a more accurate picture of whether a vendor's governance is enforced in production or only described in policy.
Build an RFP That Tests Runtime Enforcement, Not Just Policy Documents
Trussed AI provides runtime governance for enterprise AI agents, including policy enforcement, agent identity and permissions, tool approval workflows, and audit logging designed for verification during vendor evaluation.
Request a Demo