Google Agentspace vs Amazon Q Business vs Trussed: Agent Controls

Where Each Platform's Agent Controls Originate

Enterprise architects evaluating agentic AI platforms need to understand not just what each vendor claims to control, but where that control is actually enforced. Google Agentspace and Amazon Q Business both take a similar architectural approach: agent access to enterprise data is governed by identity federation combined with access control lists (ACLs) synced from connected source systems.

Google Agentspace integrates with Google Cloud IAM and supports enterprise identity federation, so agent retrieval requests are scoped to the requesting user's authenticated identity. Agentspace's enterprise search and grounding capabilities are documented as respecting document-level permissions inherited from connected systems, meaning retrieved content is filtered to what that user is already authorized to see.

Amazon Q Business follows a comparable pattern using AWS IAM Identity Center to federate identity providers and map authenticated users to application permissions, with data source connectors syncing document-level ACLs so responses reflect the requesting user's existing access. In both cases, permission enforcement is largely a byproduct of data retrieval scoping, not a dedicated runtime authorization engine built specifically for agent actions.

Agent Control Models at a Glance

The Gap Between Data Access and Tool-Call Governance

Both platforms restrict what data an agent can retrieve, but neither's public documentation describes a mechanism for governing the sequence of tool or API calls an agent makes while completing a multi-step task. Amazon Q Business's Guardrails feature illustrates this distinction clearly: it operates at the level of topics and content, allowing administrators to block or restrict specific subject matter in generated responses. It does not, based on available documentation, evaluate or restrict individual tool invocations, such as which internal system an agent calls, what parameters it passes, or how many actions it chains together before returning a result.

Enabling agent access to external tools in Amazon Q Business requires administrators to explicitly configure and scope each plugin or action, which limits exposure at setup time but does not provide ongoing runtime evaluation of how those permissions are used during execution. Google Agentspace's documentation similarly centers on retrieval permissions rather than action-level governance. For enterprises whose agents call multiple internal systems or take actions beyond answering questions, this leaves a governance gap between content moderation and operational control.

Where a Runtime Governance Layer Fits

A runtime governance layer, such as the one Trussed provides, is architecturally distinct from the identity and ACL models native to Agentspace and Q Business. Rather than relying solely on inherited permissions from source systems, it sits between agent orchestration and tool execution, evaluating each tool call against defined policy before it runs. This includes agent identity issuance, least-privilege permission scoping, and tool approval workflows that apply regardless of which cloud platform hosts the underlying agent.

Audit logging at this layer is built around agent actions and tool invocations rather than only data-access events, which addresses a gap neither Agentspace nor Q Business documentation currently details. Because this layer operates independently of platform-native IAM and ACL enforcement, it does not replace existing identity federation or connector-based access controls. It is intended to work alongside them, adding a consistent enforcement point for enterprises running agents across more than one cloud ec