DeepInspect vs Zenity: Agent Governance and the LLM Request Boundary

Zenity has become the name to know in AI agent governance. Gartner called it the company to beat in that category in an April 2026 report, and its platform governs agents built on Salesforce Agentforce, Microsoft Copilot Studio, ServiceNow, and Power Platform through build-time posture checks, observability, and real-time risk detection and response. If your exposure is a growing fleet of agents inside low-code and SaaS platforms, Zenity is squarely aimed at your problem. DeepInspect sits at a different point in the flow, and understanding where the two boundaries meet is the whole decision. I want to draw that line precisely, because these tools overlap less than the shared "agent security" label suggests.

TL;DR

Zenity governs agents at the platform layer, strongest for organizations building on Agentforce, Copilot Studio, and ServiceNow that need visibility and posture control over what those agents are and do. DeepInspect enforces identity-bound policy and writes a per-decision audit record on the HTTP calls agents and users make to LLMs, strongest when authorization and audit evidence on model traffic are the requirement.

Zenity: where it sits

Zenity operates at the agent and platform layer. It discovers the agents built across low-code and SaaS environments, runs build-time posture checks on how each agent is configured and what it can access, and detects risky agent behavior at runtime, agent privilege escalation and data exfiltration among the named risks. For a Salesforce Agentforce estate, that means seeing which agents exist, whether they are configured against policy, and whether one is doing something it should not inside the platform.

The strength is coverage across the platforms where agents are actually being built, and a full lifecycle view from build time through runtime. Much of that governance happens inside the SaaS platform's own action model, which is where a large share of agent risk currently lives.

DeepInspect: where it sits

DeepInspect governs one specific thing: the HTTP calls between authenticated users or agents and the LLM APIs they reach. It is a stateless proxy at the AI request boundary that evaluates each call against identity, role, data classification, model authorization, and policy, returns pass, block, or redact before the model responds, and commits a signed per-decision audit record independent of the calling application.

That scope is deliberately narrow. DeepInspect is the enforcement and audit layer on model traffic, the place where you can prove that a specific identity sent a specific prompt to a specific model under a specific policy. It is the action lineage for AI calls, model-agnostic across providers.

The boundary between them

The honest architectural line is this. Zenity governs the agent and its behavior inside the platform, including actions that never leave the SaaS environment as an HTTP call to an external LLM. Those platform-internal actions sit outside DeepInspect's enforcement boundary, which is HTTP AI traffic between authenticated users or agents and LLMs. DeepInspect does not claim to govern a native Agentforce action that stays inside Salesforce.

Where the two meet is the model call. When an agent reaches out to an LLM over HTTP, DeepInspect is the layer that authorizes that call and records it. Zenity tells you the agent exists and is misconfigured; DeepInspect enforces and records what happens when that agent talks to a model. Different layers, and a program with heavy agent adoption often wants both.

Feature comparison

On scope: Zenity spans build-time posture and runtime behavior across agent platforms; DeepInspect enforces on the LLM HTTP request path specifically.

On the decision: Zenity detects and flags risky agent behavior; DeepInspect makes a deterministic pass, block, or redact authorization decision on each model call and fails closed.

On audit: DeepInspect's output is a signed, identity-bound per-decision record for model traffic, written independent of the application. Confirm what per-action record Zenity produces for platform-internal agent actions and how it is retained.

On model-agnosticism: DeepInspect governs any HTTP LLM endpoint regardless of which platform the agent runs on; Zenity's governance is organized around the agent platforms it integrates with.

Pick Zenity if

Your exposure is a fleet of agents built on Agentforce, Copilot Studio, ServiceNow, or Power Platform, and you need to discover them, check their build-time posture, and detect risky behavior inside those platforms. You want lifecycle governance of the agents themselves, and your primary risk is what agents are configured to do within the SaaS environments where they run.

Pick DeepInspect if

You need identity-bound authorization and a signed per-decision audit record on the HTTP calls agents and users make to LLMs, independent of any single agent platform. You run models across providers and want one enforcement policy on that traffic. Your requirement is proving, per model call, who was permitted to do what with which data, which is the enforcement-and-audit layer rather than the agent-posture layer.

Pricing approach

Both sell through enterprise sales rather than published pricing. Zenity is priced around agent governance across the platforms it covers. DeepInspect is scoped to enforcement and audit on AI request traffic. Because they occupy different layers, the comparison is less either-or than most vendor bake-offs; the budgeting question is whether you are governing agent posture, governing model traffic, or both.