AI Agents vs. API Integrations: The New Stack for SOC 2 Evidence

SOC 2 audits still require screenshots, application evidence, and clear documentation that auditors can review. While tools like Drata and Vanta automate infrastructure checks via API, evidence collection for application workflows often remains manual. You connect your GRC platform to AWS, GitHub, and Okta, and 80% of your controls turn green. But then you look at the remaining 20%—the application-level controls—and realize you still have to manually capture screenshots of user access lists, admin panel configurations, and change management workflows.

This is where the new compliance stack emerges: API integrations for infrastructure combined with AI agents for application interfaces.

This article explains the technical differences between these approaches, why you need both to fully automate SOC 2 evidence collection, and how to structure a stack that auditors trust.

What Is the Difference Between API and Agentic Evidence Collection?

To understand why your current automation stack hits a wall, you have to look at how the data is retrieved.

API Integrations (used by Drata, Vanta, Secureframe) connect directly to the backend of a service. They query a structured endpoint to get a binary answer or a JSON log.

• Example: Querying the AWS API to check if an S3 bucket is encrypted.
• Result: A JSON response saying "encrypted": true.

AI Compliance Agents (used by Screenata) interact with the frontend of a service. They use computer vision and browser automation to navigate a user interface (UI) exactly like a human compliance manager.

• Example: Logging into a SaaS portal, navigating to Settings > Users, filtering for "Admins," and taking a timestamped screenshot.
• Result: A visual artifact (PDF/Image) showing the actual configuration screen.

Comparison: APIs vs. AI Agents

Feature	API Integrations	AI Compliance Agents
Data Source	Structured Backend Data (JSON)	Visual Frontend Interface (DOM/Pixels)
Primary Output	Logs, Binary Pass/Fail Status	Screenshots, PDF Evidence Packs
Best For	Infrastructure (AWS), Identity (Okta), MDM	SaaS Admin Panels, Internal Tools, Custom Workflows
Limitations	Limited to available public endpoints; no visual context	Slower than APIs (seconds vs. milliseconds)
Auditor Preference	Good for continuous monitoring	Preferred for "Inspection" and "Observation" testing

Why Do APIs Hit a Wall with Application-Level Evidence?

Most engineering teams assume that "automation" implies APIs. If a tool doesn't have an API, they assume it can't be automated. This creates the "manual gap" in SOC 2 prep.

1. The "No Endpoint" Problem

Many SaaS tools—especially HR platforms, smaller marketing tools, or legacy billing systems—do not have public APIs that expose granular security settings. You might be able to get a list of users via API, but you often cannot get the specific "Role Permissions" configuration that proves what an "Admin" can actually do.

2. The Visual Context Requirement

Auditors often need context that raw data lacks.

• API Evidence: A log entry showing User: John Doe, Role: Admin.
• Agentic Evidence: A screenshot showing the "Admin" role definition screen, listing exactly which permissions are checked (e.g., "Can Delete Users," "Can Export Data").

For SOC 2 control CC6.1 (Logical Access), an auditor needs to verify that the role itself is configured correctly, not just who holds the role. An API rarely provides the schema of the role; a screenshot of the settings page provides immediate, irrefutable proof.

3. Custom Internal Tools

Your internal "Backoffice" or "Admin" tool likely controls critical data. It almost certainly does not have a standardized external API for compliance. Traditionally, you have to build a custom script or manually screenshot these panels. AI agents can simply be taught to navigate the internal URL and capture the evidence, requiring zero engineering effort to expose endpoints.

How Do AI Agents Automate SOC 2 Controls That APIs Can't?

When you combine APIs with Agents, you can cover specific controls that are notoriously difficult to automate with GRC platforms alone.

User Access Reviews (CC6.1)

The API Limit: Drata can pull a list of users from GitHub. It asks you to review them. The Agent Solution: Screenata logs into GitHub, navigates to the "Organization Settings," captures the list of owners, captures the settings for 2FA enforcement, and compiles this into a PDF. It then does the same for your HR system, your billing portal, and your internal admin panel. The agent handles the collection of the artifact, while the GRC platform handles the workflow of the review.

Change Management (CC8.1)

The API Limit: Vanta checks if a Pull Request (PR) was approved before merging. The Agent Solution: An auditor often wants to see the "story" of a change. An agent can:

1. Go to the Jira ticket and screenshot the description and approval status.
2. Go to GitHub and screenshot the PR conversation and diff summary.
3. Go to the CI/CD pipeline and screenshot the successful deployment log.
4. Stitch these three screenshots into a single "Change Evidence Pack" that proves the entire lifecycle of the change.

Vendor Risk Management (CC9.2)

The API Limit: There is no API to fetch a vendor's SOC 2 report. The Agent Solution: An agent can log into a vendor's trust portal (like the AWS Artifact portal or a private trust center), navigate to the "Documents" section, download the latest SOC 2 Type II report, and upload it to your evidence library.

Do Auditors Accept AI-Generated SOC 2 Evidence?

A common concern is whether auditors trust evidence collected by AI. The key distinction lies in how the AI operates.

Deterministic Navigation vs. Generative Hallucination

Screenata's agents are not Large Language Models (LLMs) guessing at what evidence looks like. They use computer vision to navigate deterministically. They locate the "Settings" button, click it, and take a screenshot.

The artifact produced is a screenshot. It is a literal pixel-perfect capture of the screen at a specific timestamp.

• Auditor Trust: Auditors trust screenshots because they are difficult to spoof without obvious artifacts.
• Metadata: AI agents append metadata to the evidence—URL, timestamp, user session ID, and browser version—creating a chain of custody that is often stronger than a human dragging a file to a desktop folder.

In fact, auditors often prefer agent-generated evidence because it is consistent. Humans crop screenshots differently, forget to include the URL bar, or miss the date. Agents capture the exact same viewport every single time.

Where Traditional SOC 2 Automation Stops

If you rely solely on API-based automation tools, you will eventually hit a plateau where the remaining work is manual.

Control Area	API Automation (Drata/Vanta)	Manual Gap (Filled by Agents)
Cloud Infrastructure	100% Automated (AWS/GCP APIs)	None
Employee Devices	90% Automated (MDM APIs)	10% (Exceptions, Linux devices)
SaaS Access	50% Automated (SSO/Okta)	50% (Apps without SSO/SCIM)
Change Management	60% Automated (GitHub Checks)	40% (Ticket context, deployment screens)
Internal Tools	0% Automated	100% (Admin panels, DB GUIs)

The "New Stack" for compliance isn't about choosing between APIs or Agents. It's about layering them. You use APIs for the high-volume, structured data (infrastructure logs), and you use Agents for the visual, human-centric evidence (application settings and workflows).

Learn More About SOC 2 Compliance Automation

For a complete guide to automating SOC 2 evidence collection, see our guide on automating SOC 2 evidence collection in 2025, including how to integrate these workflows into your existing audit preparation.