Automate IT and AI Compliance with Agents

Why not using AI for governance will become your disadvantage

In today’s enterprises, teams who want to use basic software and AI tools often wait 4+ months to get approval for their solutions – even though they could have been deployed in a week. Not because these are particularly risky solutions or because relevant information is missing, but because compliance processes are yet manual and governance headcount is already running at full capacities.

And with the rise of agent builders and citizen development platforms, the amount of assets and AI use cases to assess and govern grows almost exponentially. Manual governance is not feasible anymore.

Not changing your approach to IT and AI governance will block innovation and your AI adoption. This is why we at trail believe in automated governance that scales with your use cases. And for that you can and should use AI to speed up approval cycles of your low-risk assets, while gaining back focus on the assessment of high-risk assets.

This article shares how trail’s Agent Flows can help you with that, how our Copy-on-Write mechanisms give you the necessary human control, and why it’s not sufficient to use general-purpose AI tools, like ChatGPT, Claude or Gemini, for governance.

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What is the current state of AI-based automations and agents in GRC?

Deterministic Automations

In classical GRC tools, many automations already exist to speed up certain processes or pre-defined workflows in a structured, step-wise manner, such as the recommendation of relevant questionnaires and controls. These automations may save time in certain parts of the workflow, but the actual work is still manual.

Chatbots

Many GRC tools also provide AI chatbot assistants, but their functionalities are very limited and require significant prompt engineering to get results you can work with. Typical GRC chatbots are mainly useful for FAQs or clarifications, giving basic responses – not for actually getting work done, as they cannot execute complex processes, make tool calls and take in relevant contexts.

AI Agents

This is where trail’s governance agents come in. Agents are usually part of broader software systems and use large language models to take in inputs, reason, and autonomously perform actions. Agent actions can include browsing the web, writing code or documents, or calling APIs, with the outcome of accomplishing a specific goal. Unlike a basic chatbot that just responds, agents can plan multi-step tasks, use lots of different tools, and loop through results until the job is done. Additionally, agents do not necessarily need to operate through a UI but can completely work in the background while still interacting with your tools. All of that can occur with direct user oversight or – ideally – in the background, while users focus on other tasks and come back later to finalized results.

Agents in GRC are yet uncommon, often marketed as agents even though they are more basic assistants or are heavily restricted by the underlying software system they are part of (e.g. available tool calls or GRC logic).

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Which governance agents does trail provide?

trail provides both off-the-shelf agents that have been tested in real scenarios and custom agent flows for teams who want to build and customize their own for their workflows.

Off-the-shelf agents

trail provides various agents that are commonly used for IT and AI governance tasks across businesses. They work in the background, directly with your asset’s information, documents, evidence, and context in trail. You can specify in which situation these agents should be triggered on to run automatically, such as every time an asset gets detected and enters your registry or every time a certain risk level is set. Once the given task is done, you receive a notification, such that you don’t need to constantly check and refresh the agent over and over again.

In trail, these include agents for:

• Policy Generation and Drafting
• Technical Documentation Generation
• Evidence Collection and Review
• Risk Analysis
• Risk Classification
• Pre-Audit Gap Analysis
• Control Assessments
• … and more

Custom agent flows

Agents are also customizable within trail. You can add all sorts of assessments, documents, SOPs or any other context that is needed for your specific governance workflow and industry freely. This also allows you to complete multiple tasks in parallel to automate complex workflows. At the end, you can decide which results to keep or ignore.

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What makes trail’s governance agents special?

Agent flows run when you want them to

You can leverage trail’s trigger and webhook functionalities to automatically set the conditions when an agent flow should run and start working. For example, a DPIA assessment flow can run whenever a use case involves personally identifiable information (PII), a vendor risk assessment flow can run whenever a new vendor is logged in the registry, or a whole AI intake and classification process can be triggered whenever a user submits a request for a new AI use case. Our trail agents work across governance objects, i.e. they can work and trigger on assets, requirements, documents and more.

Examples of triggers include:

• Linked Assets (e.g. vendors and systems included)
• Asset lifecycle stage
• Model type
• Use case characteristics
• Results of a questionnaire or assessment
• … and more

trail’s agents run in the background and don’t need constant “babysitting”

Unlike typical chatbots or AI automations, users do not need to “babysit” or watch trail’s agents – they run in the background. Rather than constantly refreshing and waiting for the agent to complete the tasks, you can just let the agent run and check back when the entire flow is completed and you get notified about it. In the meantime, you can work on other tasks.

Headless version as your automation layer

Large organizations run on many legacy systems and GRC tools, and you may want to keep your current stack rather than adopt yet another tool. That’s why trail can also run headless – putting its agentic capabilities to work across your existing governance stack (ServiceNow, Collibra, and others) without needing to use our UI, reading from and writing to your tools directly. This turns trail into your automation layer, shifting from helping your team do compliance to doing compliance in the background and involving humans only when a critical decision is needed.

Domain-specific context and Human-in-the-Loop

Speaks GRC natively (GRC Harness)

trail’s agents are fluent in GRC. Instead of bolting governance onto a general-purpose model at execution time, trail encodes GRC business logic – the relationships between assets, requirements, risks, controls, evidence and so on – directly into the tool surface that agents operate on. This means agents don’t have to infer how compliance works. They act on it natively, which is also what makes their outputs more accurate and trustworthy in a domain where generic models without the relevant context fall short.

That also allows you to bring your own AI, such as Claude or ChatGPT, and give it the respective “GRC harness” through trail, i.e. the context and tool surface to let your AI execute governance tasks reliably.

And because trail continuously benchmarks its agents against expert human workflows, their accuracy on real GRC tasks keeps improving over time – the tool surface gets sharper with every iteration, so your agents get better and better.

Efficient Human-in-the-Loop (Copy-on-Write)

While agents are great to automate complex tasks, they can make mistakes or may not always behave the way you expect them to. This is especially critical when agent changes have implications for compliance, audits, and dealing with sensitive use cases. At the same time, it is not feasible and reasonable to approve every single action of an agent by hand.

trail’s Copy-on-Write mechanism allows you to let agents work autonomously in a simulated environment with a complete and transparent audit trail and your real data first. You only need to review and approve agents’ actions and their proposed changes once before they write information into your live databases and systems. This gives you both automation and efficient control over your agents.

Learn more about Copy-on-Write for Agents and our open-source project.

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Deploy GRC agents that work safely and responsibly

trail’s governance agents allow both business and governance teams to automate complex IT and AI governance processes while making sure that these agents “don’t go wild”. Contact us to learn more about how trail can help you automate governance in your current situation.

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FAQ

What are trail's governance agents?

trail provides AI-powered agents built specifically for IT and AI governance tasks. Unlike general-purpose AI tools, trail's agents understand GRC business logic and trail offers a tool surface that is made for agentic interactions.

Why can't I just use my standard AI for GRC?

General-purpose AI tools weren’t built for GRC or AI governance. Without access to your organization’s governance context – assets, controls, evidence, requirements, and workflow history – they often produce generic answers and can miss critical details. trail’s agents operate inside a GRC harness and can pull the relevant evidence and system context to generate outputs that are more accurate and audit-ready. Our Copy-on-Write mechanism additionally ensures that outputs can be reviewed.

What off-the-shelf agents does trail offer?

‍trail includes ready-to-use agents for: Policy Generation and Drafting, Technical Documentation Generation, Evidence Collection and Review, Risk Analysis, Risk Classification, Pre-Audit Gap Analysis, and more.

Can the agents be customized to our governance process?

Agent flows in trail are customizable to fit your processes. You can automate complex workflows that run tasks in parallel or that are triggered under certain circumstances.

Do the governance agents run in the background?

Yes, trail's agents run in the background and notify you when a task is complete. You don't need to watch progress or manually advance steps – just set the agent in motion and check back when it's done.

Can trail work with my existing governance tools?

Yes, trail can be used in a “headless” version, integrating directly with your organization's existing (GRC) systems without requiring you to introduce a new tool to the whole organization. Think of it as an automation layer on top of your current governance landscape.

How does trail ensure agent actions are safe and don't create additional compliance risks?

trail uses a Copy-on-Write (CoW) mechanism for agents: any changes made by an agent are staged for human review and approval before being officially committed. All agent activity, approvals, and logs are recorded for full transparency in an audit trail.

Who can build and manage agent flows – do users need to be technical?

No, trail's agent flows are designed to be accessible to both technical and non-technical stakeholders. Domain experts who are not engineers can safely build, configure, and run agent flows using trail's chat interface or recording feature.