Preparing for AI agent governance​: 3 pitfalls to avoid

AI has rapidly moved from a tool you ideate and brainstorm with to one that can act on your behalf. In the workplace, for example, AI agents now draft contracts, route approvals, flag anomalies in data, and coordinate across teams.

There’s little question that the models are capable, but companies struggle to deploy agents into existing, trusted workflows with governance, approvals, and human accountability. Whether you’ve yet to incorporate agentic AI into your workflows or are already building agent-driven experiences, pause to consider whether you can answer the following questions: How do you know what your agents are doing, and whether they're doing the right thing? What happens when an agent takes an action that a human wouldn't have approved?

This is what AI agent governance is about — ensuring that you have a plan for human-agent collaboration, and a way to observe and trace agent decisions and actions.

What is AI agent governance and why is it so important?

AI agent governance is how you define what AI agents can and can’t do within your organization. It typically involves creating policies that inform your processes and workflows, and determines the system access and permissions you grant to agents. Governance makes it clear what agents can see, what actions they can take, and when a human needs to review an output or take over, and it provides a way to track and audit an agent’s work. 

Without governance, you put your business at risk should the agent do something unexpected and potentially damaging — especially if you don’t have a line of sight into the decisions the agent makes or the information it’s drawing from. McKinsey’s 2026 AI Trust Maturity Survey found that only 30 percent of organizations have so far reached a maturity level of three or above, revealing a governance gap that puts a lot of businesses at significant risk as they race to deploy AI and realize its value. This is why it’s important to invest early in AI agent governance, so that you don’t need to retrace your steps later to figure out what went wrong. 

3 of the biggest AI agent governance challenges — and how to prepare 

Governance applies to each individual agent, deployed to handle specific tasks, across your org. But consider the potential agent sprawl across teams. Is everyone deploying agents the same way, held to the same standards? 

1. Lack of visibility into agent decisions and actions

One of the most common governance problems is that agents work quickly behind the scenes, often across many records, and don’t necessarily surface what they did and why. They’re basically invisible task-masters. However, if someone questions the output, it can be hard to trace the reasoning.

This is why observability matters. You don’t need to micromanage every AI decision, but autonomous execution should require continuous tracking of agent decisions. All agent actions should be logged and inputs recorded so that system interactions are traceable over time. 

What you can do:

2. Loose or undefined system access and permissions

Agents need a lot of context to make the best decisions. That said, agents shouldn’t be granted carte blanche (meaning complete or unconditional authority) access to your business. You are likely sharing sensitive customer and/or financial data with your agents, so carefully consider where an agent can read and write across your entire data landscape.

Teams can get into trouble when they deploy agents without being intentional about scope. Consider applying the “principle of least privilege” — which means giving access only to what's needed for the task or workflow. Often, this principle applies to humans as well, where the amount of information shared corresponds to an employee’s job role.

What you can do:

3. Assigning ownership and accountability

Agents are supposed to be working for you, but like any good manager, you need to check in. When and how often can be difficult to determine. Ideally, it’s before a mistake is made, although sometimes mistakes happen.

For humans, there’s usually a clear accountability chain, but working with AI is different and it can be easy to point fingers when something goes wrong. Was the problem the individual prompt? Or the way the technology was implemented and trained? Is it the vendor’s fault? This is why it’s vital to establish and align on HITL workflows: when and where are you checking in along the way?

What you can do:

Confidently define governance and scale agents with Airtable 

The old “move fast and break things” motto doesn’t apply to AI adoption. If you move fast, without the appropriate guardrails in place, something is likely to break — which can be costly. 

Instead, build a clear foundation and invest in a system of record that provides visibility into AI agent actions, allows you to set granular permissions, and keeps humans in the loop. Airtable is that system of record, providing both humans and agents a shared, transparent operational surface to work from. The workflows you define become your governance layer, and are fully auditable.

Ready to see how it works?