AI accountability: Who's responsible when agents make bad calls?

Many organizations already have AI agents operating in their cloud environments. However, many leaders still don’t have a clear view of what those agents are doing, what they can access, or how much autonomy they have. 

Let’s assume you’re a step ahead. You’ve inventoried your environment, and you know what’s running. Do you know who’s responsible for each agent when one makes a bad call?

Many organizations haven’t defined this yet. It’s not because they don’t care about accountability. It’s because they’ve updated their systems, but not their operating model. And that gap matters more than many teams realize.

Learn why visibility is so important for AI agents.

With people, accountability is usually easy to understand. If someone makes a decision that causes a problem, there’s a person to talk to: their boss! You can understand what happened, walk through the reasoning, coach them, and adjust the process so the same issue is less likely to happen again. The outcome may still be messy, but the chain of responsibility is usually clear enough to work with.

With AI agents, that clarity starts to break down. The agent made the decision, but the agent isn’t sitting in a review meeting explaining what happened. The engineer who built the system may not know the decision was made. The leader who approved the deployment may not understand how much authority the agent was given. And the person affected by the outcome just knows something went wrong.

That is where many organizations are right now. They adopted agents to move faster, but they never revisited accountability now that some decisions within the business are no longer made directly by humans. 

The technology moved forward. The governance model did not.

When I talk about an AI agent making a bad call, I’m not only talking about dramatic failures that take down a system or trigger a major incident. Those are obvious. The more common issue is the silent kind of failure that slips into production, causes confusion or damage, and goes undetected until someone downstream feels its impact.

That might occur when an agent:

• Approves a refund that it should have rejected

• Sends a customer message with inaccurate information

• Escalates a case to the wrong team and adds days to the resolution timeline

• Summarizes a document and leaves out one critical detail that someone else later relies on to make a decision

These are the kinds of failures that make accountability so important. They aren’t always dramatic in the moment, but they create real business consequences. And when leadership starts asking who let it happen, the answer is often unclear because nobody ever defined ownership of the agent’s behavior.

A single-agent mistake is one thing. Multi-agent systems raise the stakes—and the need for AI governance.

As organizations move towards more connected agent architectures, agents aren’t just calling tools and interacting with data. They’re also passing work to other agents, relying on each other’s outputs, and operating across workflows that span multiple systems. That opens the door to more complex failure modes and makes root cause analysis much harder.

Take prompt injection as one example. Let’s say an agent receives input that manipulates its behavior, causing it to take an action it was never supposed to take. 

In a single-agent system, that may still be contained. In a multi-agent system, the manipulated output can move downstream as if it were trustworthy. Another agent treats it as valid input. Then another system acts on it. By the time a person notices the result, the original issue may be buried several steps back in the chain.

Tool misuse is another example. An agent may call the correct tool with the wrong parameters, or call the wrong tool entirely. In a more isolated setup, that may be easier to contain. 

In a chained system, a mistake early in the flow can produce outputs that appear believable enough to keep moving. That is one reason multi-agent systems can be so difficult to manage. The results may look coherent even when the underlying logic has already gone off course.

Then there’s data leakage. Once agents access multiple systems through tools, services, or other agents, the surface area for potential problems expands. Every access point requires well-defined oversight.

As multi-agent systems become more common, leaders don’t need to become protocol experts. They need to understand the layers where control and AI accountability live.

One of those layers is Model Context Protocol, or MCP. MCP defines how agents connect to tools and data sources. In practice, that means it plays a major role in determining what an agent can reach, use, and incorporate into its decision-making process. If an agent has access to something it shouldn’t or can call tools beyond its intended scope, that’s a control problem that should raise leadership concerns.

The second layer is Agent-to-Agent, or A2A communication. A2A governs how agents communicate and pass work between each other. Once an agent starts handing off work to another agent, failures travel faster and further. Problems don’t stay isolated when one agent hands bad output to another agent as input. 

Leaders don’t have to configure MCP or A2A themselves. Instead, they should gain AI transparency and ask their teams whether: 

• Agent access is properly scoped

• Communication paths are monitored

• Validation checkpoints are in place

• Anyone has access to what could go wrong if connections are misused or manipulated

One of the biggest mistakes organizations make is assuming accountability will sort itself out later. It usually doesn’t.

There’s often a tendency to say the engineering team owns the agent because they built it. That sounds reasonable until you look at how these systems actually evolve in production. 

The engineering team may have built the original version six months ago, but since then, the business process may have changed several times. The data sources may have expanded. The tools the agent can access may be different. The level of autonomy may have increased. 

If nobody updated ownership and oversight along with those changes, then saying engineering owns it is not really a good accountability model.

Real accountability for AI agents has to be explicit and current. Every agent in production should have a clearly identified human owner, not just a team name or the group that originally built it. 

A real person should be responsible for understanding what that agent is supposed to do, whether it’s still aligned to the business outcome it supports, and if its permissions, boundaries, and behavior still make sense.

That person should be able to answer basic questions without hesitation: 

• What is this agent allowed to do on its own? 

• What decisions still require human review? 

• What should happen when the agent encounters something outside its boundaries? 

• Who gets notified when that happens? 

• Who has the authority to pause or shut the system down if needed?

If organizations can’t answer these questions, they don’t really have an AI accountability model.

Leaders already understand the risk. The issue is that agent deployment is outpacing AI governance and operational discipline. Teams are building because they have access to the tools that allow them to. Leadership is trying to catch up to what’s already in motion. 

That gap is not going to stay manageable for long.

Organizations that take AI risk management and accountability seriously now will be in a much stronger position than those that wait until something goes wrong in production. By the time a failure forces the conversation, the damage may already include customer impact, compliance concerns, data exposure, or just a loss of trust in the system itself.

For leaders, the real work is making sure innovation has structure. If an AI agent is allowed to make decisions or take action within your business, you need to define ownership.