Who gets paged when an AI agent quietly approves a refund it shouldn’t, emails the wrong customer, or burns through ten thousand dollars in tokens overnight? Is it the engineer who wrote the prompt, the PM who pushed the feature, or the Ops lead staring at a dashboard at 3 a.m.? And when everyone joins the call, who actually has the authority to stop the system?

If you’ve ever sat through that silence, you already know the answer is rarely written down.

The uncomfortable truth about “ownership” in agentic systems

In traditional software, ownership is boring in a good way. A service has a team, a pager, and a runbook. When it breaks, the blast radius is at least predictable. AI agents break that contract the moment they’re promoted from demo to production.

An agent is not just code. It’s code plus prompts, models, tools, policies, data, and runtime behavior that shifts over time. That makes “ownership” feel fuzzy, and fuzzy is deadly in production. I’ve watched teams argue in real incidents about whether a bad outcome was a “model issue,” a “product decision,” or an “infra blip.” Meanwhile, the agent kept running.

The first hard stance I’ll take is this: if an AI agent can affect customers, money, or trust, it must have a single accountable owner in production. Not a committee. Not a shared spreadsheet. One owner who can be paged, who can shut it off, and who feels the consequences when it misbehaves.

That owner is almost never Product, and it’s not Ops either.

Why Product can’t own AI agents in production

Product teams are excellent at defining outcomes. They are terrible at being on-call, and that’s not an insult. Their job is to decide what should happen, not to debug why it happened at 2 a.m.

In agentic systems, behavior emerges. A PM can specify that an agent should “resolve simple support tickets,” but they can’t anticipate every tool call chain, edge-case prompt completion, or downstream API failure. When the agent starts looping, hallucinating confidence, or taking an action that technically satisfies the spec but violates common sense, someone has to intervene fast.

Ownership means authority plus responsibility. Product can and should define guardrails, risk tolerance, and success metrics. They should not be the final owner of a system that requires incident response, rollback decisions, and deep technical triage. Every time I’ve seen Product “own” an agent in name, Engineering quietly absorbed the real burden.

That mismatch always surfaces during the first serious incident.

Why Ops alone can’t own AI agents either

There’s a tempting argument that AI agents are “just another service,” so Ops should own them. After all, Ops already handles uptime, scaling, and incident response. The problem is that agent failures are rarely just infrastructure failures.

When an agent makes a bad decision, the root cause might live in prompt logic, tool semantics, model selection, or data freshness. Ops can restart pods and roll back deployments, but they can’t rewrite intent. They also shouldn’t be making judgment calls about whether an agent’s behavior is acceptable from a product or ethical standpoint.

I’ve seen Ops teams forced into impossible positions, asked to keep a system alive while not fully understanding what “correct” behavior even looks like. That’s not operational maturity; that’s abdication disguised as process.

Ops is essential, but ownership without design authority turns them into firefighters with no map.

Engineering owns AI agents in production, whether they like it or not

If we’re being honest, Engineering already owns AI agents in production in practice. They build the orchestration, choose the models, define system boundaries, wire up tools, and get paged when things go wrong. The mistake is pretending this ownership is shared equally with groups that aren’t equipped to carry it.

Engineering ownership doesn’t mean engineers make product decisions in a vacuum. It means they are accountable for the agent’s end-to-end behavior once it crosses the production boundary. That includes how it fails, how it’s observed, and how it’s stopped.

In every healthy setup I’ve worked in, the owning engineering team treats the agent like a living service with agency. It has a lifecycle, not just a release. It has failure modes that are documented and rehearsed. It has a clear kill switch. That mindset is the difference between experimentation and production-grade agentic systems, which I’ve explored more deeply in production-grade agentic systems .

who owns AI agents in production

The cleanest answer is also the least satisfying: the team that can be paged and can change the system owns the agent in production. In practice, that’s an engineering team with explicit operational responsibility.

Ownership here is not about credit. It’s about accountability. When an AI agent does something harmful, the owner must be able to answer three questions immediately. What happened, why did it happen, and how do we prevent it from happening again?

If those answers depend on another team being awake, ownership is broken.

This doesn’t mean Engineering works alone. Product informs intent. Legal and compliance set constraints. Ops provides the runtime and incident muscle. But one team must sit at the center, synthesizing all of that into a system that behaves predictably enough to trust.

AI agent responsibility engineering vs product vs ops

The healthiest way to think about responsibility is as layers, not silos. Product owns the “should.” Engineering owns the “how.” Ops owns the “keep it alive.”

Problems start when those layers blur. When Product sneaks implementation decisions into prompts without review. When Engineering ships agents without on-call rotation. When Ops is asked to guarantee behavior they can’t influence.

In mature teams, Engineering acts as the integrator. They translate product goals into agent behavior, operationalize constraints, and work with Ops to ensure the system can survive real-world abuse. They also push back when goals are unsafe or underspecified, which is an underappreciated part of ownership.

This is where human-in-the-loop safeguards stop being a design pattern and start being an ownership tool. Deciding when a human must intervene is not a UX choice alone; it’s an operational one.

A brief digression: why this keeps failing in otherwise smart teams

I want to pause here, because I’ve seen very capable teams repeat the same mistake. They treat AI agents as features, not systems. Features get roadmapped. Systems get owned.

When an agent launches as a feature, it inherits the product development lifecycle. Success is measured in adoption and engagement. Failure is measured in bugs. The moment that agent starts acting independently, those metrics stop telling the whole story.

Teams delay defining ownership because everything seems fine. Then the first serious incident hits, and suddenly everyone is involved and no one is in charge. Postmortems get written, action items get assigned, and ownership remains vague. The cycle repeats with more expensive consequences.

The digression ends with a simple lesson: if it can wake someone up at night, it’s not a feature anymore.

operating AI agents in real-world systems

Real-world systems are messy. Inputs are ambiguous. Dependencies fail in creative ways. Users do things you didn’t design for. AI agents amplify all of that because they don’t just fail; they improvise.

Operating agents means accepting that correctness is probabilistic and context-dependent. That makes observability non-negotiable. Logs alone won’t save you when an agent makes a “reasonable” but wrong decision. You need traces of reasoning, tool calls, and state transitions to reconstruct intent after the fact.

This is why I push teams to invest early in agent observability signals . Ownership without visibility is just liability.

Operating also means designing failure as a first-class behavior. Agents should fail smaller than the system they live in. They should degrade gracefully, not catastrophically. When they can’t complete a task safely, they should stop and escalate. That philosophy shows up in graceful failure paths , but it starts with ownership clarity.

MLOps vs DevOps is the wrong argument

I still hear teams debate whether AI agents belong to MLOps or DevOps. In my experience, that argument is a distraction. The real question is whether the owning team understands both model behavior and production constraints.

Agents sit at the intersection. They need model versioning, prompt evolution, and evaluation, but they also need deployment pipelines, rollbacks, and on-call rotations. Splitting ownership along organizational lines almost guarantees gaps.

The teams that get this right don’t argue about labels. They build hybrid practices and accept that agent lifecycle management spans disciplines. What matters is that someone owns the integration points and feels responsible when they break.

Defining ownership before the first incident

If you take nothing else from this, take this: define ownership before you need it. Write it down. Decide who gets paged, who can shut the agent off, and who approves changes to its behavior.

That decision will feel premature during early success. It will feel political. It will feel like slowing down. Then, one night, it will feel like the only reason a bad situation didn’t get worse.

Ownership is not about control for its own sake. It’s about creating a system where accountability matches reality. AI agents don’t care about your org chart. Production will expose whatever you leave ambiguous.

If you’d benefit from a calm, experienced review of what you’re dealing with, let’s talk. Agents Arcade offers a free consultation.