AI agents are easy to add. Running them reliably isn't.

Every SaaS platform seems to be adding an agent to something right now — support, sales, onboarding, internal tooling. The barrier to trying one has genuinely dropped: a capable model, an API key and an afternoon is often enough for a working prototype. That’s real progress. It’s also created a gap that doesn’t get talked about as much as it should: the distance between “we added an agent” and “we can trust this agent in production.”

The demo isn’t the hard part

An agent that works well in a demo, on the ten inputs its builder happened to test, tells you very little about how it behaves on input eleven — the edge case, the ambiguous request, the adversarial prompt, the day the upstream API it depends on changes its response format. Traditional software fails in ways that are mostly deterministic and reproducible. Agents built on top of language models can fail in ways that are plausible-sounding and easy to miss until a person actually reads the output.

That’s not a reason to avoid them. It’s a reason to treat “the agent works” as the start of the operational question, not the end of it.

What actually needs to exist before an agent is genuinely production-ready

Someone reviewing what it’s actually doing, not just whether it ran. A successful execution and a correct execution aren’t the same thing for an agent the way they usually are for a script. Logging that the agent “completed successfully” tells you almost nothing on its own.

A clear boundary on what it’s allowed to do unsupervised. Drafting an email for a human to send is a very different risk profile from sending it directly, or taking an action against a customer account. The more autonomy an agent has, the more that boundary needs to be a deliberate decision — not just whatever scope was easiest to wire up first.

A plan for when it’s confidently wrong. Models can be fluent and wrong at the same time, which is a genuinely different failure mode from most software bugs. Knowing how a wrong output gets caught — before a customer sees it, ideally — matters more than optimising for how often the agent is right.

Cost and usage that’s actually tracked, not assumed. Agentic workflows can call a model multiple times per task, sometimes with tool calls and retries in between. Without visibility into that, it’s easy to discover the economics of a feature only after it’s already shipped and scaling.

The pattern, again

This is, structurally, the same argument we’d make about any part of a platform: the risk isn’t usually in building the thing, it’s in what happens after it’s live and nobody’s specifically watching it anymore. Agentic AI just makes the stakes a little higher, because the failure mode is less predictable and the output looks confident regardless of whether it’s correct.

The platforms that get real, durable value out of AI agents this year won’t be the ones that shipped first. They’ll be the ones that treated the agent as a production system from day one — with the same operational seriousness as everything else they run.