28 May 2026 · 8 min
Wiring agents together is easy. Making them reliable, cheap and safe enough to trust is the actual project.
The demos are intoxicating. An AI agent takes a vague instruction, breaks it into steps, calls tools, browses, writes code, and hands back a finished result — apparently thinking for itself. It is genuinely one of the most exciting things happening in software. It is also where more projects quietly fail than almost anywhere else in applied AI, and the reason is always the same: the gap between an agent that works in a demo and one you can trust in production is enormous, and it is made of exactly the unglamorous engineering that demos skip.
This is not an argument against agents. It is an argument for building them soberly, with a clear head about where autonomy earns its keep and where it just adds ways to fail.
A demo shows the happy path once. Production runs the unhappy paths thousands of times. In a multi-step agent, small error rates compound: if each step is ninety-five percent reliable, ten steps in sequence are only about sixty percent reliable end to end, and twenty steps are a coin flip. The demo succeeded because someone ran it until it worked. Production does not get that luxury, and the compounding is unforgiving.
This is the single most important fact about agent reliability, and it is invisible in any single successful run. It only shows up at scale, which is exactly when it is most expensive to discover.
An unbounded agent — one that can loop, retry and call tools without limit — is a liability. It can spin forever, rack up cost, or wander far from the task. The first discipline of reliable agents is bounding them: explicit limits on steps, retries, time and spend, so that failure is contained and predictable rather than open-ended. An agent that fails cheaply and visibly is far better than one that fails expensively and silently.
These bounds are not a constraint on capability; they are what make the capability safe to deploy. A well-bounded agent that stops and asks for help when it hits a limit is production-grade. An unbounded one that usually works is not.
When a ten-step agent produces a wrong answer, "the AI got it wrong" is not a diagnosis you can act on. Reliable agents carry explicit, inspectable state: what was decided at each step, which tool was called with what arguments, what came back. When something fails, you can see exactly where and why, and fix that step rather than re-rolling the dice on the whole chain.
Without this, debugging an agent is guesswork, and a system you cannot debug is a system you cannot improve. The state model is not an afterthought; it is the backbone that makes the agent maintainable.
The most valuable judgement in this whole area is knowing where an agent genuinely helps and where plain, deterministic code is simply better. If a task has a fixed sequence of well-defined steps, writing it as ordinary code is faster, cheaper, more reliable and easier to test than handing it to an agent that must rediscover the sequence each time. Agents earn their complexity when the path genuinely varies with the input and cannot be enumerated in advance.
Reaching for an agent because agents are exciting is a common and expensive mistake. We are deliberate about drawing this line, and often the strongest design uses an agent for the genuinely open part of a task and deterministic code for everything around it.
For any consequential action — sending, paying, deleting, publishing — a reliable agent does not act unilaterally. It proposes, and a human confirms, or it operates within tightly scoped permissions where the worst case is acceptable. The point is not distrust of the model; it is that the cost of a rare bad action is often far higher than the cost of a confirmation step, and good design weighs the two honestly.
Where the checkpoint goes, and how light it can be without becoming rubber-stamping, is a real design decision. Done well it makes the agent both safe and pleasant to use.
Evaluating an agent by watching it succeed tells you almost nothing. What matters is how it behaves when a tool returns an error, when input is malformed, when a step produces nonsense, when it hits its bounds. We build evaluation that deliberately exercises these paths, because the failure modes are where reliability is won or lost. An agent that degrades gracefully under failure is trustworthy; one that only shines on the happy path is a demo wearing a production costume.
The reliable agent is almost always less magical than the demo. It is bounded, inspectable, human-checked where it matters, and deterministic wherever it can be. That is not a compromise of the vision; it is the vision made real. The exciting autonomous behaviour survives exactly where it adds value, and everywhere else the boring engineering carries the weight. That is how you get an agent people actually depend on rather than one they demo and quietly retire.
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