Automatización / Nota de campo
Tu primer agente de IA: guía de campo para 2026
La mayoría de los proyectos de agentes fracasan por el alcance, los datos y una aprobación poco clara, no por el modelo. Empieza con una tarea, prototipa con datos de ejemplo y mantén a una persona en cada bucle consecuente.
Esta nota de campo se publica en inglés.
By 2026 the question has stopped being "can AI do this?" and become "can we trust it to, and who checks?" Business owners now widely believe AI assistants improve customer experience, and a large share of common customer questions can be handled automatically. The gap between that belief and a dependable production system is usually not the model — it is the scope, the data and the approval design around it.
Why this year is different
The first wave of AI projects optimised for the demo: impressive, disconnected, hard to trust with real work. The second wave — the one teams are building now — optimises for the workflow. Inputs are defined, sources are named, outputs are reviewable and every consequential step pauses for a person. That is the difference between a chatbot that answers and an agent a business can rely on.
Start with one task
Pick one repetitive job with a clear source of truth. Not "automate the operations team" — something like "compare every product record against the supplier feed and flag missing fields." A narrow task makes the next decisions concrete: what is the input, what is the output, what is an observation versus a proposal, and who is allowed to act on each.
Prototype on sample data
Before connecting any tool, run the workflow on a small, varied set of inputs: a complete record, an incomplete one, a contradictory one and an unavailable source. Watch where the process becomes uncertain. Those moments tell you what to fix in the data, the instructions or the handoff — far cheaper to discover on a sample than in production.
Design the review, not just the output
Show the current value beside the proposed one, name the source, and make editing or rejecting straightforward. Reading a public page and publishing a new description are different decisions; the interface should treat them differently. A review screen that reflects the real consequence of the action is what makes an agent safe to leave unattended.
Evaluate before you roll out
Define what good looks like before launch — accuracy on a held-out set, how often a human needed to correct it, and how long each task took. Add guardrails for out-of-scope questions and an audit trail of what the agent proposed and what happened next. Measure after launch too: an agent that quietly drifts from its brief is worse than one you never trusted.
Keep a person in the loop
Human-in-the-loop is not a compromise — it is the feature that makes adoption possible. The controls should match the consequences: a draft can be automatic, a publish decision never should be. Most agents that get cancelled in 2026 will be cancelled because nobody could see what they were doing. Make progress visible, make decisions reviewable and the rest follows.
