The AI Assistant That Does Everything Does Nothing
An agent's reliability collapses as you add systems it must be simultaneously correct about. The only agents that reach production are ones you could write a job description for.
Bence FedorIT Solution ArchitectCompound reliability is arithmetic, not a model limitation
At 95% accuracy per step, a twelve-step end-to-end process succeeds 54% of the time. Three steps succeed 86% of the time. That is multiplication, and a better model does not rescue it.
A scoped agent reads like a job title: turns an inbound portal enquiry into a CRM record with budget, size, location and move-in date populated, then creates a task for the owning agent. Not "handles enquiries".
The four-part scoping test
One input channel. One output system. One named human owner. One number that moves. If any of the four is plural, split the agent in two.
The classic overreach in hospitality is an AI concierge expected to answer booking questions from the property management system, recommend restaurants and handle complaints. That is three knowledge bases, three escalation paths and three owners. It ships in month nine and gets switched off in month ten.
What the timelines actually look like
A single-handoff agent goes live in two to four weeks and pays back inside a quarter. Assistant projects run six months or more, and their most common end state is a demo nobody uses.
The failure mode is scope creep during the build, usually requested by the sponsor. Every added capability multiplies the test surface and dilutes the owner's accountability.
Write your intended agent's job description as though you were hiring a person: one paragraph of duties, one KPI, one manager. If it does not fit on half a page, you do not yet have a project.
Designs the API and integration layer that automation depends on, across systems never built to connect.
Meet the team →Questions we get asked
How many AI agents should a mid-sized company end up running?
More than you expect, and each narrower than you expect. Companies that succeed typically end up with eight or nine narrow agents rather than one broad assistant — and they never merge them. Narrow scope is the permanent architecture, not a cautious first phase.
Why does adding capabilities to an agent reduce reliability so sharply?
Because step accuracy multiplies. Twelve steps at 95% each gives 54% end-to-end success. Every extra system the agent must be correct about adds steps, so reliability falls geometrically rather than linearly.
How long should a first AI agent take to deploy?
Two to four weeks for a properly scoped single-handoff agent, with payback inside a quarter. If the timeline you are quoted runs to six months, the scope is too broad.
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