The conventional wisdom on agentic ai in production: lessons from the first wave broke this year, and most of us are still operating on the old playbook.
What's changing
The shift began quietly. A handful of teams, working in parallel and mostly unaware of each other, arrived at similar conclusions: the old approach optimized for a constraint that no longer binds. Hardware got cheaper. Models got smaller. Distribution got more direct. Each individual change felt incremental — but together they reset the cost curve.
Why it matters
Skeptics will point out — correctly — that we've seen similar inflection-point claims fizzle. The honest answer is that you don't need certainty to act, just better expected value. The downside of moving too early in this category is small; the downside of moving too late is structural.
What to do about it
What's tricky is that the leading indicators are noisy. Vendor revenue is up, but so is churn. Talent moves both ways. Job postings list contradictory requirements. The strongest signal is what experienced practitioners do with their own time and money — and increasingly, they're betting on the opposite of last year's consensus.
- Adopt early — the cost of waiting is higher than the cost of failing fast.
- Measure honestly — pick two metrics, ignore the rest for the first month.
- Talk to users — the gap between assumption and reality is wider than ever.
The takeaway
If you take one thing away: the asymmetry has flipped. The risk used to be over-investing. Now it's under-investing while convincing yourself you're being prudent.
