I change my ai agents at least once a week.
Usually it’s because I learn something new from the data I’m getting back.
Or something breaks like a prompt that produces the wrong tone. Or I notice a small friction that's been bothering me for days.
And here's what surprised me: the flexibility is almost too tempting.
With SaaS, you accept imperfection because you have no choice. The vendor ships what they ship. You adapt. With custom agents, you can always make it better. So you do. Constantly.
That's the honest trade-off in the "build vs. buy" debate.
Not maintenance burden vs. clean subscription. It's this: Will you use the flexibility to compound improvements, or will you use it to chase perfection instead of shipping?
The answer determines whether custom AI becomes an appreciating asset or an endless distraction.
The Compounding Effect (When You Get It Right)
Companies that build internal AI capability and dedicate someone to tune their agents get something SaaS can't provide: accumulated intelligence about their own processes.
Every tweak teaches the system more. Performance improves month over month.
The knowledge stays in-house. Institutional expertise compounds.
My marketing agents are meaningfully better than they were three months ago.
Not because I rebuilt them. Because dozens of small adjustments stacked. A prompt tweak here. A quality gate there. Each one took maybe ten minutes.
Together they changed what the system can produce.
Companies that buy off-the-shelf get a product designed for the average customer. Same tool their competitors use. No accumulated advantage. Whatever the vendor ships, that's what you get.
The first group is building an appreciating asset. The second is renting a commodity.
But here's the catch: the first group can also waste enormous time chasing marginal improvements. The flexibility that enables compounding also enables perfectionism.
The Discipline Problem
The "you need developers" objection is dying. Tools like Claude Code let non-developers build and modify AI agents through conversation. Microsoft Foundry and Google Vertex handle deployment infrastructure. The technical barriers have collapsed.
The new barrier is discipline.
When you can change anything, you're tempted to change everything. I catch myself adjusting agents when I should be using them. Optimizing when I should be shipping.
The skill isn't programming. It's knowing your own processes well enough to explain them clearly, and having the restraint to improve on a schedule rather than on impulse.
That's a business analyst role with guardrails. Maybe your best operations person with a weekly tuning session on the calendar. Someone who knows the business cold, can learn prompt patterns in a few weeks, and won't disappear into endless optimization.
What This Means for Your Business
Four implications:
Reframe the question. Stop asking "can we maintain it?" Start asking, "will this compound or stagnate?"
The hiring priority shifts. You're not looking for AI engineers. You're looking for process-minded people who can work with AI daily and ship imperfect improvements on a regular cadence.
Build in constraints. Weekly tuning sessions, not constant tinkering. A backlog of improvements you batch, not an open invitation to optimize whenever something bothers you. The discipline matters as much as the capability.
Your competitors who figure this out will pull away. Compounding advantages are brutal. A 10% improvement every month for a year is a 3x multiplier. That gap doesn't close easily.
What to Watch
Three signals I'm tracking:
New job titles. "AI Operations Analyst" or similar roles that don't require engineering backgrounds but do require structured improvement cycles.
Vendor frustration. Companies realizing their SaaS AI tools aren't improving while their internal capability could be. The ceiling becomes visible.
Discipline frameworks. Best practices emerging for how often to tune, when to ship "good enough," and how to resist the perfectionism trap.
The maintenance objection will persist. It's comfortable. But the companies treating AI capability as an appreciating asset - with the discipline to compound rather than tinker - will pull past those treating it as a subscription.
I pay the maintenance cost weekly. But I'm paying it to compound, not to stand still.
What are you seeing? Is the build vs. buy conversation shifting in your industry?