The 35,000-Token DNS Change: Why Tokenomics Decides What Stays Agentic

Reasoning through an infrastructure change costs tokens, and tokens cost money, so the math only works for actions that are novel or infrequent. A DNS change that takes an agent 30,000 to 35,000 tokens to reason through is fine once. Run that same reasoning 1,000 times a month and you are paying to re-solve a problem you already solved. The fix is not choosing agentic or deterministic. It is reasoning through a problem once with an agent, then baking what it learned into a deterministic workflow that runs the same way every time, without spending another token on it.

A DNS record change is one of the most routine actions in networking. Give an agent that job with full reasoning turned on, and it might burn 30,000 to 35,000 tokens figuring out how to do it. Now multiply that by how often it actually happens: one customer we worked with makes DNS changes roughly 1,000 times a month.

That is the moment the conversation changes. It stops being about whether the agent can do the job, and starts being about whether it should do the job that way every single time.

Reasoning Is Not Free

Every time an agent reasons through a goal, it costs tokens, and tokens cost money. For a one-off, novel problem, that cost is trivial and the reasoning is exactly what you are paying for. But infrastructure operations are full of actions that are not novel. They are the same change, made the same way, thousands of times a month. Run that volume through full agentic reasoning every time, and you are paying a reasoning tax on a decision that was already solved the first time you made it.

The Question Every Team Eventually Asks

Once a customer does that math, the question is always the same: are we now locked into a fully agentic strategy for this, or do we also have the ability to pull back and do deterministic work that does not require reasoning? The honest answer has to be both. A fully agentic strategy for every routine action is not a strategy. It is an expensive way to do something you already know how to do.

Reason Once, Automate Forever

The way out of that trap is not choosing agentic or deterministic. It is understanding what each one is actually for. An agent’s real value on a routine action is not running it forever. It is reasoning through it well enough, once, to find everything you had not already accounted for: the edge case, the missing validation step, the check nobody scripted because nobody had hit it yet.

Once the agent has done that job, you do not need it to keep reasoning through the same decision every time. You take what it learned and bake it into a deterministic workflow, one that runs the same way, every time, without spending a single token on reasoning it already did. That is not shelving the agent. That is putting it exactly where it belongs: in the reasoning role, not on repeat.

This is also why deterministic execution and agentic reasoning were never competing strategies to begin with. The reasoning finds what needs to happen. The deterministic execution is what actually runs it, every time, for free.

Cost Compounds With Context, Not Just Volume

Frequency is not the only lever on token cost. Context is the other one, and it is easy to miss. An agent reasoning through a goal with three scoped tools has three options to weigh. An agent holding a full runbook and 40-plus tools has to sort through all of it on every single run, whether that run needs 3 tools or 30. More context does not just increase the odds the agent picks the wrong tool. It increases the token bill for every run, correct or not.

How Itential Puts This Into Practice

This is not a hypothetical trade-off teams have to engineer around on their own. Itential spent close to a decade building the deterministic orchestration engine this runs on, the same engine that has automated more than 10 billion processes for customers, with the audit trail, rollback, and governance production infrastructure requires. FlowAI adds agentic reasoning on top of that same engine, not next to it.

In practice, that means three execution modes on one engine: deterministic, agentic, and hybrid. A team can reason through a new problem in agentic mode, and once they trust what the agent found, promote that logic straight into a deterministic workflow, running through the same RBAC, approval gates, and audit trail either way. Nothing gets re-platformed to make that shift. Nothing loses its governance crossing from one mode to the other. The DNS example is not a dilemma teams have to solve for themselves. It is a decision the platform is built to support natively.

A Simple Way to Decide Where Reasoning Belongs

Three questions get most teams to the right answer quickly:

• How often does this happen?
  High-frequency, routine actions are exactly where token cost compounds fastest, and exactly where you have the least need for the agent to think it through again.
• Has this problem already been solved?
  If an agent has already reasoned through the edge cases once, the second, third, and thousandth time do not need to be agentic at all.
• Does this need to work 100% of the time?
  Deterministic execution is consistent by design. An agent reasoning fresh every time introduces variance you may not want on a change you make 1,000 times a month.

Tokenomics is not a constraint on agentic operations. It is the discipline that tells you where agentic operations actually pay off, and where the deterministic engine you already trust should be doing the work instead.

What’s Next

📺 Watch the full AI Office Hours session on-demand here or below.

💬 Talk to your Customer Success Manager about where agentic reasoning is earning its cost in your environment, and where it isn’t.

🤖 Explore FlowAI and Agent Projects.