The Baseline: Classical Cash-Flow Stream
In the freely available Construx Lunch & Learn session Software Engineering 101, Steve Tockey models how a for-profit company makes engineering decisions. To choose on economic grounds, the hypothetical company compares the cash-flow streams of several mutually exclusive proposals, including the development of a new payroll system.
The Classical Assumptions:
- Initial Investment: $250k
- Staffing Costs: $225k/FTE per year.
- Development: 9 developers in Years 1 and 2.
- Maintenance: 3 maintainers in Years 3 through 7.
- Income: Sales, upgrades, maintenance contracts, and training classes begin in Year 1.
Using a minimum acceptable rate of return (MARR) of 12%, Tockey calculates the present worth (PW) of the entire cash-flow stream. By discounting future cash flows back to today, the company can determine if development of a payroll system is a better investment than a some alternative (like "doing nothing" or restructuring another package). The resulting PW is $874k—a clear, economically driven justification for the project compared to other choices. Again, consult the previously mentioned session for more details.
The Twist: Agentic Cash-Flow Stream
Now, let’s imagine a modern alternative. The company decides to leverage agentic engineering. Instead of a team of 9 developers, they hire just one developer to orchestrate a fleet of autonomous AI agents. Because agents can generate code at breakneck speed, the project is delivered in a single year; actually much less, but let keep things simple. Instead of 3 human maintainers, they retain just one human maintainer to review and guide the agents during the maintenance phase.
The New Assumptions:
- Initial Investment: Let's assume $50k (the agentic platform subscription and infrastructure).
- Staffing Costs: 1 developer in Year 1; 1 maintainer in Years 2–7.
- Income: Matches the classical model exactly.
If we look at this through the lens of cost accounting, the choice is a no-brainer. You are saving millions in human payroll, accelerating time-to-market by a year, and drastically reducing overhead. The immediate "best practice" is obvious: adopt the agents.
Why the Cash-Flow Stream Breaks Down
In classical engineering economics, we can discount future cash flows because we understand the variables: human salaries are predictable, licensing costs are known, and maintenance effort is a historically grounded estimate.
In agentic engineering, the core variables are structurally indeterminate. We cannot map a cash-flow stream because of the various missing data.
1. The Unknown Token Liability
Where do tokens fit in our example cash-flow categories? They are an operating and maintenance cost. But how much will the single maintainer spend on tokens in Year 3 to fix a bug? An agent working on a large codebase must re-read vast amounts of context. Because agentic loops can be unpredictable—sometimes spinning endlessly on a single task—the future token consumption is a long-tailed distribution. We cannot convert present token values into future token values because we don't know the volume.
2. The Volatility of the Unit Price
Even if we could predict the volume of tokens, we cannot predict the price. Are we currently experiencing a temporary price war, or a permanent deflation in inference costs? More dangerously, what happens when the economic regime shifts?
We already saw this with GitHub Copilot. Users built workflows assuming a flat-rate economic model. Then, Copilot shifted to a usage-based billing model, and some users saw bills jump 10x overnight. If a vendor can re-price the fundamental building block of your agentic workflow with little warning, you cannot accurately discount future cash flows. The risk premium is unquantifiable.
3. The Maintenance Enigma
In classical engineering, maintenance is a known cost. In agentic engineering, maintenance creates a "verification debt." If an agent writes 10,000 lines of code in a week, how much human time will it take to verify it? Will the human maintainer need to use even more tokens to have an agent explain its own code? We are trading human salary costs for an unknown future liability of AI compute costs and human review time.
4. Commercial vs. Open-Source Shifts
If the company plans to use an open-source model to control costs, what happens when that model's maintainers pivot to a commercial model, or when the licensing terms change? The future state of the software supply chain is opaque.
The Cost-Accounting Fallacy
Because we cannot produce a defensible, discounted cash-flow stream for agentic variant, engineering teams are defaulting to cost-accounting choices. They look at the ledger today and say: “A token costs $0.001, a human costs $0.01. Therefore, we choose the token.” This is not engineering. As E. Paul DeGarmo et. al. notes, “...engineering is about finding the balance between what is technically feasible and what is economically acceptable.” Or citing Arthur Wellington, it is “...the art of doing that well with one dollar which any bungler can do with two...”
Right now, agentic engineering is entirely focused on how to spend that one dollar today, while ignoring the fact that the dollar is a variable-rate, unhedged liability that might cost ten dollars in Year 3.
Conclusion
We need a new framework for agentic engineering economics, a way to model:
- Regime-Shift Risk: How much architectural flexibility are we willing to pay for today to protect ourselves from future vendor price hikes?
- Context Decay: How much will it cost to re-feed an agent the context of a sprawling codebase in Year 4?
- Compute-Liability Discounting: How do we allocate the financial risk of infinite agent loops?
Until we can draw a cash-flow diagram for an agentic system and calculate a present worth against a classical alternative, we aren't doing real software engineering. The volatility of vendor-controlled token pricing and the unquantifiable nature of endless agentic loops pushes AI development beyond traditional risk models into pure speculation.
Comments
Post a Comment