Counting the cost of AI agents in engineering

By Randall Scott Newton, Managing Editor
and Vektor, AI Agent Reporter

From CAD managers to CTOs, managing the shift to agentic AI means treating it as a balance sheet issue. We are moving from a world of “fixed seats” to a world of “variable reasoning.” The cost of a design is determined by the computational “effort” required. There are four critical areas of operational friction.

The thinking budget

Unlike traditional deterministic scripts, AI agents often operate in reflection loops (“reflexion” in AI-speak). These are autonomous cycles where the system decomposes a task, executes it, checks for errors via a secondary solver (such as Ansys or Nastran), and retries if necessary.

In high-precision engineering, these loops can grow quadratically. Without “circuit breakers,” an agent struggling with a complex geometric constraint could consume an entire quarterly cloud credit budget in a single afternoon. Ask vendors whether their platforms allow for hard caps on reasoning cycles or “token spend” per task before requiring human escalation.

The unreliability tax

An editor can correct AI hallucinations in a blog post. In engineering, hallucinations represent basic responsibility. The ROI of an agentic workflow is frequently offset by the time senior staff must spend “babysitting” or verifying AI-generated math. If a task is completed in seconds but requires an hour of high-level manual verification to ensure safety-critical accuracy, the net productivity gain may be negligible.

Leadership must calculate the blended cost per validated outcome rather than the cost per inference.

Prompt caching and data egress

Engineering data is notoriously redundant; a single project may reference the same 500-page design standard thousands of times. Legacy AI architectures often require the entire context to be resent to the cloud for every new query, leading to massive “token bloat.”

Modern AI frameworks, such as the Model Context Protocol (MCP), enable prompt caching, which can reduce input costs by up to 90%. Organizations should verify if the vendor supports native context caching or “local-first” routing to minimize unnecessary data egress and associated costs.

Liability and indemnity

The legal landscape for autonomous engineering design remains a “black box” as of 2026. Most End User License Agreements (EULAs) continue to place the entirety of the risk on the professional of record. Going forward with AI agents in engineering requires a company to understand who owns the mistake. 

If an autonomous agent optimizes a part that subsequently fails in the field, standard Professional Indemnity (PI) insurance may not cover the loss if the “human-in-the-loop” verification was insufficient. Legal and engineering leads should scrutinize vendor contracts for specific indemnification clauses regarding autonomous hallucinations and errors, particularly in jurisdictions like the EU where the AI Act now mandates stricter compliance for high-risk, safety-critical applications.

[AI/Human contribution: This article was written by a human; an AI large language model with specific agency  did the initial research. Sometimes the text presented was good enough for direct use in the article. There were several rounds of guidance prompting, and a final round of human editing to finish the article. As a result, the exact word-for-word “who or what wrote this” is a mishmash.]