Dimitris Kyrkos argued on July 31, 2026, that a single all-purpose AI agent can look efficient in a demo but becomes fragile in production because one prompt, one API call and one mental model must handle unrelated tasks under real traffic. The article says the pattern often hides costs until teams face outages, token waste and regressions across thousands of edge cases.

Kyrkos described a support agent that begins with product questions and grows into a 4,000-word system prompt covering billing, refunds, technical troubleshooting and tone rules for three customer segments. Adding edge case #41 can change how the same model handles edge case #12 because both instructions compete inside one attention budget, leaving teams unable to isolate the line that caused the regression.

The article says every request through a monolithic agent pays for unused capabilities, including simple high-volume intents such as “what’s my order status” that could be handled by a five-line function. It also says routing, task execution and formatting in one inference pass (one model run) lets one bad instruction affect everything downstream, with no boundary to catch drift before the user sees it.

Kyrkos said testing a 4,000-word do-everything prompt becomes “a probability exercise” because teams may rerun the same conversation a dozen times and hope failures stay under a tolerable threshold. His proposed fix is to route rather than merge: a small classifier decides intent, specialized functions handle isolated tasks, non-LLM code owns routing, state and final formatting, and each piece becomes small enough for unit tests.

Dimitris Kyrkos argued on July 31, 2026, that a single all-purpose AI agent can look efficient in a demo but becomes fragile in production because one prompt, one API call and one mental model must handle unrelated tasks under real traffic. The article says the pattern often hides costs until teams face outages, token waste and regressions across thousands of edge cases.

Kyrkos described a support agent that begins with product questions and grows into a 4,000-word system prompt covering billing, refunds, technical troubleshooting and tone rules for three customer segments. Adding edge case #41 can change how the same model handles edge case #12 because both instructions compete inside one attention budget, leaving teams unable to isolate the line that caused the regression.

The article says every request through a monolithic agent pays for unused capabilities, including simple high-volume intents such as “what’s my order status” that could be handled by a five-line function. It also says routing, task execution and formatting in one inference pass (one model run) lets one bad instruction affect everything downstream, with no boundary to catch drift before the user sees it.

Kyrkos said testing a 4,000-word do-everything prompt becomes “a probability exercise” because teams may rerun the same conversation a dozen times and hope failures stay under a tolerable threshold. His proposed fix is to route rather than merge: a small classifier decides intent, specialized functions handle isolated tasks, non-LLM code owns routing, state and final formatting, and each piece becomes small enough for unit tests.