Historically, engineers gained an intimate understanding of business logic, conflicting workflows, and system constraints through the manual effort of writing and refactoring exploratory code. While modern agents produce working demos that compile and pass test suites, they can easily embed flawed assumptions and misinterpret critical domain rules. Because agents often lose context despite documentation, developers should leverage lower experimentation costs to generate between three and seven disposable spikes per problem. This comparative approach ensures teams thoroughly evaluate architectural trade-offs, shifting data models, and long-term workflow impacts before committing to final cod…
Key points
Promoting agent-generated spike code straight to production risks losing the deeper archi…: Promoting agent-generated spike code straight to production risks losing the deeper architectural insights that manual refactoring traditionally provided.
Teams must intentionally evaluate trade-offs and identify affected application models bec…: Teams must intentionally evaluate trade-offs and identify affected application models because documentation rarely prevents agents from losing context.
Reduced experimentation costs should be used to explore multiple disposable architectural…: Reduced experimentation costs should be used to explore multiple disposable architectural alternatives rather than creating permanent production code immediately.
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Over the last year or two of working with coding agents, I’ve noticed a shift in how we approach spikes. We start with a question, ask an agent to explore it, and quickly end up with something that looks surprisingly complete. It runs. It might have tests. We can demo it to the team. At that point, it is tempting to ask the agent to clean up a few things and turn the spike into production code.
I understand the appeal. But I think we can lose a lot of the value of a spike when we move directly from “this works” to “let’s keep it.”
In 2022, I wrote about creating spikes with lasting value. A spike is a bounded experiment to explore a problem, evaluate possible solutions, and understand the work ahead. My emphasis was on preserving what we learned. With agents doing more of the implementation, I think that requires even more intentional effort.
Why spikes feel different
The distance between articulating an idea and having reasonable code to explore it has become much shorter in my…
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