Ultra-fast Code Does Not Guarantee the Right Result
The emergence of AI agents has dramatically changed the cost of writing code: a service prototype or a new feature can now be created in mere hours. However, Specification-Driven Development (SDD), where models are fed a detailed description of the entire future architecture, often leads to a paradox. The team reinvents classic Waterfall, only operating at maximum speed.
If a specification is based on an unverified hypothesis, the agent will rapidly generate code, automated tests, and infrastructure for a solution that turns out to be unviable. In the end, resources are spent not on saving time, but on rewriting a large volume of unsuitable codebase.
Why the Bottleneck Has Shifted
Previously, the key constraint in processes was the speed of implementation. AI removed this barrier, but exposed stages that cannot be completely delegated to algorithms:
- Checking legal constraints, internal API access permissions, and data security;
- Assessing architectural impact on neighboring services;
- Conducting thoughtful Code Reviews and taking responsibility for releases;
- Organizing safe deployments and rollback mechanisms.
When code generation outpaces a team’s ability to validate and approve it, the bottleneck simply shifts to the verification and review stage.
Where SDD for Neural Networks Works Perfectly
Specifications provide the greatest value in isolated, repeatable scenarios with strict rules. For example, when configuring data flow integrations in anti-fraud systems. In such tasks, the team only needs to provide the agent with:
- Formal grammar and configuration schemas;
- Patterns and typical implementation examples;
- Automated test templates for verifying results via CI/CD.
Under these conditions, the agent operates within strictly defined boundaries, accelerating routine operations and allowing non-engineering specialists to be involved in the process under developer supervision.
A Return to Extreme Programming Principles
To avoid the risks of high-speed Waterfall, AI-assisted development must be based on agile principles and short feedback loops:
- Specify only the next immediate step: formulate a testable hypothesis rather than designing the entire system before the first test;
- Work in small batches: isolated changes behind feature flags are easier to review and safer to test on real users;
- Use pair programming: the developer acts as the architect and context validator while the agent generates code variations and tests.
The efficiency of modern development is determined not by the number of lines generated by an agent, but by how quickly a team can validate critical assumptions with minimal rework costs.