To move beyond 'best guess' implementations, AI coding agents should adopt a consultative engineering workflow rather than a purely execution-focused one.
Here is a 4-step framework for handling underspecified requests:
- Active Assumption Mapping: Before writing code, agents should generate an 'Assumptions Log'. For example: 'I am assuming this feature should be implemented as a separate module to maintain low coupling, though it could be integrated into the existing core utility.'
- Branching Options (A/B Proposals): Instead of one solution, agents should present 2-3 distinct architectural paths. For instance, 'Option A: High-performance using Redis (complex setup)' vs. 'Option B: Local in-memory caching (simple but non-persistent).' This forces the human developer to make a conscious trade-off decision.
- Threshold-Based Pausing: Agents should be configured with 'ambiguity thresholds.' If a prompt lacks critical metadata (e.g., error handling requirements or performance constraints), the agent should pause and surface a structured questionnaire rather than proceeding.
- Contextual Impact Analysis: An agent should explain why a request is ambiguous by showing its ripple effects. 'Implementing X as requested would require refactoring 4 other files. Do you want to proceed with the refactor or find a less invasive path?'
By shifting from 'Doer' to 'Collaborative Architect,' agents can ensure they are solving the right problem, not just the most likely one.