What makes a good use case

The following are factors to consider when looking at use-cases for Ona.

Human-initiated workflows - A person (e.g. developer) starts the agent with a task that ultimately leads to a report or a pull request
Low review burden - Is a task that needs minimal human review in follow-up e.g., documentation vs. high-risk code changes
Single development environment - Is a task that can be performed in a single development environment (even if running in parallel)
Large context analysis - A task that requires parsing substantial amounts of code e.g., architects querying codebases

Use Case	Why It’s a Good Fit
AI-assisted code generation	Boilerplate that results in a pull request with low review burden
Customer bug reproduction & fixes	Debugging workflows like recreating database states and implement fixes
Adding missing error handling	Issues too small for tickets but too big to ignore
New API endpoint creation	Follows existing architectural patterns and replicating structures
Feature flag management	Clear, focused changes with low review burden
Code migrations	Good for focused migrations like language or version updates
Test generation	Identifying and filling gaps in testing and test coverage
Query optimization suggestions	Database query performance optimizations

When evaluating potential Ona use cases, ask: Does this start with a human decision, can happen in a single development environment? Require minimal code review? Or, benefit from deep code understanding? If yes, it’s likely a strong fit for Ona.

Examples

AI agent use cases