I've been experimenting with AI agents as development partners, not replacements, but force multipliers.
What I've tried:
- Code generation: Asking the agent to write boilerplate, tests, and documentation
- Planning: Using structured prompts to break down features into tasks
- Review: Having the agent scan code for issues before I review it myself
- Multi-agent workflows: Orchestrating specialized agents (writer, coder, reviewer) working together
What works:
- Documentation generation is the highest ROI. The agent produces clean READMEs, API docs, and changelogs that I'd rather not write by hand.
- Task decomposition: Give it a feature spec, get back a structured plan with acceptance criteria.
- Code review catches: Surprisingly good at finding edge cases I'd miss.
What doesn't:
- Blind trust. The agent hallucinates APIs, invents libraries, and occasionally writes code that looks right but breaks at runtime. Every output needs human review.
- Context window limits. Complex codebases require careful prompting to keep the agent oriented.
The workflow that clicked: Kanban-style task boards where the agent picks up structured work items, does the implementation, and reports back with changed files and test results. It turns the agent from a chatbot into a pipeline worker.