We've spent months experimenting with AI agents for development tasks. Some ideas worked brilliantly. Others failed spectacularly. Here's our honest assessment of what's actually useful and what's still hype.
The High-Value Wins
These are the use cases where agents genuinely save time and reduce cognitive load:
1. Automated Testing
Running test suites automatically and notifying you of failures is straightforward, predictable, and genuinely useful. No complex decision-making required—just execution and reporting.
We've set up agents that run tests on every commit, on a schedule, or when dependencies change. They catch regressions early and save us hours of manual testing.
Verdict: Highly recommended. This is low-hanging fruit.
2. Dependency Management
Monitoring and updating dependencies automatically works great for security patches and minor updates. The agent can check for updates, test them, and create pull requests.
For major version updates, we still require human review—the breaking changes are too risky to automate completely.
Verdict: Very useful, but set clear boundaries for what can be auto-merged.
3. Code Formatting & Linting
Automatically formatting code and running linters is simple, reliable, and actually useful. It's not really an "agent" use case—it's more of a workflow—but it's worth mentioning.
No decision-making required, just consistent execution. Set it up once, forget about it.
Verdict: Essential. Do this.
4. Documentation Generation
Generating basic documentation from code comments works okay for simple cases. The agent can extract function signatures, parameters, and basic descriptions.
For complex APIs or nuanced explanations, it still needs human touch. But for keeping basic docs up to date, it's helpful.
Verdict: Useful for maintenance, not for initial creation.
5. Deployment Automation
Automating deployment pipelines is more workflow than agent, but it works well. The agent can handle the orchestration, run checks, and deploy when conditions are met.
Just make sure you have good rollback procedures. Automated deployments are great until they're not.
Verdict: Very useful, but requires careful setup and testing.
The Tricky Middle Ground
These use cases show promise but require careful implementation and human oversight:
6. Automated Code Review
Agents can catch obvious bugs, style issues, and common mistakes. They're good at finding things like unused variables, potential null pointer exceptions, and basic security issues.
But they miss a lot. They're not good at architecture decisions, design patterns, or understanding the broader context of why code is written a certain way.
Verdict: Useful as a first pass, but always requires human review. Don't rely on it exclusively.
7. Bug Fixing
We've tried having agents fix bugs automatically. Sometimes it works beautifully. Sometimes it makes things worse by introducing new bugs or "fixing" things that weren't actually broken.
The challenge is that fixing bugs often requires understanding the broader system context, which agents struggle with.
Verdict: Promising but not reliable yet. Use with extreme caution and always review changes.
8. Feature Implementation
Having agents implement features from descriptions is hit or miss. Simple, well-defined features work okay. Complex features that require understanding existing code patterns need too much hand-holding to be useful.
The agent can generate code, but you'll spend more time fixing and refining it than you would have spent writing it yourself.
Verdict: Useful for boilerplate and simple features, not for complex implementations.
What Doesn't Work (Yet)
These are areas where agents consistently struggle:
9. Architecture Decisions
Agents aren't good at making high-level architecture decisions. They can suggest things based on patterns they've seen, but they lack the judgment needed for decisions that will affect the entire system.
Architecture requires understanding trade-offs, long-term implications, and team context—things agents can't really grasp.
Verdict: Not ready. Keep humans in the loop for architecture.
10. Complex Problem Solving
For complex, novel problems that don't follow established patterns, agents struggle. They're good at following patterns and applying known solutions, but they're not good at creating new solutions.
If you're solving a problem that hasn't been solved before, or that requires creative thinking, agents won't help much.
Verdict: Not the right tool for this job. Use agents for repetitive tasks, not creative problem-solving.
The Pattern
Looking at what works and what doesn't, a clear pattern emerges:
- Agents excel at: Repetitive, well-defined tasks with clear success criteria
- Agents are okay at: Semi-structured tasks with clear patterns, but require human oversight
- Agents struggle with: Creative tasks, novel problems, and high-level decision-making
Our Recommendation
Start with the simple stuff. Get automated testing, formatting, and basic deployment working well. These are low-risk, high-value wins.
Then gradually add complexity. Try code review assistance. Experiment with bug fixing on non-critical issues. See what works for your specific context.
Don't try to automate everything at once. You'll just end up frustrated and with a system that's harder to maintain than doing things manually.
And always remember: agents are tools, not replacements. Use them to augment your capabilities, not to replace your judgment.