Let’s face it - many aspects of software development are undeniably tedious. Chief among those is debugging issues in production .

Diagnosing production errors conjures the same level of enthusiasm as attending your fourth status update meeting of the week. Developing a new feature that delights users? That’s rewarding. But tracking down the root cause of a sporadic 504 Gateway Timeout error? Not so much.

We believe there's a better way.

Generative AI is transforming what's possible in software development. With its ability to mimic human decisions, explain its reasoning, and even generate new code, this technology opens doors for engineering teams to monitor and diagnose production systems in ways we’ve only dreamed of. Imagine a future where systems don’t just alert you to errors but proactively analyze, diagnose, and even suggest solutions. Need to identify the root cause of a pattern of anomalies? Done. Need to trace an error back through a series of API calls with context from related services? Simple. Need an intelligent agent to suggest a code fix for a previously unseen edge case? Consider it handled. This is the future of AI-powered error resolution.

With the power of new large language models and the commodification of distributed tracing and machine learning, we see a clear path to realizing our vision—an intelligent agent that proactively detects issues, analyzes context, and offers informed remediation. CodeComet's App Copilot is our first step towards this vision. Our goal is to relieve engineering teams of the tedious troubleshooting tasks in production software, freeing them to focus on higher-impact work. If you catch yourself raising your eyebrows the first time you see our system in action, we’ll do our best to feign surprise. 🙂

To start, we’re focusing specifically on production systems for APIs and backends. We envision foundational capabilities that will drive the next generation of software management:

• Error Resolution: Detect production errors, like 5xx or 4xx responses, and suggest code-level fixes.

  
  

• Performance Analysis: Identify performance issues in real time and recommend improvements.

  
  

• Oracle: Ask natural language questions about backend behavior or performance and receive detailed responses.

  
  

• Security and Vulnerabilities: Identify security risks and vulnerabilities, and provide actionable remediation steps.

  
  

• Documentation and SDKs: Automatically generate and update documentation and SDKs.

  
  

• Infrastructure and Resources: Enhance resource allocation and usage for better efficiency.

  
  

• Mocking: Easily create mock APIs for testing and development.

  
  

• Testing: Create comprehensive test suites to ensure app behavior and reliability.

  
  

• Architecture: Analyze and provide insights into your app architecture for better design and scalability.

  
  

• CI/CD Management: Optimize your CI/CD workflows for faster builds and more reliable deployments.

  
  

• Logging and Monitoring: Enhance logging and monitoring to gain deeper insights into API and backend operations.

  
  

There’s even more just around the corner. While we have big ambitions, we’ve reined them in for our first pass. We won’t launch with everything listed above, instead initially focusing on Python systems. We want to make sure we can walk before we run. So we are building a compact, effective tool, one that doesn’t skip those easy-to-neglect edge cases.

Though we are early in this journey, we are committed to creating developer—focused tools that bring us closer to the future we envision a future where all software systems are performant, reliable, secure, observable…and diagnosable.

If our vision of CodeComet resonates with you, we'd love to hear from you.