Mission Control

Inspiration

When managing production applications, especially those handling significant user traffic or complex state, the blast radius of a bug is massive. We realized that the most agonizing part of incident response isn't just identifying that an error occurred—it's the tedious process of tracing the stack trace back to the source code, context-switching, writing a patch, and pushing it through CI/CD. We wanted to build a self-healing system: an autonomous agentic workforce that doesn't just alert you to a fire, but actively puts it out and rebuilds the structure safely.

## What it does Our project is an autonomous triage and remediation pipeline. When a production application throws an error, our agents catch the telemetry data, trace the anomaly to the specific repository and commit, and intelligently draft a code fix. The system rigorously validates the fix against existing test suites and prompt-injection safeguards before automatically pushing the sanitized, corrected code back to production.

How we built it

We orchestrated a multi-agent system connected directly to a mock production environment and a repository. Part 1: Project Story Draft Inspiration When managing production applications, especially those handling significant user traffic or complex state, the blast radius of a bug is massive. We realized that the most agonizing part of incident response isn't just identifying that an error occurred—it's the tedious process of tracing the stack trace back to the source code, context-switching, writing a patch, and pushing it through CI/CD. We wanted to build a self-healing system: an autonomous agentic workforce that doesn't just alert you to a fire, but actively puts it out and rebuilds the structure safely. What it does Our project is an autonomous triage and remediation pipeline. When a production application throws an error, our agents catch the telemetry data, trace the anomaly to the specific repository and commit, and intelligently draft a code fix. The system rigorously validates the fix against existing test suites and prompt-injection safeguards before automatically pushing the sanitized, corrected code back to production.

## How we built it We orchestrated a multi-agent system connected directly to a mock production environment and a repository. Triage Agent: Parses incoming error logs and isolates the fault domain.

Remediation Agent: Analyzes the buggy code block and generates a patch.

Validation Agent: Acts as an adversarial check. To ensure safety, we implemented a quantitative confidence scoring model to evaluate proposed fixes before merging. The total confidence score S_{total} is calculated as:

S_{total} = w_1 \cdot P_{tests} + w_2 \cdot P_{security} + w_3 \cdot P_{lint}

where P_{tests}, P_{security}, and P_{lint} represent the boolean success outcomes (0 or 1) of unit tests, security scans (including prompt injection heuristics), and syntax linting respectively, heavily weighted (w_i) to require a perfect score before proceeding.

Challenges we faced The most critical challenge was balancing autonomy with safety. An LLM that has write-access to a production codebase is a massive security risk, particularly susceptible to prompt injection if user-generated error logs contain malicious instructions. We had to build strict LLM-as-a-judge guardrails and sandboxed execution environments to ensure the agents could not be tricked into deploying rogue code. What we learned We learned that AI agents are incredibly powerful for synthesizing context, but they require deterministic boundaries. Bridging the probabilistic nature of LLMs with the deterministic requirements of a production CI/CD pipeline taught us a lot about system architecture, adversarial testing, and the future of DevOps.