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Sentinel AI — Autonomous Security Audit Agent

Cybersecurity

The Problem

Traditional static analysis tools bury engineering teams in "maybe" bugs. They flag patterns without understanding context — whether a finding is actually exploitable in this codebase.

Confirming exploitability and shipping fixes normally requires scarce, expensive human security expertise, so real vulnerabilities sit in a backlog while teams triage false positives.

Our Solution

We built an autonomous, multi-agent security pipeline that closes the loop from discovery to fix.

Five specialized agents, one orchestrator

  • Ingestion walks the repository, indexes source files, and detects languages.
  • Analysis opens an agentic loop where the model chooses which files to read, then returns findings as validated structured data — no hallucinated output.
  • Exploit generates a proof-of-concept for each finding and runs it in an isolated sandbox to confirm which vulnerabilities are actually exploitable. These run in parallel.
  • Remediation reads each affected file and writes a targeted patch with an explanation.
  • Pull Request applies the patches and opens a remediation PR with a severity table and risk score.

Built on modern agent patterns

The system demonstrates agentic reason-act-observe loops, forced-tool-call structured outputs, parallel sub-agents, subprocess sandboxing, and a custom Model Context Protocol (MCP) server that exposes the sandbox to any MCP-compatible client.

Outcome

Instead of a backlog of unverified alerts, teams get a pull request: every reported vulnerability is confirmed by a real sandboxed exploit, patched, and explained — turning a slow, expert-dependent review into an automated, end-to-end workflow.

Tools & Technologies

PythonAnthropic SDKClaudeMCPPydanticasyncioSandboxingGitHub API

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