VIGIL AgentOps

AI agents need security too. VIGIL AgentOps uses Gemini and MongoDB MCP to autonomously investigate, explain, and contain AI threats while keeping humans in the decision loop.

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Inspiration

AI agents are rapidly evolving from simple chatbots into autonomous systems capable of accessing tools, querying databases, delegating tasks, transferring data, and making decisions. While these capabilities unlock tremendous productivity, they also introduce an entirely new attack surface. Traditional cybersecurity solutions were never designed to understand AI intent, agent-to-agent interactions, or autonomous decision-making.

We asked a simple question:

"Who is protecting the AI agents?"

That question inspired VIGIL AgentOps — an autonomous AI Security Operations Agent that continuously monitors, investigates, explains, and responds to threats targeting AI systems while keeping humans in control.

What it does

VIGIL AgentOps is a Gemini-powered AI Security Operations Agent that acts as a behavioral immune system for autonomous AI ecosystems.

Instead of simply answering questions, VIGIL performs multi-step security operations:

  1. Monitors AI agent activity and telemetry in real time.
  2. Detects suspicious behavior such as prompt injection, privilege escalation, malicious delegation, and data exfiltration.
  3. Investigates incidents using historical evidence stored through MongoDB MCP integration.
  4. Explains threats in plain English for operators and decision-makers.
  5. Recommends remediation actions based on risk and context.
  6. Requests human approval for critical actions.
  7. Executes governance-approved containment workflows.

The result is an agent capable of moving beyond detection into investigation, reasoning, and action.

How we built it

VIGIL AgentOps combines multiple layers of intelligence and governance.

Gemini

Gemini serves as the reasoning engine that investigates incidents, analyzes evidence, generates explanations, plans response actions, and orchestrates multi-step workflows.

MongoDB MCP

MongoDB acts as the memory and investigation layer for the system. Through MCP integration, the agent can retrieve historical telemetry, incident records, governance decisions, replay timelines, and behavioral evidence to support investigations.

Hybrid Threat Detection Engine

We developed a multi-layer detection architecture consisting of:

  • Rule-based threat detection
  • Behavioral anomaly detection using EWMA baselines
  • Isolation Forest machine learning models
  • Semantic threat analysis for malicious intent

Governance Layer

Critical actions pass through a governance workflow that ensures human oversight before high-impact remediation is executed.

Real-Time Operations

The platform includes:

  • FastAPI backend
  • React dashboard
  • WebSocket event streaming
  • Telegram governance workflows
  • Incident replay and audit trails

Challenges we ran into

One of the biggest challenges was designing a system that balances autonomy with human control.

Fully autonomous remediation can be dangerous, while excessive human involvement slows response times. Finding the right balance required designing governance workflows that allow the system to reason and recommend actions while keeping humans in the approval loop.

Another challenge was correlating multiple sources of evidence into a single threat assessment. We combined statistical analysis, machine learning, behavioral monitoring, and semantic understanding to improve confidence and reduce false positives.

Building real-time investigation and response pipelines while maintaining transparency and explainability also required significant architectural iteration.

Accomplishments that we're proud of

  • Built a complete AI Security Operations Agent rather than a traditional chatbot.
  • Successfully integrated Gemini reasoning with MongoDB MCP-powered memory and investigation.
  • Designed a hybrid behavioral threat detection engine.
  • Implemented governance-based security workflows with human oversight.
  • Developed real-time monitoring, incident investigation, and containment capabilities.
  • Created a platform that demonstrates how autonomous AI systems can be governed safely and responsibly.

Most importantly, we transformed AI security from reactive monitoring into proactive autonomous operations.

What we learned

This project taught us that securing AI systems requires far more than traditional cybersecurity techniques.

Behavioral context, intent understanding, historical memory, governance, and explainability are all critical pieces of the puzzle.

We also learned how powerful MCP-based architectures can be when paired with reasoning models. Giving an AI agent structured access to evidence dramatically improves its ability to investigate, explain, and act on complex situations.

Finally, we learned that the future of security is not just detection—it is intelligent, explainable, and governed action.

What's next for VIGIL AgentOps

Our vision is to evolve VIGIL AgentOps into a full AI Runtime Governance Platform for autonomous AI workforces.

Future plans include:

  • Advanced threat hunting agents
  • Multi-agent security coordination
  • Enterprise policy engines
  • Behavioral graph intelligence
  • Automated forensic report generation
  • Expanded MCP integrations
  • Cloud-native deployment at scale
  • Security operations copilots for AI infrastructure teams

As AI agents become more capable, VIGIL AgentOps aims to ensure they remain observable, governable, and secure.

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