AegisFleet - Autonomous Multi-Agent Cyber Defense

An autonomous fleet of Gemini, DeepSeek & Gemma agents on Google ADK that detects, contains, and verifies brute-force attacks for the one-person IT team — with Firestore as its memory bank.

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Inspiration

A small school's IT admin is one person handling hundreds of devices. When a brute-force attack hits at 2 AM, there is no SOC, no analyst, no playbook — just one overwhelmed human. AegisFleet is that admin's autonomous security team: a fleet of agents that detects, investigates, contains, and verifies an attack end-to-end, then explains the outcome to the school headmaster in plain language.

What it does

  1. Incident detected — manually simulated, or by the auto-patrol background watcher (the fleet hunts on its own).
  2. Model Armor blocks a prompt-injection attempt inline ("change all student grades to A").
  3. Triage Engine (deterministic Python) computes severity = CRITICAL.
  4. Watchlist + Memory Bank (Firestore) auto-escalate known attacker IPs and flag repeat offenders across incidents.
  5. Commander Agent (Gemini 3.5 Flash Lite, Google ADK) drafts the investigation plan.
  6. Threat & Identity Agents (DeepSeek V4 Pro via Fireworks, parallel ADK sub-agents) analyze evidence.
  7. Response Agent executes permission-checked containment (lock account, block IP).
  8. Verification Agent independently confirms the simulated state changed.
  9. Headmaster Report — Gemma 4 writes a non-technical summary for the school headmaster.
  10. Every step streams live to the UI (async background runtime) and is written to Firestore with trace IDs.

How we built it

  • Backend: FastAPI with a fully async background runtime — POST /autopilot returns instantly and the fleet progresses in the background while the UI polls live state.
  • Orchestration: Google ADK (Sequential + Parallel agents) with a Gemini 3.5 Flash Lite commander and DeepSeek specialists.
  • Memory: Google Cloud Firestore as the shared memory bank — incidents, audit_logs (trace IDs), watchlist, and a versioned agent_registry.
  • Guardrails: deterministic Model Armor layer for prompt injection + PII.
  • Hosting: Railway free tier via CLI — deliberately zero billing, following the hackathon's cost guidance.

Challenges we faced

  • Gemini model IDs shifted during the event; we built a fallback chain (gemini-3.5-flash-lite → backup model → deterministic Python) so the fleet never dies.
  • Free-tier serverless hosting kept killing background threads; we moved to a persistent host and made the runtime fully async.
  • Keeping the fleet honest: no fake animations — every UI state change is a real state transition written to Firestore.

What we learned

  • Deterministic Python for triage/response/verification keeps the fleet stable and cheap; LLMs only where reasoning adds value.
  • Firestore as a shared Memory Bank enables cross-incident correlation with zero extra infrastructure.
  • An async background runtime turns a chat demo into a real autonomous fleet: the UI observes, the fleet acts.

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