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Email Drafter — Multi-Agent Email Writing with Google ADK and Cloud Run

What I Built I built Email Drafter, a small web app that turns structured user input into a polished email draft using a multi-agent workflow. The app takes: recipient type purpose tone language key points Instead of using one giant…

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What I Built



I built Email Drafter, a small web app that turns structured user input into a polished email draft using a multi-agent workflow.



The app takes:




  • recipient type

  • purpose

  • tone

  • language

  • key points



Instead of using one giant prompt, I split the workflow into multiple specialized roles.



The system works like this:




  • one agent plans the email

  • one agent reviews the plan

  • one agent writes the final draft

  • one orchestrator coordinates the workflow



I also deployed the system as separate services on Cloud Run, so the app feels much closer to a real distributed AI workflow instead of just a local prototype.



I picked email drafting because it is a practical use case where planning, reviewing, and writing feel like naturally separate tasks. That made it a good fit for experimenting with multi-agent orchestration.






Cloud Run Embed













Your Agents



This project uses three specialized agents plus one orchestrator.




  • Researcher Agent


    Creates a structured email plan from the user input.


  • Judge Agent


    Reviews the plan and checks whether it is complete enough before moving on.


  • Content Builder Agent


    Writes the final email draft from the approved plan.


  • Orchestrator Agent


    Coordinates the workflow between the other agents and returns the final result to the frontend.




The overall flow is:



Frontend -> Orchestrator -> Researcher -> Judge -> Content Builder -> Final Email Draft






Key Learnings



This project helped me understand why multi-agent systems can be useful even for a relatively small app.



A few things stood out while building it:




  • Splitting the workflow made debugging easier. It was much easier to tell whether the issue came from planning, review, or writing than trying to fix one large prompt.

  • Deployment was harder than the basic prompt logic. Getting multiple services running locally, wiring them together, and then deploying them to Cloud Run took more effort than writing the first version of the agents.

  • Prompt wording had a big impact on output quality. Small instruction changes made a noticeable difference between getting a planning-style response and getting something that looked like a real email.

  • Multi-agent design felt more modular and production-oriented. Even for a simple email drafting tool, separating responsibilities made the system easier to reason about and improve.






Demo



1. Sofort-Triage & Abwehrmaßnahmen

SOC Incident Playbook: Vulnerability Remediation & Verification
Syntax validiert (0 Fehler)
title: Detect Exploitation - Email Drafter — Multi-Agent Email Writing with Google ADK and Cloud Run
id: aae15359-1f05-4369-a6be-06b177b1347b
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-27
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
Syntax validiert (0 Fehler)
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-27"
        description = "YARA Signature for "
    strings:
        $str = "Email Drafter — Multi-Agent Em" ascii wide
    condition:
        any of them
}
Syntax validiert (0 Fehler)
index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("Email Drafter  Multi-Agent Email Writing")
| stats count earliest(_time) as first_seen latest(_time) as last_seen by src_ip, dest_ip, dest_host, signature
| eval first_seen=strftime(first_seen, "%Y-%m-%d %H:%M:%S"), last_seen=strftime(last_seen, "%Y-%m-%d %H:%M:%S")
| sort - count
Syntax validiert (0 Fehler)
message: "*Email Drafter  Multi-Agent Email Writing*"
Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "Email Drafter  Multi-Agent Email Writing"
| summarize EventCount = count(), FirstSeen = min(TimeGenerated), LastSeen = max(TimeGenerated) by SourceIP, DestinationIP, DestinationPort, Activity
| extend DetectionRule = "iShareStuff-CTI-Compiled"
| sort by EventCount desc

2. Cyber Threat Intelligence & Forensik

CTI Threat Relationship Graph2 Knoten / 1 Relationen
CVE / Incident Software MITRE ATT&CK CWE Weakness IoC
🎯
MITRE ATT&CK Matrix Navigator 14 Taktiken
Reconnaissance
-
Resource Development
-
Initial Access
Execution
Persistence
-
Privilege Escalation
Defense Evasion
Credential Access
-
Discovery
-
Lateral Movement
-
Collection
-
Command and Control
Exfiltration
-
Impact
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Analyse für identifizierte Bedrohung auf Basis von Live-CTI (ENISA EUVD): CVSS 0.0 · EPSS 0.0% · CISA KEV: nein. Handlungsableitung aus den verlinkten Hersteller-Quellen.

🛡️ Angriffsfläche & Exposure

Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.

⚡ Empfohlene Sofortmaßnahmen
  • 1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
  • 2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
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