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Building a medical emergency AI agent boot using A2A protocol for HNG backend stage.

Building a medical emergency AI agent boot using A2A protocol for HNG backend stage. Motivation: Building Emergent AI and AI Agent that answers medical emergency related questions and give tips on what to do based on described…

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Building a medical emergency AI agent boot using A2A protocol for HNG backend stage.



Motivation:



Building Emergent AI and AI Agent that answers medical emergency related questions and give tips on what to do based on described emergency.



It is a difficult thing to exactly know what to do in different emergency situation, the data is already out their is just to have the exact information of what to do on different situation before help arise can save lives and property.



I wanted to be able to have an AI agent that can easily guide someone through this situation.



Tech stack:



PHP/Laravel

Laragent library

Railway



In short Emergent is a chat bot that answers #medical #emergency related questions & help with tips!



System design:




User Message (Telex)
↓
JSON-RPC 2.0 Request
↓
Railway App Service (NGINX Server)
↓
Laravel Framework & API
↓
Gemini AI Agent
↓
Format & Return
↓
A2A Response
↓
Telex.im Display






💻 Building the Agent:



I set up Laravel

Install Laragent

Register my API KEY on Gemini

Add the API_KEY to my .env file

I test the agent ability

I Implement the A2A Protocol

I test it again

I push to GitHub and deployed to Railway

Result



The challenges:




  • I wasn't able to understand how a2a works.
    I go through the documentation and articles multiple times.

  • I wast not getting the right message.
    I went on to test and debug the codebase again.

  • Other AI model I used wasn't working.
    I tried multiple LLM before finally using Gemini.

  • Formatting the response and error.
    I created different methods to format the responses for error and success.

  • I have production error while deploying to Railway.
    I changed the PHP version from 8.2 to 8.3.

  • I wasn't able to integrate it with telex.
    An article and step by step guide was later sent to the slack group that is want I used to be able to setup my Agent.



Performance:



Response agent api time <500ms

Total response time ~2-3s

Uptime 99.9%

Success rate 99%



Conclusion:



Building Emergent AI Agent allow people to be able to seek emergency tips before help arrive and I was able to work on something extra-ordinary using AI and combining it with a technology new to me Telex A2A protocol.



Connect with me:



GitHub: https://github.com/abdulsalamamtech/hng-stage-3

X (Twitter): https://x.com/abdulsalamtech

Linkedin: https://linkedin.com/in/abdulsalamamtech

Facebook: https://facebook.com/abdulsalamamtech

1. Sofort-Triage & Abwehrmaßnahmen

SOC Incident Playbook: Vulnerability Remediation & Verification
Syntax validiert (0 Fehler)
title: Detect Exploitation - Building a medical emergency AI agent boot using A2A protocol for HNG backend stage.
id: 027e3c7b-f8d0-4163-801d-d873900bc187
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-25
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 = "2026-09-25"
        description = "YARA Signature for "
    strings:
        $str = "Building a medical emergency A" ascii wide
    condition:
        any of them
}
Syntax validiert (0 Fehler)
index=security sourcetype IN ("cisco:asa", "pan:traffic", "zeek_conn", "suricata", "WinEventLog:Security")
("Building a medical emergency AI agent bo")
| 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: "*Building a medical emergency AI agent bo*"
Syntax validiert (0 Fehler)
CommonSecurityLog
| where Message has "Building a medical emergency AI agent bo"
| 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

🎯
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:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich Building a medical emergency AI agent bo.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

🛡️ 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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