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A layered Agentic AI architecture is built on five key pillars, enabling autonomous, scalable, and secure AI operations. This framework enhances AI capabilities by integrating knowledge access, execution coordination, decision-making, continuous learning, and governance.
🔹 Knowledge & Access Layer
Connects AI agents to APIs, databases, and external tools for real-time data retrieval.
Commands & Tools:
- Use `curl` to fetch API data:
curl -X GET https://api.example.com/data -H "Authorization: Bearer TOKEN"
- Query databases via `sqlcmd` (SQL Server) or `psql` (PostgreSQL):
SELECT FROM datasets WHERE category='AI';
- Scrape web data with
wget:wget --mirror --convert-links https://ai-resources.org
🔹 Execution & Coordination Layer
Breaks complex tasks into sub-tasks, manages workflows, and tracks progress.
Automation with Cron & Bash:
Schedule a Python AI task 0 /usr/bin/python3 /scripts/ai_agent_task.py >> /var/log/ai_tasks.log
Multi-Agent Coordination with Docker:
docker-compose up -d ai_agent1 ai_agent2
🔹 Decision & Cognition Layer
Hosts LLMs (e.g., GPT-4, Llama 3) and reasoning engines.
Run an LLM Locally:
ollama pull llama3 ollama run llama3 "Explain Agentic AI architecture."
Python Decision-Making Script:
from transformers import pipeline
classifier = pipeline("text-classification")
result = classifier("Should the AI agent fetch new data?")
print(result)
🔹 Learning & Feedback Layer
Uses reinforcement learning (RL) and A/B testing for self-improvement.
TensorFlow RL Training:
import tensorflow as tf agent = tf.agents.DQNAgent(...) agent.train(experience=dataset)
Log Feedback with ELK Stack:
curl -X POST http://localhost:9200/ai_feedback/_doc -d '{"query":"error_rate", "response":"improved"}'
🔹 Trust & Governance Layer
Ensures security, compliance, and auditability.
Linux Security Hardening:
sudo apt install auditd sudo auditctl -w /etc/passwd -p wa -k ai_access
Windows Audit Logs:
Get-EventLog -LogName Security -Newest 50 | Where-Object {$_.EventID -eq 4624}
You Should Know:
- Multi-Agent Systems use Docker/Kubernetes for orchestration:
kubectl create deployment ai-agent --image=ai/agent:v1
- API Security is critical—use JWT tokens:
openssl genrsa -out ai_key.pem 2048
- Continuous Monitoring with Prometheus:
scrape_configs: </li> <li>job_name: 'ai_agents' static_configs: </li> <li>targets: ['ai-agent1:9090']
What Undercode Say:
Agentic AI transforms automation by enabling autonomous problem-solving. However, security risks (e.g., API breaches, model poisoning) require robust governance. Future systems will integrate quantum-resistant encryption (openssl genpkey -algorithm x25519) and federated learning (tensorflow_federated).
Prediction:
By 2026, 60% of enterprises will deploy multi-agent AI, leveraging architectures like Habib Shaikh’s framework.
Expected Output:
AI Agent Task Log: - [bash] Data fetched from API at 2025-06-07T14:30Z - [bash] Model retraining triggered via RL feedback - [bash] Security scan passed (CVE-2025-0000 mitigated)
Relevant URL:
IT/Security Reporter URL:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
