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Agentic AI Design Patterns are revolutionizing real-world AI systems, moving beyond linear “prompt → output” models to dynamic, autonomous agents capable of reflection, adaptation, planning, and collaboration. The RTPM framework (Reflection, Tool-Use, Planning, Multi-Agent Collaboration) provides a blueprint for scalable agentic systems.
🔗 Reference: Agentic AI Systems – Fireworks AI Blog
You Should Know: Practical Implementation of RTPM Framework
1. Reflection (Self-Correcting AI Agents)
Agents must evaluate their actions dynamically. Below are practical steps to implement reflection:
Linux/CLI Commands for Logging & Self-Evaluation
Monitor AI agent logs in real-time
tail -f /var/log/agent.log
Use jq to parse JSON-based reflection outputs
cat agent_output.json | jq '.reflection.score'
Automated validation script (Python snippet)
import json
with open('agent_output.json') as f:
data = json.load(f)
if data['success'] == False:
os.system('retry_agent_task.py')
Windows PowerShell for Reflection Checks
Check if an API response matches expected output
$response = Invoke-RestMethod -Uri "http://ai-agent/api/validate"
if ($response.status -ne "success") {
Start-Process "C:\scripts\retry_agent.ps1"
}
2. Tool-Use (Integrating External Capabilities)
Agents must execute code, query APIs, and interact with databases.
Linux Command-Line Tool Integration
Execute Python script from agent
python3 /opt/ai/tools/data_fetcher.py --query "SELECT FROM logs"
Call REST API using curl
curl -X POST http://api.fireworks.ai/execute --data '{"tool": "sql_query", "params": {"query": "SHOW TABLES"}}'
Schedule tool execution via cron
echo "0 /usr/bin/python3 /opt/ai/tools/daily_report.py" | crontab -
Windows Tool Automation
Run a SQL query from PowerShell $sqlQuery = "SELECT FROM Transactions" Invoke-Sqlcmd -Query $sqlQuery -ServerInstance "DBServer" Trigger an external executable Start-Process "C:\tools\data_processor.exe" -ArgumentList "--mode=analyze"
3. Planning (Task Decomposition & Execution)
Agents must break tasks into steps and adjust dynamically.
Linux-Based Workflow Automation
Use Makefile for task dependencies
make plan_agent_tasks
Parallel execution with GNU Parallel
cat task_list.txt | parallel -j 4 "./execute_task.sh {}"
Validate DAG (Directed Acyclic Graph) execution
python3 validate_dag.py --graph task_graph.json
Windows Task Scheduling
Schedule tasks dynamically Register-ScheduledTask -TaskName "AI_Planner" -Trigger (New-ScheduledTaskTrigger -AtStartup) -Action (New-ScheduledTaskAction -Execute "C:\planner\ai_planner.ps1")
4. Multi-Agent Collaboration (Distributed AI Systems)
Specialized agents (planner, retriever, executor) must communicate efficiently.
Linux IPC (Inter-Process Communication)
Use Redis for inter-agent messaging
redis-cli PUBLISH agent_channel '{"task": "process_data", "agent": "executor"}'
Shared memory via tmpfs
mount -t tmpfs -o size=1G tmpfs /dev/shm/agent_memory
Windows Multi-Agent Coordination
Named pipes for agent communication
$pipe = New-Object System.IO.Pipes.NamedPipeServerStream("ai_pipe")
$stream = New-Object System.IO.StreamWriter($pipe)
$stream.WriteLine("Task assigned: ID=45")
$stream.Close()
What Undercode Say
The RTPM framework transforms AI from static chatbots into autonomous, self-improving systems. By integrating reflection logs, tool automation, task planning, and multi-agent IPC, developers can build resilient AI architectures. Expect future advancements in self-healing AI, decentralized agent networks, and real-time adversarial adaptation.
🔗 Further Reading:
https://youtube.com/sal78ACtGTc
– Fireworks AI Blog
Prediction
By 2026, 70% of enterprise AI systems will adopt agentic patterns, reducing manual oversight by 40% and enabling fully autonomous IT operations.
Expected Output:
- AI agents self-correcting via reflection logs.
- Automated tool execution via CLI/PowerShell.
- Dynamic task planning using DAGs.
- Multi-agent systems communicating via Redis/Named Pipes.
IT/Security Reporter URL:
Reported By: Aishwarya Srinivasan – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
