Practical Agentic AI Patterns I've Used in Production

What Makes an Agent "Agentic"?

The key distinction between a simple LLM call and an agentic system is the loop — an agent observes, reasons, acts, and repeats until a goal is achieved.

Pattern 1: ReAct Loop

The most fundamental pattern. Reason → Act → Observe → Repeat.

while not task.complete:
    thought = llm.reason(task, observations)
    action = llm.select_action(thought, available_tools)
    result = execute(action)
    observations.append(result)

Pattern 2: Tool-Augmented Agents

Give the LLM access to real-world tools — APIs, databases, browsers, file systems.

The key insight: tool descriptions matter as much as the tools themselves. Well-documented tool schemas dramatically improve agent performance.

Pattern 3: Multi-Agent Orchestration

For complex workflows, decompose into specialized agents:

• Planner Agent — breaks down tasks
• Executor Agent — carries out individual steps
• Reviewer Agent — validates outputs
• Router Agent — directs traffic between specialists

Key Takeaway

Start simple. A single ReAct loop with well-chosen tools solves 80% of real-world agentic problems. Multi-agent systems add complexity — use them only when the problem genuinely requires decomposition.