Most people think of LLMs as “smart chatbots.
Agents are different.
Agents combine language models + tools + control flow to create systems that can:
Reason about a task
Decide which tool to use
Act on the world
Observe results
Iterate until a goal is reached
This is what makes AI agentic, not just conversational.
🧠 Mental Model
Input → Model → (Tool?) → Model → Output
Under the hood, it’s a loop:
Reason → Act → Observe → Reason → … → Finish
An agent runs until a stop condition is met:
Final answer emitted, or
Iteration limit reached
🏗️ createAgent() in LangChain
createAgent() provides a production-ready agent runtime built on LangGraph:
Graph-based execution (not ad-hoc)
Each step is a node (model, tool, middleware)
Explicit, debuggable transitions
Safety via recursion limits and state tracking
This isn’t a demo abstraction — it’s infrastructure.
🔧 Core Building Blocks of an Agent
1️⃣ Model (Reasoning Engine)
Decides what to do next
- Static or dynamically selected at runtime
2️⃣ Tools (Actions)
Enable agents to fetch data, call APIs, execute logic, and chain actions
- Sequential, retried, or dynamic calls
3️⃣ System Prompt (Policy Layer)
Defines role, constraints, stopping behavior, and safety rules
→ Most production agent bugs are prevented here
4️⃣ State & Memory
Message history
Optional structured state
Enables multi-step workflows and long-running sessions
5️⃣ Middleware (Control Plane)
Guardrails, routing, error handling
Logging, observability, and control
This is how agents become enterprise-ready.
🔁 ReAct Isn’t a Buzzword — It’s a Pattern
Agents follow ReAct:
Reason → decide
Act → call tool
Observe → read result
Repeat until done
This enables adaptation, recovery, and non-trivial task solving.
🚨 A Hard-Earned Lesson
If an agent can call tools, it should not handle user-facing “talking”.
In production, separate:
Execution agents (reasoning + tools)
Presentation layers (formatting, tone, UX)
This avoids loops, parsing failures, and fragile behavior.
🧩 Why This Matters
Agents move us from:
“AI answers questions.”
to “AI completes wor.k”
From copilots → autonomous workflows
Frameworks like LangChain + LangGraph help build them safely.
If you’re building AI backends, developer tools, workflow automation, or autonomous systems, you’re already building agents.
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