Creating Agentic AI Agents

There's a significant less amount of complex Python coding and algorithim design when creating AI Agents. Pretrained LLMs do most of the heavy lifting in creating Agentic AI Agents. What matters more today is the agentic design and AI architecture.

Here’s an explanation of my POV:

✅ There's less code than expected

1. LLM handles most logic implicitlyA single well-designed prompt to GPT-4o or Claude 3 can execute what would’ve been hundreds of lines of hardcoded logic a few years ago (task planning, summarization, code generation, search relevance, etc.).

2. Python code acts as glueMost of the code is:

   • defining agents (classes/functions)

   • wiring tools (APIs, search, database calls)

   • setting up execution loops (like ReAct, AutoGPT-style steps)

   • adding memory or state tracking (like scratchpads or vector stores)But not much “complex algorithm” coding — the LLM’s reasoning replaces that.

3. Prompts are the real source codeEach agent or tool is usually defined more by its prompt template than its Python class. That's where most of the specialization lies (e.g., planner vs executor vs researcher).

4. Self-reflection or judge loopsWhen those are added, they’re often very simple:

   python

   CopyEdit

   decision = judge_agent.run([solution1, solution2])

   Again, the complexity is inside the LLM’s pretrained capability, not the code itself.

   
   

✅ Architecture still matters — but differently

While the code might be light, good agentic design still requires thought:

• Modular decomposition of tasks

• Tool choice and routing logic

• Prompt design per sub-agent

• Memory design (e.g., when to recall vs summarize vs reset)

• Performance/cost tradeoffs (e.g., calling 3 models vs 1)

  
  

⚠️ Where complexity can grow

If you go deeper, complexity arises in:

• Tool orchestration at scale (e.g., LangGraph, CrewAI, Autogen)

• Multi-agent communication protocols (e.g., hierarchical planners)

• Long-term memory and retrieval strategies

• Guardrails / safety filters / evals

• Streaming, real-time apps (e.g., voice agents or autonomous data agents)

But for many use cases, you’re right: the “magic” is in prompts and design, not volume of code.