AI Agents???

From Subroutines to AI Agents: A Guide for Old-School Programmers

If you’ve been around since the COBOL, JCL, DB2, and SQL days, you likely think in terms of subroutines, JCL jobs, and database logic. Back then, we built systems with strict rules and predictable outcomes. AI Agents may seem like a big leap, but they’re not so different.

AI Agents are essentially supercharged subroutines with the flexibility to make decisions and adapt. They sense, decide, and act — with more autonomy than anything we had in the '80s.

What is an AI Agent?

An AI Agent is a goal-driven "program" that can sense its environment, make decisions, and take action. Unlike subroutines, which run fixed instructions, AI Agents react to new inputs and change their approach in real time.

Key difference:

• Subroutine: Call it, pass inputs, get a fixed output.
• AI Agent: Call it, give it a goal, and it figures out how to achieve it.

How AI Agents Relate to What We Know

AI Agent: Acts like a subroutine but adapts to input changes.

Prompt: Like passing parameters to JCL.

Environment: Inputs like system variables but smarter.

Decision-Making: Adapts logic dynamically, unlike rigid IF statements.

Memory / Context: Maintains context like global variables but updates constantly. Autonomy: Calls other tasks as needed, like JCL job dependencies.

Subroutines vs. AI Agents: Key Differences

Input/Output: Subroutine = Fixed inputs/outputs | AI Agent = Dynamic inputs/goals

Logic: Subroutine = Static, predefined | AI Agent = Adaptive, flexible

Memory: Subroutine = Stateless | AI Agent = Maintains context

Autonomy: Subroutine = No autonomy | AI Agent = Autonomous, goal-driven

Predictability: Subroutine = Consistent | AI Agent = May adapt based on learning

An AI Agent tasked with "organizing customer data" might adjust its approach if it detects errors, unlike a subroutine that runs the same process every time.

How is an AI Agent Like a JCL Job?

Job Steps: AI Agent chains multiple tasks like JCL jobs.

Input Parameters: Prompts guide the agent like JCL inputs.

Conditional Triggers: "IF RC=0 THEN" logic becomes goal-driven decisions.

Multiple Jobs: Calls other agents, like chaining JCL jobs.

Real-World Examples of AI Agents

• Chatbot: It "senses" user input, "decides" how to respond, and "acts" with a reply. It adapts its responses with context.
• Self-Driving Car: It senses the road, decides actions, and adapts as the environment changes.
• Email Autoresponder: Smart replies predict and suggest responses, learning from past interactions.

Key Takeaways

1. AI Agents = Supercharged Subroutines: They adapt, learn, and work toward goals.
2. AI Agents = Smarter JCL Jobs: They call tasks and make decisions on the fly.
3. AI Agents Learn: Unlike your old subroutines, AI Agents adjust, learn, and improve over time.

This is my understanding of AI Agents, and I can’t wait to try building some of my own.

Still learning about this - drop a comment and tell me which analogy you can relate to — subroutines, JCL jobs, or something else. I’d love to hear how you’re making sense of this modern world of AI.