ai · agents · explainer

AI agents, explained without the hype.

Ramswaroop
28 Sept 2026

aiagentsexplainer

Strip away the keynote slides and an "AI agent" is three things in a trench coat: a language model, some tools it may call, and a loop that keeps going until the job is done.

the entire personality of every agent framework

The loop in 12 lines

messages = [system_prompt, user_task]
for step in range(MAX_STEPS):          # always cap it
    reply = model(messages, tools=TOOLS)
    if reply.is_final_answer:
        return reply.text
    for call in reply.tool_calls:
        result = run_tool(call)        # validate + sandbox here
        messages.append(result)
raise TooManySteps()

Everything else — memory, planning, "multi-agent swarms" — is decoration on top of this loop. Useful decoration sometimes. But decoration.

Where agents actually fall over

Compounding errors. If each step is 95% reliable, ten steps is roughly 60%. Long loops need checkpoints, not optimism.
Context rot. Every tool result lands in the window. Bloated context makes the next decision worse.
Unbounded cost. A loop without a step cap and a token budget is a very efficient way to donate money.
Too much power. An agent that can do anything will, eventually, do the wrong thing.

rule of thumb

If you can draw the steps as a fixed flowchart, write a workflow. Only reach for an agent when the path genuinely depends on what the model discovers along the way.

How I'd start

Begin with one model call. Add a single tool. Add a loop with a hard cap. Log every step so you can replay it. Only then think about frameworks. Boring plumbing that you understand beats clever plumbing you inherited.

The best agent is the one you could have replaced with a cron job — and honestly checked whether you could.