Building My First AI Agent: Lessons Learned

#agents #llm

I finally built my first fully autonomous AI agent, and it was equal parts magical and humbling. Here’s what I learned.

The planning loop matters more than the model

I started with a naive “LLM-in-a-loop” approach — send a prompt, get an action, execute it, repeat. It worked… sometimes. The breakthrough came when I added explicit planning: forcing the agent to write out a step-by-step plan before acting, and to revise that plan after every tool call.

while not task_complete:
    plan = llm("Given the goal and current state, what's the plan?")
    action = llm("Execute the next step of the plan.")
    result = tools.execute(action)
    memory.append(action, result)

Memory is harder than it looks

Short-term memory (the current conversation) is easy. The hard part is long-term memory — deciding what’s worth remembering across sessions. I ended up with a simple three-tier approach:

  1. Scratchpad — everything from the current task
  2. Episodic — summaries of past tool executions
  3. Semantic — a vector store of distilled facts

Failure modes to watch for

  • Loop addiction — the agent repeats the same failing action forever
  • Hallucinated tool outputs — it “remembers” results that never happened
  • Scope creep — a simple task balloons into a 40-step saga

Overall: agents are 20% model, 80% scaffolding. The intelligence is real, but the engineering around it is what makes it useful.

More experiments coming soon — subscribe to follow along.