AI Agents Are Becoming Software Systems

Sample article — realistic starter content for NeuralSys. Replace with your own writing anytime; the pipeline (MDX → Git → Vercel) stays the same.

Introduction

A year ago, an "AI agent" meant a clever prompt and a demo. Today it means a long-running software system: tools, memory, retries, budgets, evaluation, and observability.

The prompt is maybe 5% of the work. The other 95% is systems engineering.

From LLM Calls to Systems

A single LLM call is stateless and forgetful. An agent wraps that call in a control loop:

User
 |
 v
AI Agent
 |
 +------> LLM (reasoning)
 |
 +------> Context Engine (memory + retrieval)
 |
 +------> Tools (code, APIs, browsers)
 |
 +------> Evaluator (did it work?)

Each arrow is an engineering decision: timeouts, retries, schemas, budgets, fallbacks.

The Anatomy of a Reliable Agent

1. A typed tool interface

Tools should look like function signatures, not prose. Validate inputs with schemas, return structured results, and version them.

from pydantic import BaseModel
 
class SearchArgs(BaseModel):
    query: str
    top_k: int = 5
 
def search_tools(args: SearchArgs) -> list[dict]:
    """Deterministic wrapper around retrieval."""
    results = retriever.search(args.query, k=args.top_k)
    return [{"title": r.title, "score": r.score} for r in results]

2. Bounded execution

Every loop needs a budget: max steps, max tokens, max wall-clock time, max spend. Unbounded agents are incidents waiting to happen.

const policy = {
  maxSteps: 12,
  maxTokens: 24_000,
  timeoutMs: 120_000,
  onBudgetExceeded: "summarize-and-ask",
};

3. Memory that is actually retrieval

"Memory" in agents is almost always context assembly: recent turns + retrieved documents + structured state, ranked and truncated to fit the window.

Context Engineering

The highest-leverage skill in agent building is deciding what goes into the prompt on each step:

Evaluation Before Scale

If you cannot score an agent run, you cannot improve it. Start with three things:

1. A golden task set — 20–50 realistic tasks with checkable outcomes.
2. Trajectory logging — every step, tool call, and token count.
3. A failure taxonomy — wrong tool, bad retrieval, premature stop, format drift.

Implementation Sketch

A minimal agent loop fits in a page of code. Everything else is hardening:

def run_agent(task, tools, llm, budget):
    state = {"steps": 0, "history": []}
    while state["steps"] < budget.max_steps:
        context = assemble_context(task, state)  # the real work
        action = llm.decide(context)
        if action.type == "finish":
            return action.answer
        result = tools.execute(action, timeout=budget.tool_timeout)
        state["history"].append((action, result))
        state["steps"] += 1
    return summarize_and_ask(task, state)

Key Takeaways

• Agents are distributed systems with an LLM inside, not prompts with tools bolted on.
• Budgets, schemas, evaluation, and observability are the product.
• Context assembly is the highest-leverage layer to invest in.
• Start narrow, measure everything, then widen autonomy.

Next in this series: context engineering as the missing layer, and why RAG is a retrieval problem first.