When NOT to build an agent

# --- Pick your provider (set the matching API key env var) ---
# Anthropic:  from anthropic import Anthropic; client = Anthropic()        # ANTHROPIC_API_KEY
# OpenAI:     from openai import OpenAI;       client = OpenAI()           # OPENAI_API_KEY
# Gemini:     from google import genai;        client = genai.Client()     # GEMINI_API_KEY

# When workflow is right
def get_user_summary(user_id):
    user = db.get(user_id)                          # not LLM
    recent = db.recent_orders(user_id, limit=10)    # not LLM
    summary = generate_summary(user, recent)        # 1 LLM call
    return summary
# Deterministic, fast (~500ms), cheap (~$0.005).

# When agent is right
def help_me_debug_this_error(error_msg, codebase):
    # Steps depend on the error — might need to grep code,
    # read docs, search SO, look at git blame, run tests.
    # No fixed path.
    return run_debugging_agent(error_msg, codebase, max_steps=10)
# Variable cost ($0.05-$0.50), variable latency (10-60s), high value if it works.

# Anti-pattern: an agent for a workflow
def classify_ticket_with_agent(ticket):
    # gives the model "search_kb" and "ask_human" tools
    # but really we just want CLASSIFY -> RETURN
    # so we end up paying agent overhead for a 1-call task.
    ...

# This is a workflow:
def classify_ticket(ticket):
    return client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=20,
        system="Classify into: billing, technical, sales, other. Reply one word.",
        messages=[{"role": "user", "content": ticket}],
    ).content[0].text.strip()