Ask HN: Is anybody getting value from AI Agents? How so?

Hacker News Discussion ↗

Overall sentiment on AI agents

  • Many commenters report disappointment with current “agent” systems (AutoGPT-like, LangChain agents, OpenAI Assistants, etc.): too slow, expensive, fragile, and unpredictable.
  • Strong sense that agents are “too early”: models still make frequent errors, diverge in loops, and need heavy supervision.
  • Some think we’re entering a post‑hype correction phase; others are bullish long‑term but bearish on near‑term deployment at scale.

Where agents / LLMs are delivering value

  • Coding assistance (not full agents): Copilot, GPT‑4, Cursor, aider, plandex, etc. help with boilerplate, refactoring, tests, parsing compiler errors, and multi‑file edits—always with a human in the loop.
  • Customer support / ops: AI assistants handling a large share of chats, email classification, drafting replies, order cancellations, FAQ bots, Discord helpers. Often paired with fallback to humans.
  • Internal workflows: summarizing docs/books, RAG chat over papers, security‑audit diffs, sales research on websites, social‑media signal scanning, form filling, intent classification for chatbots.
  • Personal tools: custom Telegram or Shortcuts-based assistants tied into calendars, reminders, RSS, 3D printers, news “radio shows,” etc.
  • E‑commerce and marketing: personalized cold email generation, site conversion optimization agents reportedly lifting click‑through.

Technical and product challenges

  • Reliability: agents choose wrong tools, ignore tools, hallucinate tasks, get stuck in loops, or disbelieve correct tool outputs.
  • Latency and cost: chain‑of‑thought and multi‑step plans make systems slow and expensive; p99 latencies of several seconds to tens of seconds.
  • Error compounding: multi‑step workflows magnify per‑step error; attempts at error‑checking with more LLM layers have limited success.
  • Architecture: complex agent graphs and rule‑based orchestration feel like a regression to brittle, edge‑case‑ridden systems; some advocate small, tightly constrained agents plus deterministic state machines or workflows.
  • Models: current LLMs seen as bottleneck; many think better base models will suddenly make existing agent code “just work.”

Trust, legal, and social issues

  • Legal risk: liability for AI mistakes (e.g., hallucinated promises), copyright status of AI outputs.
  • Low trust: advice to treat AI like a high‑schooler; only use for low‑stakes or easily reviewed tasks.
  • Broader concerns: demand may be weaker than investors assume; environmental costs and creative‑industry impacts raised.