Ask HN: What are some actual use cases of AI Agents right now?
Claims that AI “agents” can autonomously handle complex tasks are meeting skepticism from developers, who report that current models still need tight scoping, human oversight, and careful engineering to be reliably useful. Commenters highlight narrow but effective uses—code copilots, RAG-backed internal knowledge bots, specialized scraping/orchestration tools, document drafting and summarization, customer-support triage, and research assistants—while noting that end-to-end “fire and forget” workflows tend to break down. A recurring theme is that error rates compound across multi-step plans, making accuracy, reliability, and retrieval the main bottlenecks to turning agent hype into robust products.
Definition and Scope of “Agents”
- Term is seen as overloaded and often marketing-driven.
- Rough split between:
- Retrieval/assistant tools (“chat with your data”).
- Action-taking systems that operate software or APIs on a user’s behalf.
Current Practical Use Cases
- Coding assistance: boilerplate, unfamiliar languages, test generation, refactoring, code review-style linting.
- Research and summarization: economic/political research writeups with citations; RAG chatbots over large corpora (docs, regulations, internal knowledge).
- Web scraping/orchestration: small, specialized agents for proxy selection, navigation, schema detection, and data transformation rather than a single general agent.
- Customer support and triage: routing tickets, first-line chatbots, content moderation, FAQ answering with human fallback.
- Personal productivity: note aggregation, RSS-style filtering, search replacement for direct answers, tutoring, documentation Q&A.
- Domain tools: medical visit summarization, security workload prioritization, knowledge-based article writing and comparison.
Where Agents/LLMs Fall Short
- Autonomous completion of complex tasks (e.g., multi-step web flows, “order me X”, travel booking) is unreliable.
- Story and image generation struggle with long-term coherence, plot, and persistent characters.
- Converting design images to HTML/CSS remains poor without task-specific training.
- Multi-agent systems often loop, fail to converge, or are too unpredictable.
Technical and Productization Challenges
- Error compounding: chaining even 90%‑accurate steps quickly degrades reliability.
- Planning across many steps, handling vision (screenshots, layouts), and rigorous tool use are persistent weak spots.
- RAG quality hinges on retrieval engineering; naive setups underperform.
- Trust and hallucinations limit fully automated, high-stakes actions (payments, legal/medical decisions).
Design Patterns and Mitigations
- Use many constrained agents instead of one general one; treat LLMs as components in traditional workflows.
- Keep humans in the loop for review, especially where accuracy is costly.
- Break tasks into small, tightly specified steps; use meta-prompts and editor passes.
- Favor low-risk domains (summaries, drafts, triage) where “80% good but cheap” already has value.