Building Effective "Agents"

Workflows vs “Agents” in Practice

• Many commenters agree the real current value is in workflow automation rather than fully autonomous agents.
• “Agentic” planning steps are often removed in production because companies want predictable, repeatable behavior; what’s left is a deterministic workflow with LLM calls.
• DAG-like or state-machine workflows with clear decision trees (e.g., email triage, customer support, marketing flows) are seen as much more deployable than open-ended agents.
• Narrow, well-defined tasks with constrained choice spaces work far better than “boil the ocean” general-purpose agents.

Frameworks vs Simple Patterns

• Strong sentiment against heavyweight frameworks like LangChain/LangGraph for many real-world cases: seen as adding verbosity, indirection, and complexity without proportional benefit.
• Several practitioners report ripping out such frameworks and replacing them with plain code and simple async patterns, often with large LOC reductions.
• Lightweight abstractions or “transitional software design” is favored: mostly traditional code, with LLMs used only for “LLM-hard” subproblems (creative writing, fuzzy classification, nuanced decisions).

Reliability, Testing, and Limits of LLMs

• Recurrent theme: LLMs are inherently probabilistic and unreliable; you must test hard, with archives, edge cases, and regression suites.
• Some argue occasional failures are acceptable in domains already dominated by probabilistic methods (e.g., spam-like tasks); others warn that “magic thinking” about reliability is widespread and dangerous.
• Concerns about compounding errors in multi-step workflows; mitigation via verifiable checks, tests, and possibly LLM-as-judge.
• Complaints about lack of robust structured output and degradation of JSON-like patterns, making agents brittle.

Definitions and Ethics of “Agents”

• Long debate over the term “agent”:
  • One camp emphasizes the legal/ordinary sense (acting on behalf of someone, with responsibility).
  • Another cites the AI-textbook sense (anything that perceives and acts in an environment).
• Some see current marketing use of “agent” as confusing or disingenuous for non-technical audiences, especially when responsibility for errors is at stake.

Architecture, Orchestration, and Tooling

• Several view LLMs as components in larger orchestrated systems: prefrontal-cortex analogies, system‑1 vs system‑2 splits, and complex multi-model architectures are discussed.
• Durable workflow engines (Temporal, Inngest, Hatchet, Windmill) are highlighted for long-running, fault-tolerant, human-in-the-loop processes, though they introduce their own complexity and learning costs.
• Tool use is seen as central to powerful agentic workflows, but defining tools and handling stateful tools correctly is non-trivial.