AI代理與工作流程之間的譜系：為何LLM代理大多是圖形結構 (2025-2026觀點)
本文分析了當前AI代理的現況，指出大多數現代「AI代理」本質上是包含動作、LLM呼叫和工具執行的定向圖，並具備路由邏輯和共享狀態。文章提出了2025-2026年的實用共識，將AI代理視為一個譜系，從預定義的DAG到由LLM控制的開放式規劃。
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Jan 16, 2026
AI Agents vs Workflows: Why LLM Agents Are Mostly Graphs (2025–2026 Perspective)
AI agents: less magic, more graphs
You’re spot on: most modern “AI agents” boil down to directed graphs, often with cycles.
Common structure:
Nodes: actions, LLM calls, tool executions
Edges: routing logic (conditional or LLM-decided)
Shared state: memory/context
Loops/feedback: what makes it feel non-linear
Add:
LLM-based routing
cycles (reflection, retries)
➡️ and a boring DAG suddenly feels agentic.
That’s why frameworks like LangGraph are explicit about this abstraction.
Why the Hacker News frustration makes sense
On HN, “agent” is criticized as a marketing label:
anything with ≥1 tool call + a loop gets branded as an agent,
even when execution is mostly predefined.
Many production “agents” are actually quite deterministic.
The 2025–2026 practical consensus: a spectrum
Fully predefined DAG
LLM used for fixed tasks
Highly predictable, low cost
→ Zapier/n8n + LLM node, classic RAG
Mostly structured
LLM decides branches, retries, tool choice within guardrails
Bounded loops and planning
→ Used by many real products
→ Andrew Ng: agentic systems
LLM controls its own process
Open-ended planning and self-correction
Rare in production at scale today
The pragmatic definition
Many experienced engineers say:
“If the DAG is largely steered by the LLM at runtime, it’s agentic.”
The winning approach:
➡️ start with workflows
➡️ add agentic behavior where needed
➡️ scale autonomy as models improve and costs drop
AI Agents vs Workflows: Why LLM Agents Are Mostly Graphs (2025–2026 Perspective)
AI agents: less magic, more graphs You’re spot on: most modern “AI agents” boil down to directed graphs , often with cycles. Common structur...
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