2026 AI Agent Tech Stack: How Agents Think, Act, and Remember

1. Overview: Six‑Layer Agent Stack

Understanding a complex system is easiest when it is broken into layers. The author defines a six‑layer stack for AI Agents, analogous to OSI or web development layers:

L1 Model Layer : The foundation; strong LLMs (GPT‑4o, Claude 4, DeepSeek, Qwen3, GLM) are required. Selection criteria are inference ability, context length, and tool‑calling stability.

L2 Framework Layer : Wraps the model into an Agent. Two sub‑categories: code‑centric frameworks (LangChain, LlamaIndex) and no‑code platforms (Dify, Coze). The latter bundle layers L2‑L5, enabling rapid prototyping.

L3 Memory Layer : Addresses LLM forgetfulness. Short‑term memory is prompt‑based history; long‑term memory uses vector databases (Milvus, Pinecone, Chroma) or specialized services (Zep, MemGPT).

L4 Tool Layer : Provides the Agent’s actions. Function Calling (native tool calls), MCP (standardized tool protocol), browser automation (Playwright), and code execution sandboxes (E2B, Docker) are the fastest‑growing tool types.

L5 Orchestration Layer : Manages multi‑step or multi‑Agent workflows. Frameworks such as LangGraph, CrewAI, and AutoGen provide state‑machine or role‑based orchestration.

L6 Observation & Evaluation Layer : Supplies tracing and quality assessment (LangSmith, Langfuse, Ragas) to answer "why did the Agent decide this?".

The author warns that choosing a framework does not automatically cover all layers; most frameworks act as glue, except Coze and Dify which bundle multiple layers at the cost of limited customization.

2. Principle I – Why Agents "Think"

The ability to "think" stems from prompting. The 2022 Chain‑of‑Thought (CoT) paper showed that asking a model to reason step‑by‑step improves accuracy, but CoT cannot interact with the external world.

ReAct (Reasoning + Acting) extends CoT by alternating between thoughts and actions. The author provides a concrete prompt example and a full interaction trace where the model searches for Tesla's stock price, calculates the percentage change, and outputs the final answer.

你是一个助手。你可以使用以下工具：

search: 搜索互联网。输入：搜索关键词。
calculator: 计算器。输入：数学表达式。

使用以下格式：
Question: 用户的问题
Thought: 你现在的想法
Action: 工具名
Action Input: 工具的输入
Observation: 工具返回的结果
...（这个 Thought/Action/Observation 可以重复多次）
Final Answer: 最终答案

开始！

Question: 特斯拉现在的股价比上周涨了多少？
Thought: 我需要先查特斯拉现在的股价。
Action: search
Action Input: 特斯拉 股价

After the search result is fed back, the model continues with a calculation action and finally produces the answer, illustrating that "thinking" is a structured text‑based loop.

Potential pitfalls include infinite loops if the model never emits a Final Answer; therefore, production systems must enforce maximum loop counts and token limits.

3. Principle II – How Agents "Act"

While ReAct uses a fragile text protocol, modern production relies on Function Calling (OpenAI) or tool use (Claude). The model receives a JSON schema describing available tools and returns a structured tool_calls array instead of free‑form text.

{
  "model": "gpt-4o",
  "messages": [{"role": "user", "content": "查一下特斯拉股价"}],
  "tools": [{
    "type": "function",
    "function": {
      "name": "search",
      "description": "搜索互联网",
      "parameters": {
        "type": "object",
        "properties": {"query": {"type": "string", "description": "搜索关键词"}},
        "required": ["query"]
      }
    }
  }]
}

The model returns:

{
  "role": "assistant",
  "content": null,
  "tool_calls": [{
    "id": "call_abc123",
    "type": "function",
    "function": {"name": "search", "arguments": "{\"query\": \"特斯拉 股价\"}"}
  }]
}

This JSON‑based exchange reduces parsing errors by an order of magnitude.

MCP (Model Context Protocol) further decouples tool providers from consumers. An MCP Server exposes tools, resources, or prompts; any Agent framework can act as an MCP client, turning an N×M integration problem into N+M.

The author judges Function Calling as a stable model‑layer standard and MCP as an emerging ecosystem standard with strong momentum in 2026.

4. Principle III – How Agents "Remember"

LLMs are stateless; short‑term memory is achieved by appending prior dialogue to the prompt, limited by context window size and attention decay. Long‑term memory relies on external vector stores (Milvus, Pinecone, Chroma) and is commonly implemented via Retrieval‑Augmented Generation (RAG).

Effective memory requires a "write‑gate" that filters what gets stored (e.g., only user preferences, key decisions, or factual statements). The author also mentions consolidation services (MemGPT, Zep) that periodically cluster, summarize, and compress weekly memory chunks.

5. Principle IV – How Agents "Plan"

Four main planning modes are compared:

ReAct : Interleaved reasoning and acting; flexible but lacks global view.

Plan‑and‑Execute : Generates a full step list first; strong global perspective but brittle to failures.

Reflexion : Adds a self‑critique after each step; improves code‑generation tasks at the cost of roughly double token usage.

Tree of Thoughts (ToT) : Explores multiple reasoning branches simultaneously; highest theoretical performance but very expensive in tokens.

The author’s practical rule: default to ReAct; switch to Plan‑and‑Execute for tasks longer than five steps; add Reflexion for code‑related scenarios; consider ToT only when token budget permits.

6. End‑to‑End Request Flow

A concrete example walks a user request "Summarize today's A‑share tech sector and post a brief report to the department group" through all six layers, detailing which layer handles each step, the exact messages exchanged, and the number of LLM and tool calls (4 LLM calls, 3 tool calls).

The author emphasizes that each step can fail (wrong tool selection, malformed parameters, API timeouts, memory mismatches, non‑converging loops) and that robust tracing (L6) is essential for debugging.

7. Practical Technology Selection

Validate ideas quickly with no‑code platforms (Coze or Dify); they are suited for proof‑of‑concepts.

For deep customization, adopt LangGraph for complex state‑machine workflows.

Multi‑Agent pipelines: start with CrewAI for simple role‑task division, then explore AutoGen/AG2 for richer dialogue‑based coordination.

When adding new tools, implement them as MCP Servers to retain future flexibility.

Prefer off‑the‑shelf vector stores (Milvus or Pinecone) and memory services (Zep) over building from scratch.

Integrate observation from day one using Langfuse (self‑hosted) or LangSmith (managed) to capture traces for production reliability.

8. Summary

Agent stack consists of six layers: Model, Framework, Memory, Tool, Orchestration, Observation; frameworks are glue, not a full solution.

Thinking is a ReAct‑style text loop where the model outputs thoughts and actions.

Acting upgrades the text protocol to structured JSON via Function Calling; MCP standardizes tool ecosystems.

Memory combines prompt‑based short‑term context with vector‑based long‑term storage, gated by relevance filters.

Planning offers four modes—ReAct, Plan‑and‑Execute, Reflexion, Tree of Thoughts—each with trade‑offs.

A real request triggers 4 LLM calls and 3 tool calls; every step must be guarded against failure.

Recommended stack: prototype with Coze/Dify, customize with LangGraph, scale multi‑Agent with CrewAI or AutoGen, use MCP for new tools, adopt Milvus/Pinecone for memory, and enable tracing via Langfuse or LangSmith.