📊 智能体资源感知优化：AI系统的经济性与可靠性平衡术本文深入探讨智能体资源感知优化技术，分析了经济策略实现、多智能体

在研读《资源感知优化》技术文档并结合当前AI智能体发展趋势后，我对智能体系统中的资源管理有了更系统的认识。资源感知优化不仅是技术实现问题，更是智能体能否在真实环境中可持续发展的核心考量。

📌 一、智能体经济策略的技术实现路径

文档强调的资源感知优化与"智能体经济"概念高度契合。李开复博士指出，AI智能体将成为"永不休息的数字员工"，边际成本趋近于零。这一经济特性在技术层面的实现，依赖于动态资源分配机制。

在设计和开发智能体系统时，我们需要将经济性作为核心架构考量。比如在电商客服场景中，可以借鉴路由智能体理念，设计多层级问答系统。简单查询由轻量级模型处理，复杂问题才动用高端模型。这种架构在火山引擎的HiAgent平台已得到验证，能够在金融、医疗等行业实现成本与效果的平衡。

📌 二、多智能体协同架构的资源优化价值

文档中介绍的Google ADK多智能体架构，与联想集团落地的"城市超级智能体"采用的"1×N智能体方案"有异曲同工之妙。这种架构通过功能分解实现了资源的精细化管理。

在智慧城市类项目中，我们可以参考此模式，设计"总控智能体+专业智能体"的架构。总控智能体负责任务分解和路由，专业智能体处理特定任务，比如交通调度、安防监控等。河北灵寿的智慧城市实践表明，这种架构能够有效提升系统整体资源利用率。

📌 三、动态路由与回退机制保障服务可靠性

文档强调的动态模型切换和回退机制，在实际项目中是确保服务可靠性的关键技术。当主模型不可用时，系统能自动降级至备用模型，这与湘潭市公共资源交易中心的"机器管招投标"系统的故障切换逻辑相似。

在API服务设计中，应当实现模型健康度监控和自动切换机制。可以设置响应时间阈值，当主模型响应超时，自动触发备用模型。同时，结合文档提到的Critique Agent机制，持续评估路由决策的有效性，形成优化闭环。

📌 四、边缘环境下的能效优化策略

文档提到的能源效率优化对于边缘计算场景尤为重要。智慧家居场景中的云边协同智能体通过将用户习惯数据上传至云端训练个性化模型，再通过边缘计算节点实时调控家庭环境设备，这正是资源感知优化的典型应用。

在物联网项目中，可采用模型蒸馏和量化技术，将大模型压缩为适合边缘设备部署的轻量级版本。同时，设计基于设备电量状态的自适应推理策略：电量充足时使用高精度模型，电量紧张时切换为节能模式。

📌 五、从"工具"到"结果"的商业模式实现

李开复博士提到，AI的价值正从"工具"走向"服务"，最终走向"结果"。这一转变要求智能体不仅关注过程优化，更要确保最终输出质量。文档中的Critique Agent通过评估响应质量，为这一转变提供了技术支撑。

在内容生成类项目中，可设计多轮优化机制：首轮生成使用经济模型，然后通过Critique Agent评估质量，不达标时再使用更强模型进行优化。这种"生成-评估-优化"循环既控制了成本，又保障了输出质量。

🔍 项目实施路线图

基于以上分析，我总结了在项目中实践资源感知优化的关键步骤：

需求分析与资源量化：明确项目的性能、成本、时延要求，建立可量化的资源预算指标。
智能体架构设计：采用模块化设计，区分路由、执行、评估等职能，确保各组件可独立优化。
动态路由策略实现：基于查询复杂度、实时负载、资源余额等因素设计路由逻辑。
回退与降级机制：为关键服务设计备用路径，确保核心功能在资源紧张时仍可用。
持续评估与优化：建立质量评估体系，利用Critique Agent反馈持续优化资源分配策略。

💡 写在最后

资源感知优化是智能体从实验室走向产业化应用的关键技术。正如上海市加快推动"AI+制造"实施方案所强调的，工业智能体需要深化人工智能技术与制造业的深度融合。作为技术人员，我们需要在架构设计初期就充分考虑资源约束，将经济性和可持续性融入系统基因。

通过将文档中的技术模式与产业实践相结合，我们能够构建出既智能又经济的智能体系统，真正实现AI技术的价值落地。在未来的项目中，优先考虑资源感知优化设计，确保系统在实现功能目标的同时，保持高效、可靠和可持续的运行状态。

⚠️ 互动提问：你在实际项目中遇到过哪些智能体资源管理的挑战？欢迎在评论区分享你的经验和想法！

✅ 行动建议：下次设计智能体系统时，先从资源预算和优化策略入手，这往往能让你的项目走得更远。

Chapter 16: Resource-Aware Optimization

第16章：资源感知优化

Resource-Aware Optimization enables intelligent agents to dynamically monitor and manage computational, temporal, and financial resources during operation. This differs from simple planning, which primarily focuses on action sequencing. Resource-Aware Optimization requires agents to make decisions regarding action execution to achieve goals within specified resource budgets or to optimize efficiency. This involves choosing between more accurate but expensive models and faster, lower-cost ones, or deciding whether to allocate additional compute for a more refined response versus returning a quicker, less detailed answer.

资源感知优化（Resource-Aware Optimization）使智能体能够在运行过程中动态监控和管理计算、时间和财务资源。这与简单规划不同，后者主要关注动作序列。资源感知优化要求智能体做出关于动作执行的决策，以在指定资源预算内实现目标或优化效率。这涉及在更准确但更昂贵的模型与更快、成本更低的模型之间进行选择，或者决定是否分配额外计算资源以获得更精细的响应，还是返回更快但更粗略的答案。

For example, consider an agent tasked with analyzing a large dataset for a financial analyst. If the analyst needs a preliminary report immediately, the agent might use a faster, more affordable model to quickly summarize key trends. However, if the analyst requires a highly accurate forecast for a critical investment decision and has a larger budget and more time, the agent would allocate more resources to utilize a powerful, slower, but more precise predictive model. A key strategy in this category is the fallback mechanism, which acts as a safeguard when a preferred model is unavailable due to being overloaded or throttled. To ensure graceful degradation, the system automatically switches to a default or more affordable model, maintaining service continuity instead of failing completely.

例如，考虑一个负责为金融分析师分析大型数据集的智能体。如果分析师需要立即获得初步报告，智能体可能会使用更快、更经济的模型来快速总结关键趋势。然而，如果分析师需要高度准确的预测来做出关键投资决策，并且有更大的预算和更多时间，智能体就会分配更多资源来使用强大、速度较慢但更精确的预测模型。这一类别中的一个关键策略是回退机制（fallback mechanism），当首选模型因过载或限流而不可用时，它可以作为一种保障。为确保优雅降级（graceful degradation），系统会自动切换到默认或更经济的模型，保持服务连续性而不是完全失败。

Practical Applications & Use Cases

实际应用和用例

Practical use cases include:

实际用例包括：

Cost-Optimized LLM Usage: An agent deciding whether to use a large, expensive LLM for complex tasks or a smaller, more affordable one for simpler queries, based on a budget constraint.
Latency-Sensitive Operations: In real-time systems, an agent chooses a faster but potentially less comprehensive reasoning path to ensure a timely response.
Energy Efficiency: For agents deployed on edge devices or with limited power, optimizing their processing to conserve battery life.
Fallback for service reliability: An agent automatically switches to a backup model when the primary choice is unavailable, ensuring service continuity and graceful degradation.
Data Usage Management: An agent opting for summarized data retrieval instead of full dataset downloads to save bandwidth or storage.
Adaptive Task Allocation: In multi-agent systems, agents self-assign tasks based on their current computational load or available time.
成本优化的LLM使用（Cost-Optimized LLM Usage）：智能体基于预算约束决定是使用大型、昂贵的LLM处理复杂任务，还是使用更小、更经济的模型处理简单查询。
低延迟操作（Latency-Sensitive Operations）：在实时系统中，智能体选择更快但可能不够全面的推理路径，以确保及时响应。
能源效率（Energy Efficiency）：对于部署在边缘设备或电力有限的智能体，优化其处理以节省电池寿命。
服务可靠性回退（Fallback for service reliability）：当首选模型不可用时，智能体自动切换到备用模型，确保服务连续性和优雅降级。
数据使用管理（Data Usage Management）：智能体选择汇总数据检索而非完整数据集下载，以节省带宽或存储。
自适应任务分配（Adaptive Task Allocation）：在多智能体系统中，智能体基于当前计算负载或可用时间自分配任务。

Hands-On Code Example

实战代码示例

An intelligent system for answering user questions can assess the difficulty of each question. For simple queries, it utilizes a cost-effective language model such as Gemini Flash. For complex inquiries, a more powerful, but expensive, language model (like Gemini Pro) is considered. The decision to use the more powerful model also depends on resource availability, specifically budget and time constraints. This system dynamically selects appropriate models.

一个回答用户问题的智能系统可以评估每个问题的难度。对于简单查询，它使用具有成本效益的语言模型，如Gemini Flash。对于复杂查询，它会考虑更强大但更昂贵的语言模型（如Gemini Pro）。使用更强大模型的决定还取决于资源可用性，特别是预算和时间限制。该系统动态选择适当的模型。

For example, consider a travel planner built with a hierarchical agent. The high-level planning, which involves understanding a user's complex request, breaking it down into a multi-step itinerary, and making logical decisions, would be managed by a sophisticated and more powerful LLM like Gemini Pro. This is the "planner" agent that requires a deep understanding of context and the ability to reason.

例如，考虑一个使用层次化智能体构建的旅行规划器。高级规划涉及理解用户的复杂请求、将其分解为多步行程并做出逻辑决策，这将由像Gemini Pro这样复杂且更强大的LLM管理。这是"规划器"智能体，需要对上下文有深入理解和推理能力。

However, once the plan is established, the individual tasks within that plan, such as looking up flight prices, checking hotel availability, or finding restaurant reviews, are essentially simple, repetitive web queries. These "tool function calls" can be executed by a faster and more affordable model like Gemini Flash. It is easier to visualize why the affordable model can be used for these straightforward web searches, while the intricate planning phase requires the greater intelligence of the more advanced model to ensure a coherent and logical travel plan.

然而，一旦计划确立，计划中的各个任务，如查询航班价格、检查酒店可用性或查找餐厅评论，本质上是简单、重复的网络查询。这些"工具函数调用"（tool function calls）可以由更快、更经济的模型如Gemini Flash执行。我们更容易理解为什么经济的模型可以用于这些直接的网络搜索，而复杂的规划阶段需要更先进模型的更高智能，以确保旅行计划连贯且符合逻辑。

Google's ADK supports this approach through its multi-agent architecture, which allows for modular and scalable applications. Different agents can handle specialized tasks. Model flexibility enables the direct use of various Gemini models, including both Gemini Pro and Gemini Flash, or integration of other models through LiteLLM. The ADK's orchestration capabilities support dynamic, LLM-driven routing for adaptive behavior. Built-in evaluation features allow systematic assessment of agent performance, which can be used for system refinement (see the Chapter on Evaluation and Monitoring).

Google的智能体开发工具包（Agent Development Kit，ADK）通过其多智能体架构支持这种方法，允许多模块化和可扩展的应用程序。不同的智能体可以处理专门的任务。模型灵活性允许直接使用各种Gemini模型，包括Gemini Pro和Gemini Flash，或通过LiteLLM集成其他模型。ADK的编排能力支持动态、LLM驱动的路由以实现自适应行为。内置的评估功能允许系统评估智能体性能，可用于系统改进（参见评估和监控章节）。

Next, two agents with identical setup but utilizing different models and costs will be defined.

接下来，将定义两个设置相同但使用不同模型和成本的智能体。

# Conceptual Python-like structure, not runnable code
from google.adk.agents import Agent
# from google.adk.models.lite_llm import LiteLlm  # If using models not directly supported by ADK's default Agent

# Agent using the more expensive Gemini Pro 2.5
gemini_pro_agent = Agent(
    name="GeminiProAgent",
    model="gemini-2.5-pro",  # Placeholder for actual model name if different
    description="A highly capable agent for complex queries.",
    instruction="You are an expert assistant for complex problem-solving."
)

# Agent using the less expensive Gemini Flash 2.5
gemini_flash_agent = Agent(
    name="GeminiFlashAgent",
    model="gemini-2.5-flash",  # Placeholder for actual model name if different
    description="A fast and efficient agent for simple queries.",
    instruction="You are a quick assistant for straightforward questions."
)

A Router Agent can direct queries based on simple metrics like query length, where shorter queries go to less expensive models and longer queries to more capable models. However, a more sophisticated Router Agent can utilize either LLM or ML models to analyze query nuances and complexity. This LLM router can determine which downstream language model is most suitable. For example, a query requesting a factual recall is routed to a flash model, while a complex query requiring deep analysis is routed to a pro model.

路由智能体（Router Agent）可以基于简单指标（如查询长度）引导查询，较短的查询发送到较便宜的模型，较长的查询发送到更强大的模型。然而，更复杂的路由智能体可以利用LLM或ML模型来分析查询的细微差别和复杂性。这种LLM路由器可以确定哪个下游语言模型最合适。例如，请求事实回忆的查询被路由到flash模型，而需要深入分析的复杂查询被路由到pro模型。

Optimization techniques can further enhance the LLM router's effectiveness. Prompt tuning involves crafting prompts to guide the router LLM for better routing decisions. Fine-tuning the LLM router on a dataset of queries and their optimal model choices improves its accuracy and efficiency. This dynamic routing capability balances response quality with cost-effectiveness.

优化技术可以进一步提高LLM路由器的有效性。提示词调优（Prompt tuning）涉及精心设计提示词，引导路由器LLM做出更好的路由决策。在查询数据集及其最佳模型选择上微调LLM路由器可以提高其准确性和效率。这种动态路由能力平衡了响应质量和成本效益。

# Conceptual Python-like structure, not runnable code
from google.adk.agents import Agent, BaseAgent
from google.adk.events import Event
from google.adk.agents.invocation_context import InvocationContext
import asyncio

class QueryRouterAgent(BaseAgent):
    name: str = "QueryRouter"
    description: str = "Routes user queries to the appropriate LLM agent based on complexity."
    
    async def _run_async_impl(self, context: InvocationContext) -> AsyncGenerator[Event, None]:
        user_query = context.current_message.text # Assuming text input
        query_length = len(user_query.split()) # Simple metric: number of words
        
        if query_length < 20: # Example threshold for simplicity vs. complexity
            print(f"Routing to Gemini Flash Agent for short query (length: {query_length})")
            # In a real ADK setup, you would 'transfer_to_agent' or directly invoke
            # For demonstration, we'll simulate a call and yield its response
            response = await gemini_flash_agent.run_async(context.current_message)
            yield Event(author=self.name, content=f"Flash Agent processed: {response}")
        else:
            print(f"Routing to Gemini Pro Agent for long query (length: {query_length})")
            response = await gemini_pro_agent.run_async(context.current_message)
            yield Event(author=self.name, content=f"Pro Agent processed: {response}")

The Critique Agent evaluates responses from language models, providing feedback that serves several functions. For self-correction, it identifies errors or inconsistencies, prompting the answering agent to refine its output for improved quality. It also systematically assesses responses for performance monitoring, tracking metrics like accuracy and relevance, which are used for optimization.

评论智能体（Critique Agent）评估语言模型的响应，提供具有多种功能的反馈。对于自我纠正，它识别错误或不一致，促使回答智能体改进其输出以提高质量。它还系统评估响应以进行性能监控，跟踪准确性和相关性等指标，用于优化。

Additionally, its feedback can signal reinforcement learning or fine-tuning; consistent identification of inadequate Flash model responses, for instance, can refine the router agent's logic. While not directly managing the budget, the Critique Agent contributes to indirect budget management by identifying suboptimal routing choices, such as directing simple queries to a Pro model or complex queries to a Flash model, which leads to poor results. This informs adjustments that improve resource allocation and cost savings.

此外，其反馈可以作为强化学习或微调的信号；例如，一致识别Flash模型响应不足可以完善路由智能体的逻辑。虽然评论智能体不直接管理预算，但它通过识别次优路由选择（如将简单查询引导至Pro模型或将复杂查询引导至Flash模型，导致结果不佳）来间接管理预算。这为改进资源分配和节省成本的调整提供了信息。

The Critique Agent can be configured to review either only the generated text from the answering agent or both the original query and the generated text, enabling a comprehensive evaluation of the response's alignment with the initial question.

评论智能体可以配置为仅审查回答智能体生成的文本，或者同时审查原始查询和生成的文本，从而能够全面评估响应与初始问题的一致性。

# Example system prompt for the Critic Agent
CRITIC_SYSTEM_PROMPT = """
You are the **Critic Agent**, serving as the quality assurance arm of our collaborative research assistant system. Your primary function is to **meticulously review and challenge** information from the Researcher Agent, guaranteeing **accuracy, completeness, and unbiased presentation**. Your duties encompass: * **Assessing research findings** for factual correctness, thoroughness, and potential leanings. * **Identifying any missing data** or inconsistencies in reasoning. * **Raising critical questions** that could refine or expand the current understanding. * **Offering constructive suggestions** for enhancement or exploring different angles. * **Validating that the final output is comprehensive** and balanced. All criticism must be constructive. Your goal is to fortify the research, not invalidate it. Structure your feedback clearly, drawing attention to specific points for revision. Your overarching aim is to ensure the final research product meets the highest possible quality standards.
"""

The Critic Agent operates based on a predefined system prompt that outlines its role, responsibilities, and feedback approach. A well-designed prompt for this agent must clearly establish its function as an evaluator. It should specify the areas for critical focus and emphasize providing constructive feedback rather than mere dismissal. The prompt should also encourage the identification of both strengths and weaknesses, and it must guide the agent on how to structure and present its feedback.

评论智能体基于预定义的系统提示词运行，该提示词概述了其角色、职责和反馈方法。为该智能体设计的良好提示词必须明确确立其作为评估者的功能。它应该指定关键关注点，并强调提供建设性反馈而非仅仅驳回。提示词还应该鼓励识别优点和缺点，并必须指导智能体如何组织和呈现其反馈。

Hands-On Code with OpenAI

使用OpenAI的实战代码

This system uses a resource-aware optimization strategy to handle user queries efficiently. It first classifies each query into one of three categories to determine the most appropriate and cost-effective processing pathway. This approach avoids wasting computational resources on simple requests while ensuring complex queries get the necessary attention. The three categories are:

该系统使用资源感知优化策略来高效处理用户查询。它首先将每个查询分类为三个类别之一，以确定最合适且具有成本效益的处理路径。这种方法避免在简单请求上浪费计算资源，同时确保复杂查询得到必要的关注。这三个类别是：

simple: For straightforward questions that can be answered directly without complex reasoning or external data.
reasoning: For queries that require logical deduction or multi-step thought processes, which are routed to more powerful models.
internet_search: For questions needing current information, which automatically triggers a Google Search to provide an up-to-date answer.
simple（简单）：对于可以直接回答而不需要复杂推理或外部数据的直接问题。
reasoning（推理）：对于需要逻辑推理或多步思考过程的查询，这些查询会被路由到更强大的模型。
internet_search（互联网搜索）：对于需要当前信息的问题，自动触发Google搜索以提供最新答案。

The code is under the MIT license and available on Github: (github.com/mahtabsyed/…)

代码采用MIT许可证，可在GitHub上获取：(github.com/mahtabsyed/…)

# MIT License
# Copyright (c) 2025 Mahtab Syed
# https://www.linkedin.com/in/mahtabsyed/

import os
import requests
import json
from dotenv import load_dotenv
from openai import OpenAI

# Load environment variables
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
GOOGLE_CUSTOM_SEARCH_API_KEY = os.getenv("GOOGLE_CUSTOM_SEARCH_API_KEY")
GOOGLE_CSE_ID = os.getenv("GOOGLE_CSE_ID")

if not OPENAI_API_KEY or not GOOGLE_CUSTOM_SEARCH_API_KEY or not GOOGLE_CSE_ID:
    raise ValueError(
        "Please set OPENAI_API_KEY, GOOGLE_CUSTOM_SEARCH_API_KEY, and GOOGLE_CSE_ID in your .env file."
    )

client = OpenAI(api_key=OPENAI_API_KEY)

# --- Step 1: Classify the Prompt ---
def classify_prompt(prompt: str) -> dict:
    system_message = {
        "role": "system",
        "content": (
            "You are a classifier that analyzes user prompts and returns one of three categories ONLY:\n\n"
            "- simple\n"
            "- reasoning\n"
            "- internet_search\n\n"
            "Rules:\n"
            "- Use 'simple' for direct factual questions that need no reasoning or current events.\n"
            "- Use 'reasoning' for logic, math, or multi-step inference questions.\n"
            "- Use 'internet_search' if the prompt refers to current events, recent data, or things not in your training data.\n\n"
            "Respond ONLY with JSON like:\n"
            '{ "classification": "simple" }'
        ),
    }
    user_message = {"role": "user", "content": prompt}
    response = client.chat.completions.create(
        model="gpt-4o", messages=[system_message, user_message], temperature=1
    )
    reply = response.choices[0].message.content
    return json.loads(reply)

# --- Step 2: Google Search ---
def google_search(query: str, num_results=1) -> list:
    url = "https://www.googleapis.com/customsearch/v1"
    params = {
        "key": GOOGLE_CUSTOM_SEARCH_API_KEY,
        "cx": GOOGLE_CSE_ID,
        "q": query,
        "num": num_results,
    }
    try:
        response = requests.get(url, params=params)
        response.raise_for_status()
        results = response.json()
        if "items" in results and results["items"]:
            return [
                {
                    "title": item.get("title"),
                    "snippet": item.get("snippet"),
                    "link": item.get("link"),
                }
                for item in results["items"]
            ]
        else:
            return []
    except requests.exceptions.RequestException as e:
        return {"error": str(e)}

# --- Step 3: Generate Response ---
def generate_response(prompt: str, classification: str, search_results=None) -> str:
    if classification == "simple":
        model = "gpt-4o-mini"
        full_prompt = prompt
    elif classification == "reasoning":
        model = "o4-mini"
        full_prompt = prompt
    elif classification == "internet_search":
        model = "gpt-4o"
        # Convert each search result dict to a readable string
        if search_results:
            search_context = "\n".join(
                [
                    f"Title: {item.get('title')}\nSnippet: {item.get('snippet')}\nLink: {item.get('link')}"
                    for item in search_results
                ]
            )
        else:
            search_context = "No search results found."
        full_prompt = f"""Use the following web results to answer the user query: {search_context} Query: {prompt}"""
    
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": full_prompt}],
        temperature=1,
    )
    return response.choices[0].message.content, model

# --- Step 4: Combined Router ---
def handle_prompt(prompt: str) -> dict:
    classification_result = classify_prompt(prompt)
    # Remove or comment out the next line to avoid duplicate printing
    # print("\n🔍 Classification Result:", classification_result)
    classification = classification_result["classification"]
    search_results = None
    if classification == "internet_search":
        search_results = google_search(prompt)
        # print("\n🔍 Search Results:", search_results)
    answer, model = generate_response(prompt, classification, search_results)
    return {"classification": classification, "response": answer, "model": model}

test_prompt = "What is the capital of Australia?"
# test_prompt = "Explain the impact of quantum computing on cryptography."
# test_prompt = "When does the Australian Open 2026 start, give me full date?"
result = handle_prompt(test_prompt)
print("🔍 Classification:", result["classification"])
print("🧠 Model Used:", result["model"])
print("🧠 Response:\n", result["response"])

This Python code implements a prompt routing system to answer user questions. It begins by loading necessary API keys from a .env file for OpenAI and Google Custom Search. The core functionality lies in classifying the user's prompt into three categories: simple, reasoning, or internet search. A dedicated function utilizes an OpenAI model for this classification step. If the prompt requires current information, a Google search is performed using the Google Custom Search API. Another function then generates the final response, selecting an appropriate OpenAI model based on the classification. For internet search queries, the search results are provided as context to the model. The main handle_prompt function orchestrates this workflow, calling the classification and search (if needed) functions before generating the response. It returns the classification, the model used, and the generated answer. This system efficiently directs different types of queries to optimized methods for a better response.

这段Python代码实现了一个提示词路由系统来回答用户问题。它首先从.env文件加载OpenAI和Google自定义搜索所需的API密钥。核心功能在于将用户的提示词分类为三个类别：简单、推理或互联网搜索。一个专用函数利用OpenAI模型进行此分类步骤。如果提示词需要当前信息，则使用Google自定义搜索API执行Google搜索。另一个函数然后生成最终响应，根据分类选择适当的OpenAI模型。对于互联网搜索查询，搜索结果作为上下文提供给模型。主要的handle_prompt函数编排此工作流程，在生成响应之前调用分类和搜索（如果需要）函数。它返回分类、使用的模型和生成的答案。该系统有效地将不同类型的查询引导至优化的方法，以获得更好的响应。

Hands-On Code Example (OpenRouter)

实战代码示例（OpenRouter）

OpenRouter offers a unified interface to hundreds of AI models via a single API endpoint. It provides automated failover and cost-optimization, with easy integration through your preferred SDK or framework.

OpenRouter通过单一API端点提供对数百个AI模型的统一接口。它提供自动故障转移和成本优化，可通过您首选的SDK或框架轻松集成。

import requests
import json

response = requests.post(
  url="https://openrouter.ai/api/v1/chat/completions",
  headers={
    "Authorization": "Bearer <OPENROUTER_API_KEY>",
    "HTTP-Referer": "<YOUR_SITE_URL>",  # Optional. Site URL for rankings on openrouter.ai.
    "X-Title": "<YOUR_SITE_NAME>",  # Optional. Site title for rankings on openrouter.ai.
  },
  data=json.dumps({
    "model": "openai/gpt-4o",  # Optional
    "messages": [
      {
        "role": "user",
        "content": "What is the meaning of life?"
      }
    ]
  })
)

This code snippet uses the requests library to interact with the OpenRouter API. It sends a POST request to the chat completion endpoint with a user message. The request includes authorization headers with an API key and optional site information. The goal is to get a response from a specified language model, in this case, "openai/gpt-4o".

这段代码片段使用requests库与OpenRouter API交互。它向聊天完成端点发送POST请求，包含用户消息。请求包括带有API密钥的授权头和可选的站点信息。目标是从指定的语言模型获取响应，在这种情况下是"openai/gpt-4o"。

Openrouter offers two distinct methodologies for routing and determining the computational model used to process a given request.

OpenRouter提供两种不同的方法来路由和确定用于处理给定请求的计算模型。

Automated Model Selection: This function routes a request to an optimized model chosen from a curated set of available models. The selection is predicated on the specific content of the user's prompt. The identifier of the model that ultimately processes the request is returned in the response's metadata.
自动模型选择（Automated Model Selection）：此功能将请求路由到从精选可用模型集中选择的优化模型。选择基于用户提示词的特定内容。最终处理请求的模型的标识符在响应的元数据中返回。

{
  "model": "openrouter/auto",
  ... // Other params
}

Sequential Model Fallback: This mechanism provides operational redundancy by allowing users to specify a hierarchical list of models. The system will first attempt to process the request with the primary model designated in the sequence. Should this primary model fail to respond due to any number of error conditions—such as service unavailability, rate-limiting, or content filtering—the system will automatically re-route the request to the next specified model in the sequence. This process continues until a model in the list successfully executes the request or the list is exhausted. The final cost of the operation and the model identifier returned in the response will correspond to the model that successfully completed the computation.
顺序模型回退（Sequential Model Fallback）：此机制通过允许用户指定模型的层次列表来提供操作冗余。系统将首先尝试使用序列中指定的主要模型处理请求。如果该主要模型由于任何错误条件（如服务不可用、速率限制或内容过滤）而无法响应，系统将自动将请求重新路由到序列中的下一个指定模型。此过程一直持续，直到列表中的模型成功执行请求或列表用尽。操作的最终成本和响应中返回的模型标识符将对应于成功完成计算的模型。

`{ "models": ["anthropic/claude-3.5-sonnet", "gryphe/mythomax-l2-13b"], ... // Other params }`

OpenRouter offers a detailed leaderboard ( openrouter.ai/rankings) which ranks available AI models based on their cumulative token production. It also offers latest models from different providers (ChatGPT, Gemini, Claude) (see Fig. 1)

OpenRouter提供详细的排行榜（openrouter.ai/rankings），根据可用AI模型的累积令牌生产进行排名。它还提供来自不同提供商的最新模型（ChatGPT、Gemini、Claude）（见图1）

Fig. 1: OpenRouter Web site (openrouter.ai/)

Beyond Dynamic Model Switching: A Spectrum of Agent Resource Optimizations

超越动态模型切换：智能体资源优化的谱系

Resource-aware optimization is paramount in developing intelligent agent systems that operate efficiently and effectively within real-world constraints. Let's see a number of additional techniques:

资源感知优化在开发能在现实世界约束下高效运行的智能体系统中至关重要。让我们看看一些额外的技术：

Dynamic Model Switching is a critical technique involving the strategic selection of large language models based on the intricacies of the task at hand and the available computational resources. When faced with simple queries, a lightweight, cost-effective LLM can be deployed, whereas complex, multifaceted problems necessitate the utilization of more sophisticated and resource-intensive models.

动态模型切换（Dynamic Model Switching）是一种关键技术，涉及基于当前任务的复杂性和可用计算资源战略性选择大型语言模型。当面临简单查询时，可以部署轻量级、具有成本效益的LLM，而复杂、多方面的问题则需要利用更复杂且资源密集型的模型。

Adaptive Tool Use & Selection ensures agents can intelligently choose from a suite of tools, selecting the most appropriate and efficient one for each specific sub-task, with careful consideration given to factors like API usage costs, latency, and execution time. This dynamic tool selection enhances overall system efficiency by optimizing the use of external APIs and services.

自适应工具使用与选择（Adaptive Tool Use & Selection）确保智能体可以从一系列工具中智能选择，为每个特定子任务选择最合适和高效的工具，同时仔细考虑API使用成本、延迟和执行时间等因素。这种动态工具选择通过优化外部API和服务的使用来提高整体系统效率。

Contextual Pruning & Summarization plays a vital role in managing the amount of information processed by agents, strategically minimizing the prompt token count and reducing inference costs by intelligently summarizing and selectively retaining only the most relevant information from the interaction history, preventing unnecessary computational overhead.

上下文修剪与摘要（Contextual Pruning & Summarization）在管理智能体处理的信息量方面发挥着至关重要的作用，通过智能总结和选择性地仅保留交互历史中最相关的信息，战略性地最小化提示词令牌数量并降低推理成本，防止不必要的计算开销。

Proactive Resource Prediction involves anticipating resource demands by forecasting future workloads and system requirements, which allows for proactive allocation and management of resources, ensuring system responsiveness and preventing bottlenecks.

主动资源预测（Proactive Resource Prediction）涉及通过预测未来工作负载和系统需求来预测资源需求，这允许主动分配和管理资源，确保系统响应能力并防止瓶颈。

Cost-Sensitive Exploration in multi-agent systems extends optimization considerations to encompass communication costs alongside traditional computational costs, influencing the strategies employed by agents to collaborate and share information, aiming to minimize the overall resource expenditure.

多智能体系统中的成本敏感探索（Cost-Sensitive Exploration）将优化考虑扩展到除了传统计算成本外还包括通信成本，影响智能体用于协作和共享信息的策略，旨在最小化整体资源支出。

Energy-Efficient Deployment is specifically tailored for environments with stringent resource constraints, aiming to minimize the energy footprint of intelligent agent systems, extending operational time and reducing overall running costs.

节能部署（Energy-Efficient Deployment）专为资源约束严格的环境量身定制，旨在最小化智能体系统的能源消耗，延长运行时间并降低整体运行成本。

Parallelization & Distributed Computing Awareness leverages distributed resources to enhance the processing power and throughput of agents, distributing computational workloads across multiple machines or processors to achieve greater efficiency and faster task completion.

并行化与分布式计算感知（Parallelization & Distributed Computing Awareness）利用分布式资源增强智能体的处理能力和吞吐量，将计算工作负载分布在多台机器或处理器上，以实现更高的效率和更快的任务完成。

Learned Resource Allocation Policies introduce a learning mechanism, enabling agents to adapt and optimize their resource allocation strategies over time based on feedback and performance metrics, improving efficiency through continuous refinement.

学习资源分配策略（Learned Resource Allocation Policies）引入了学习机制，使智能体能够基于反馈和性能指标随着时间的推移调整和优化其资源分配策略，通过持续改进提高效率。

Graceful Degradation and Fallback Mechanisms ensure that intelligent agent systems can continue to function, albeit perhaps at a reduced capacity, even when resource constraints are severe, gracefully degrading performance and falling back to alternative strategies to maintain operation and provide essential functionality.

优雅降级和回退机制（Graceful Degradation and Fallback Mechanisms）确保智能体系统即使在资源约束严重的情况下也能继续运行，尽管可能以降低的容量运行，优雅地降低性能并回退到替代策略，以维持运行并提供基本功能。

At a Glance

一目了然

What: Resource-Aware Optimization addresses the challenge of managing the consumption of computational, temporal, and financial resources in intelligent systems. LLM-based applications can be expensive and slow, and selecting the best model or tool for every task is often inefficient. This creates a fundamental trade-off between the quality of a system's output and the resources required to produce it. Without a dynamic management strategy, systems cannot adapt to varying task complexities or operate within budgetary and performance constraints.

**什么是：**资源感知优化解决了在智能系统中管理计算、时间和财务资源消耗的挑战。基于LLM的应用程序可能昂贵且缓慢，为每个任务选择最佳模型或工具通常效率低下。这在系统输出质量和产生该输出所需资源之间造成了基本权衡。没有动态管理策略，系统无法适应不同的任务复杂性或在预算和性能约束内运行。

Why: The standardized solution is to build an agentic system that intelligently monitors and allocates resources based on the task at hand. This pattern typically employs a "Router Agent" to first classify the complexity of an incoming request. The request is then forwarded to the most suitable LLM or tool—a fast, inexpensive model for simple queries, and a more powerful one for complex reasoning. A "Critique Agent" can further refine the process by evaluating the quality of the response, providing feedback to improve the routing logic over time. This dynamic, multi-agent approach ensures the system operates efficiently, balancing response quality with cost-effectiveness.

**为什么：**标准化解决方案是构建一个智能体系统，根据当前任务智能监控和分配资源。这种模式通常使用"路由智能体"首先对传入请求的复杂性进行分类。然后将请求转发到最合适的LLM或工具—简单查询使用快速、廉价的模型，复杂推理使用更强大的模型。"评论智能体"可以通过评估响应质量进一步完善这一过程，提供反馈以改进路由逻辑。这种动态的多智能体方法确保系统高效运行，平衡响应质量和成本效益。

Rule of thumb: Use this pattern when operating under strict financial budgets for API calls or computational power, building latency-sensitive applications where quick response times are critical, deploying agents on resource-constrained hardware such as edge devices with limited battery life, programmatically balancing the trade-off between response quality and operational cost, and managing complex, multi-step workflows where different tasks have varying resource requirements.

**经验法则：**在以下情况下使用此模式：在API调用或计算能力方面预算严格、构建快速响应时间至关重要的低延迟应用程序、在资源受限硬件（如电池寿命有限的边缘设备）上部署智能体、以编程方式平衡响应质量和运营成本之间的权衡，以及管理不同任务具有不同资源需求的复杂多步工作流程。

Visual Summary 视觉摘要

Fig. 2: Resource-Aware Optimization Design Pattern 图2：资源感知优化设计模式

Key Takeaways

关键要点

Resource-Aware Optimization is Essential: Intelligent agents can manage computational, temporal, and financial resources dynamically. Decisions regarding model usage and execution paths are made based on real-time constraints and objectives.
Multi-Agent Architecture for Scalability: Google's ADK provides a multi-agent framework, enabling modular design. Different agents (answering, routing, critique) handle specific tasks.
Dynamic, LLM-Driven Routing: A Router Agent directs queries to language models (Gemini Flash for simple, Gemini Pro for complex) based on query complexity and budget. This optimizes cost and performance.
Critique Agent Functionality: A dedicated Critique Agent provides feedback for self-correction, performance monitoring, and refining routing logic, enhancing system effectiveness.
Optimization Through Feedback and Flexibility: Evaluation capabilities for critique and model integration flexibility contribute to adaptive and self-improving system behavior.
Additional Resource-Aware Optimizations: Other methods include Adaptive Tool Use & Selection, Contextual Pruning & Summarization, Proactive Resource Prediction, Cost-Sensitive Exploration in Multi-Agent Systems, Energy-Efficient Deployment, Parallelization & Distributed Computing Awareness, Learned Resource Allocation Policies, Graceful Degradation and Fallback Mechanisms, and Prioritization of Critical Tasks.
资源感知优化至关重要：智能体可以动态管理计算、时间和财务资源。关于模型使用和执行路径的决策基于实时约束和目标。
用于可扩展性的多智能体架构：Google的ADK提供多智能体框架，支持模块化设计。不同的智能体（回答、路由、评论）处理特定任务。
动态、LLM驱动的路由：路由智能体根据查询复杂性和预算将查询引导至语言模型（简单查询使用Gemini Flash，复杂查询使用Gemini Pro）。这优化了成本和性能。
评论智能体功能：专用评论智能体提供反馈用于自我纠正、性能监控和完善路由逻辑，提高系统有效性。
通过反馈和灵活性进行优化：评论的评估能力和模型集成的灵活性有助于自适应和自我改进的系统行为。
其他资源感知优化：其他方法包括自适应工具使用与选择、上下文修剪与摘要、主动资源预测、多智能体系统中的成本敏感探索、节能部署、并行化与分布式计算感知、学习资源分配策略、优雅降级和回退机制，以及关键任务的优先级排序。

Conclusions

结论

Resource-aware optimization is essential for the development of intelligent agents, enabling efficient operation within real-world constraints. By managing computational, temporal, and financial resources, agents can achieve optimal performance and cost-effectiveness. Techniques such as dynamic model switching, adaptive tool use, and contextual pruning are crucial for attaining these efficiencies. Advanced strategies, including learned resource allocation policies and graceful degradation, enhance an agent's adaptability and resilience under varying conditions. Integrating these optimization principles into agent design is fundamental for building scalable, robust, and sustainable AI systems.

资源感知优化对于智能体的开发至关重要，使其能够在现实世界约束下高效运行。通过管理计算、时间和财务资源，智能体可以实现最佳性能和成本效益。动态模型切换、自适应工具使用和上下文修剪等技术对于实现这些效率至关重要。高级策略，包括学习资源分配策略和优雅降级，增强了智能体在不同条件下的适应性和弹性。将这些优化原则整合到智能体设计中，对于构建可扩展、健壮和可持续的AI系统至关重要。

References

参考文献

Google's Agent Development Kit (ADK): google.github.io/adk-docs/
Gemini Flash 2.5 & Gemini 2.5 Pro: aistudio.google.com/
OpenRouter: openrouter.ai/docs/quicks…