In-depth analysis of OpenAI and Google's white papers and the two routes behind them | Big model research

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In 2025, AI Agent has become one of the most popular focuses in the field of artificial intelligence. It has attracted much attention and controversy! What is an intelligent agent? From product form to technology development to ecological construction, there are big questions.

As two giants in the field of AI, OpenAI and Google have successively released white papers on AI Agents, OpenAI's "A practical guide to building agents" and Google's "Agents" white paper, which elaborate on the definition, construction methods and development prospects of intelligent agents from their respective perspectives. These two documents not only provide a technical blueprint, but also represent the strategic thinking of industry giants on the future development direction of AI.

This article will systematically analyze and compare these two white papers, restore the essence of AI Agent's technical principles, product forms, and service methods, and provide readers with a comprehensive and in-depth understanding framework.

▍Concept : What is AI Agent ?

Definition of OpenAI

According to OpenAI's white paper, "Agents are systems that independently accomplish tasks on your behalf."

Specifically, OpenAI believes that an agent uses a large language model to manage workflow execution, make decisions, is able to recognize when a task is completed, correct its actions when necessary, and is equipped with various tools to access external systems to obtain context and take action - all within the scope of clearly defined instructions and guardrails.

Definition of Google

In its white paper, Google defines an AI Agent as: "an application that tries to achieve a goal by observing the world and taking actions using the tools at its disposal."

Google emphasizes the autonomy of agents, that is, their ability to act independently of human intervention, especially when they are given appropriate goals or task objectives. In Google's definition, Agent uses a generative AI model as its core decision maker and combines external tools to achieve a cycle of observation, reasoning, decision-making, and action.

Similarities and differences in definitions

Common points:

1. Autonomy: Both definitions emphasize that an agent can complete tasks independently without continuous human intervention

2. LLM-based: Both use large language models as the core reasoning engine

3. Tool usage: Both emphasize using external tools to expand capabilities

4. Goal-oriented: Focus on completing specific goals or tasks

Differences:

1. Scope: OpenAI is more focused on workflow automation, while Google’s definition is broader

2. Decision-making emphasis: OpenAI emphasizes clear instructions and guardrails, while Google emphasizes goal-driven

3. Architecture description: OpenAI uses the "model-tools-instructions" architecture, and Google proposes the "model-tools-orchestration layer" architecture, one focuses on model-driven tools and the other focuses on the capability orchestration layer.

▍Core Architecture: Elements of Agent

The components of OpenAI

OpenAI states in its white paper that an agent in its most basic form consists of three core components:

1. Model : LLM that drives the agent’s reasoning and decision making

2. Tools : External functions or APIs that the agent can use to take actions

3. Instructions : Define explicit guidelines and guardrails for the agent’s behavior

OpenAI emphasizes that as the complexity of tasks increases, the number and diversity of tools will also increase, enabling agents to access various information sources and perform different operations. At the same time, clear instructions are essential to ensure that agents work as expected, reducing ambiguity and improving decision quality.

Google's components

Google describes three core architectural components of an agent in its white paper:

1. Model : Generative language model as the core decision engine

2. Tools : including Extensions, Functions, and Data Stores

3. Orchestration Layer : The cognitive architecture that manages the observation, reasoning, decision, and action execution loop

Google particularly emphasizes the importance of the coordination layer, which guides the agent's reasoning process through frameworks such as ReAct (Reasoning and Acting), Chain-of-Thought, or Tree-of-Thoughts.

Differences and similarities in technical routes

Similarities:

1. Core engine: LLM is used as the brain and decision-making center of the intelligent agent

2. Tool integration: Both emphasize expanding the capabilities of LLM through tools

3. Interactive loop: Both use the basic process of observation-thinking-action

Differences:

1. Architecture focus: OpenAI focuses more on command and guardrail design, while Google focuses more on coordination layer design.

2. Tool classification: Google has made more detailed classifications of tools (Extensions, Functions, Data Stores)

3. Reasoning technology: Google discussed in more detail the application of various reasoning technologies such as ReAct and Chain-of-Thought

4. Implementation path: OpenAI provides an implementation based on its Agents SDK, while Google demonstrates implementations based on LangChain and Vertex AI. There is not much information about ADK.

▍Design principles: How to build an effective agent

OpenAI's design principles

OpenAI recommends the following design principles:

1. Start with a single powerful agent : Build a comprehensive agent that combines a powerful LLM, well-defined tools, and clear instructions

2. Adopt an orchestration model that supports complex workflows : supporting both single-agent loops and multi-agent architectures

3. Incremental development : Start with a high-performance model and then use smaller models to improve efficiency as needed

4. Provide clear, step-by-step instructions : Reduce ambiguity and increase predictability

5. Implementing multi-layer guardrails : ensuring security, data privacy and compliance with operational guidelines

6. Maintain modularity and flexibility : allow tasks to be distributed to multiple agents as complexity increases

OpenAI particularly emphasizes the importance of incremental development: first use the most powerful model to establish a performance baseline, and then consider introducing smaller models to optimize cost and latency.

Google's design principles

Google recommends building agents with the following characteristics:

1. Cognitive architecture integration : Building a cognitive architecture that includes internal reasoning, planning, and decision-making components

2. Dynamic tool selection : Using examples in the configuration, the agent can dynamically select and invoke the tool that is best suited for a specific task

3. Iterative development : Emphasis on continuous testing and improvement of agent performance

4. Goal Learning : Enhancing Agent Capabilities with Contextual Learning and Retrieval-Based Methods

5. Specialized Agent Integration : Using the "expert agent hybrid" approach, combining specially optimized agents, a bit like CrewAI

6. Reasoning framework application : Use frameworks such as ReAct and Chain-of-Thought to guide the reasoning process

Google particularly emphasizes the importance of specialized agents, believing that higher levels of performance can be achieved by combining agents that excel in specific fields or tasks.

Differences in implementation methods

Features of OpenAI :

• More emphasis on guardrail and safety design

• Provides clear code examples based on the OpenAI Agents SDK

• More emphasis on maximizing the capabilities of a single agent

Features of Google:

• More emphasis on cognitive architecture and reasoning technology

• Provides implementation examples based on LangChain and Vertex AI

• Prefer collaboration among specialized agents

These differences reflect the two companies' different technology paths and business strategies, but both aim to achieve more powerful and reliable AI agents.

▍Multi -agent systems: solutions to complex tasks

OpenAI's perspective on multi-agent systems

OpenAI describes two multi-agent modes in detail:

1. Manager Pattern : A central agent delegates tasks to specialized agents through tool calls

2. Decentralized Pattern : Agents operate as peers, handing off tasks to each other

OpenAI recommends that a multi-agent approach be used only when necessary, as it increases complexity, and only when the task is truly complex enough to exceed the capabilities of a single agent.

Google's perspective on multi-agent systems

Google proposed a "hybrid of expert agents" approach, which combines multiple specialized agents, each of which excels in a specific field or task, to provide excellent results in a variety of industries and problem areas.

Google predicts that as tools become more sophisticated and their reasoning capabilities increase, agents will be able to solve increasingly complex problems. In addition, the strategy of "agent chaining," which involves the collaboration of multiple specialized agents, will continue to gain momentum.

The Development Trend of Multi-Agent Systems

Combining the views of the two companies, we can see that multi-agent systems are moving in the following directions:

1. Diversified collaboration models : From centralization to decentralization, various collaboration models coexist

2. The rise of specialized agents : specialized agents for specific fields and tasks will become a trend

3. Intelligent agent market ecology : It is possible to form a market for professional intelligent agents to support applications in different fields

4. Evolution of orchestration mechanisms : The coordination and decision-making mechanisms of multi-agent systems will continue to be optimized

5. Enhanced human-machine collaboration : Multi-agent systems will better integrate into human workflows

▍Application scenarios : Real-life applications of AI Agent

Enterprise-level application cases

The application of AI Agent in enterprise environment has shown great potential:

1. Customer Service : Unit21 has implemented an AI-driven 24/7 customer support system to help customers understand product features, troubleshoot issues, and manage risks.

2. Legal Contract Processing : Cognizant built an AI agent using Vertex AI and Gemini to help legal teams draft contracts, assign risk scores, and provide recommendations

3. Sales enablement : Companies are deploying agents to analyze customer interactions, predict needs, and automatically generate personalized sales recommendations

4. Data analytics : Financial institutions use agents to analyze complex data sets, identify patterns, and generate insights

Personal Assistant Application Cases

In the field of personal productivity, AI Agents are changing the user experience:

1. Schedule management : Agents can automatically schedule meetings, set reminders, and handle calendar conflicts

2. Information filtering : Help users filter out important content from massive amounts of information and provide personalized summaries

3. Personal learning : Provide users with customized learning plans and resource recommendations

4. Health management : monitor health indicators and provide diet and exercise advice

Vertical field application cases

In specific vertical industries, AI Agent also demonstrates powerful capabilities:

1. Healthcare : AI Agent assists doctors in diagnosis and treatment decision-making by accessing medical knowledge bases, patient records, and the latest research

2. Real estate : The intelligent agent can understand natural language and provide personalized house recommendations, house viewing appointments, contract signing and other services

3. Education : Agents act as personalized learning assistants, adjusting teaching content based on students’ learning styles and progress

4. Financial services : providing professional support in investment analysis, risk assessment and asset management

▍Development Trends: The Future of Agent

OpenAI and Google's predictions for the future of agents

OpenAI ’s prediction:

OpenAI predicts that AI agents will revolutionize workflow automation, enabling systems to handle ambiguous and multi-step tasks. As development continues, agents will manage increasingly complex workflows and ensure safety and predictability, ultimately playing a central role in the next era of automation.

Google’s prediction:

Google believes that as the complexity of tools increases and reasoning capabilities increase, agents will be able to handle more complex and diverse challenges. The integration of enhanced cognitive architectures, target learning methods, and agent linking concepts will drive the development of agents and form powerful autonomous systems that can provide substantial practical value in various industries.

Market size forecast and industry chain analysis

According to data from multiple research institutions:

1. Market size : Rootanalysis predicts that the global AI Agent market size will grow from US$5.29 billion in 2024 to US$216.8 billion in 2035, with a compound annual growth rate of 40.15% during the forecast period 2024-2035.

2. Enterprise adoption rate : McKinsey research shows that more than 70% of corporate CEOs believe that AI Agents will significantly change their business models and competitive landscape in the next three years.

3. Industry chain structure :

1. Upstream: Infrastructure and technology providers (including intelligent computing center construction and large model development)

2. Midstream: AI Agent R&D and Integrator

3. Downstream: application manufacturers, end users

Technical challenges and breakthrough directions

Looking to the future, the development of AI Agents still faces many challenges and breakthrough directions:

1. Multimodal capabilities : As AI agents continue to have a growing impact on specific industries and large models evolve towards multimodality, multimodal AI agents will become an important form of intelligent product in 2025.

2. Multi-agent system : AI Agent deployment will change from "single" to "multiple", from a single intelligent entity to a "group collaboration" mode, and more Multi-agent modes will emerge.

3. Safety and transparency : Build trustworthy and safe intelligent agent systems and ensure their behaviors are transparent and explainable.

4. Long-term planning : Enhance the ability of agents to make long-term plans and decisions, rather than just solving short-term problems.

5. Hardware form : In the future, new hardware products equipped with personal basic intelligent bodies may appear, driving the application iteration of personal basic intelligent body technology.

▍Conclusion : Return to the essence of thinking

The Nature of Agent

Stripping away the technical details, the essence of AI Agent is to automate decision-making and actions. It continues the tradition of humans using tools to expand their capabilities, but this time the tools themselves have a certain degree of autonomous decision-making capabilities. Whether it is OpenAI's workflow-centric model or Google's goal-oriented architecture, they are trying to solve the same core problem:

• How to enable computer systems to better understand human intentions and take autonomous actions to achieve those intentions.

The impact of agents on human work and life

The widespread application of AI Agents will reshape our work and lifestyles:

1. Work transformation : repetitive work will be automated, and humans will focus more on creative and strategic work

2. Improved efficiency : Agent can handle multi-step complex tasks, saving a lot of time and energy

3. Capability expansion : Agents can be used as an extension of human capabilities, allowing ordinary people to gain expert-level capabilities

4. Changes in interaction methods : Human-computer interaction will become more natural and seamless, and Agent may become the main interface of the digital world

Rationally look at the development of Agent

Although AI Agents have great potential, we still need to be aware of their limitations:

1. Technical boundaries : Current agents are still limited by the underlying model capabilities and external tool sets

2. Security risks : The increased autonomy of agents also brings new security risks and ethical challenges

3. Over- reliance on agents may weaken some of the core capabilities of humans.

4. Expectation management : avoid exaggerating the agent's capabilities and establish reasonable expectations

Both OpenAI and Google's white papers present a common vision: AI agents will become a powerful assistant to human intelligence, not a substitute. Truly successful agents will be those systems that can seamlessly integrate into human workflows while respecting human dominance.

As technology continues to advance, AI Agents will continue to evolve, but their value should always be measured by enhancing human capabilities, liberating human creativity, and improving the quality of life. 2025 is just the beginning of the development of AI Agents, and there will be broader prospects waiting for us to explore in the future.