The Concept of Agents and the Background of the Era
What Is an Agent?
An Agent(Intelligent Agent) refers to an intelligent system that can perceive the environment, perform autonomous reasoning and planning, call tools to execute actions, and continuously learn and optimize through feedback loops. Unlike traditional chatbots, modern agents possess stronger autonomy, tool-use capabilities, and multi-task collaboration abilities, and can independently complete complex open-ended tasks. 2025 is widely recognized by the industry as the "Year of AI Agent Applications," and agent development has entered an ecosystem stage, with technical barriers significantly lowered and application scenarios rapidly expanding.
The Technological Evolution of Agent Development
Agent technology has gone through four main stages of development:
- Symbolic Rule Agents: Early expert systems based on preset rules
- Statistical Learning Agents: Decision-making systems based on machine learning algorithms
- Deep Learning Agents: Using neural networks for perception and decision-making
- Large Model Agents: Centered on large language models (LLMs), with multimodal interaction and autonomous planning capabilities
Current mainstream agent architectures generally adopt the paradigm of "large model + tools + memory + planning," achieving a fundamental shift from "passive response" to "proactive service."
How Agents Work
Core Working Loop
Modern agents follow a continuous loop of "perception-thinking-action-feedback":
Perception → Reasoning → Planning → Action → Feedback
This loop is continuously iterative, and the agent optimizes subsequent decisions by continuously receiving environmental feedback.
Detailed Explanation of the Six Core Components
Perception Module (Perception)
The perception module is responsible for recognizing and processing information from multimodal environments, including text, images, audio, sensor data, etc. Its implementation methods include:
- Text Perception: Understanding user input through natural language processing
- Multimodal Perception: Integrating visual, auditory, and other sensory information
- Environmental Perception: Connecting to external system APIs to obtain real-time data
Memory System (Memory)
Memory is the key to an agent's continuous learning and knowledge retention and is usually divided into:
- Short-Term Memory: Storing the current conversation context and supporting multi-turn interaction
- Long-Term Memory: Preserving historical experience, user preferences, and domain knowledge
- Memory Stream : Dynamically organizing and retrieving information, supporting self-reflection
In terms of technical implementation, vector databases (such as Pinecone and Milvus) are commonly used to store and retrieve memory information.
Reasoning and Planning Engine (Reasoning & Planning)
The reasoning engine implements complex decision-making based on large language models:
- Task Decomposition: Breaking down complex goals into executable subtasks
- Chain-of-Thought : Simulating the human process of step-by-step thinking
- Dynamic Adjustment: Adjusting plans in real time based on new information
- Reflection Mechanism: Evaluating action results and iteratively optimizing
Tool Use Capabilities
Tool use enables agents to break through plain text limitations and interact with the external world:
- API Calls: Access real-time information such as weather, stocks, and databases
- Code Execution: Run Python scripts for data analysis
- Search Engines: Obtain the latest web information
- Professional Software: Call professional tools such as CAD and MATLAB
Tool use is typically implemented through the Function Calling mechanism, and the agent autonomously decides when and how to use tools.
Action Execution (Action)
The action module translates decisions into concrete operations:
- Asynchronous Execution: Support concurrent processing of multiple tasks
- Environment Interaction: Affect external systems through APIs or Robotic Process Automation (RPA)
- Multi-step Workflows: Execute complex business processes
Feedback Loop
The feedback loop is the core of an agent's continuous evolution:
- Immediate Feedback: Action results immediately affect the next decision
- Performance Monitoring: Track metrics such as task completion rate and response quality
- Continuous Learning: Fine-tune models based on feedback data
- Reinforcement Learning from Human Feedback (RLHF) : Optimize model behavior through human evaluation
Typical Architectural Patterns
Single-Agent Architecture
Suitable for single-task scenarios, such as customer service robots. All components are centralized in one system, with a simple structure but limited scalability.
Multi-Agent Collaboration Architecture
Complex systems adopt multi-agent collaboration patterns:
- Supervisor-Executor Pattern: The main agent is responsible for task allocation, while sub-agents are responsible for specific execution
- Peer-to-Peer Collaboration Pattern: Multiple agents collaborate as equals and coordinate through communication protocols
- Specialized Agents: Each agent focuses on a specific domain (such as data analysis, customer service, scheduling)
Layered Architecture
Modern agent platforms generally adopt a layered design:
- Application Layer: User interface and business logic
- Orchestration Layer: Coordinate agent behavior and memory management
- Model Layer: Call large language models for reasoning
- Tool Layer: Integrate various external APIs and tools
- Data Layer: Knowledge base and vector storage
Technology Stack Required for Agent Development
Foundational Technical Skills
Programming Languages and Development Environments
- Python: The preferred language for agent development, with a rich AI ecosystem of libraries
- JavaScript/TypeScript: Front-end interfaces and full-stack development
- Development Environments: Jupyter Lab, Google Colab, and Anaconda for prototyping
- Version Control: Git and GitHub for code management
Large Language Model Technology
- Prompt Engineering: Master prompt design techniques to guide model behavior
- Context Management: Effectively utilize the model's context window (typically 4K-128K tokens)
- Model Fine-Tuning: Use technologies such as LoRA and QLoRA for lightweight fine-tuning
- Model quantization: Reduce deployment costs through technologies such as GPTQ and AWQ
Data processing technologies
- Data cleaning: Handle noise, missing values, and duplicate data
- Data annotation: Build high-quality training datasets
- Vector embeddings: Use Embedding models to convert text into vectors
- Knowledge graphs: Build structured knowledge networks
Advanced technical capabilities
Multimodal processing
- Visual understanding: Integrate visual models such as CLIP and BLIP
- Speech interaction: Use speech technologies such as Whisper and VITS
- Cross-modal fusion: Achieve unified understanding of text-image-audio
Reinforcement learning and alignment
- RLHF technology: Reinforcement learning from human feedback for value alignment
- DPO（Direct Preference Optimization） : Direct preference optimization, with higher training efficiency
- PPO（Proximal Policy Optimization） : Proximal policy optimization algorithm
Distributed systems
- Message queues: RabbitMQ and Kafka enable asynchronous communication
- Service orchestration: Kubernetes manages containerized deployment
- Load balancing: Nginx and HAProxy handle high concurrency
Panorama of intelligent agent development tools and platforms
Open-source frameworks and tools(latest in 2025)
Enterprise-level development frameworks
- OpenAI Agents SDK: OpenAI's official open-source framework, supporting multi-agent workflow orchestration, task execution, and monitoring
- LangGraph: A graph-structured framework based on LangChain, suitable for building complex multi-step workflows
- CrewAI: Focuses on multi-agent collaboration, supporting role-playing and task allocation
- Microsoft AutoGen: Microsoft's open-source multi-Agent collaboration framework, which can connect to multiple large models and support custom Agent logic
- Semantic Kernel: Microsoft's lightweight SDK for integrating AI services into existing applications
Low-code/no-code platforms
- Dify: Provides a visual interface and full-process support, with a built-in RAG engine and model management, greatly lowering the development threshold
- n8n: Open-source workflow automation tool that supports AI node integration
- Rasa: Open-source dialogue management and language understanding tool, suitable for building chatbots
- Xatkit: Low-code chatbot development framework
Development auxiliary tools
- AI coding assistants：GitHub Copilot、Tabnine、Cursor.ai, Amazon Q Developer, CodeGPT, Gemini CLI, etc. improve development efficiency
- Model deployment：OllamaSupports local running of open-source large models
- MCP protocol: mcp-use provides open-source development tools to help quickly build and deploy custom AI agents
Cloud service platforms
Domestic platforms
- iFlytek Spark Intelligent Agent Development Platform: Free and open, providing a full-stack development engine and a closed-loop system of evaluation-tuning-monitoring-iteration
- Yuanzhiqi AI: Lightweight intelligent agent development platform, providing low-code + AI model marketplace, supporting multimodal interaction, ready to use in the cloud
- Ant Digital Technologies Agentar: Full-stack platform, supporting the integration of large models and industry knowledge bases, low-code development, and enterprise-level security protection
- Tencent Cloud Agent Development Platform: Provides standardized API interfaces, supports integration with existing enterprise systems, and ensures data security and compliance
- Alibaba Cloud Agent Platform: Suitable for local Chinese enterprises, providing a rich set of industry solutions
- ByteDanceCoze、Alibaba BailianPlatform, Tencent Yuanqi: Provides a no-code or one-stop platform for creating and distributing agents
International Platforms
- Azure AI Agent Studio: Microsoft's enterprise-grade agent development tool, deeply integrated with the Azure ecosystem
- AWS Bedrock Agents: Amazon's fully managed service, supporting rapid agent building and deployment
- IBM Watsonx Orchestrate: An enterprise-grade AI assistant orchestration platform, emphasizing security and scalability
Tool Selection Strategy
Small and Medium Enterprises/Individual Developers: It is recommended to start with low-code platforms such as Dify, n8n, or Coze to quickly validate ideas
Technical Teams: Frameworks such as LangGraph and CrewAI can be chosen for deep customization, balancing flexibility and development efficiency
Large Enterprises: Full-stack platforms such as Tencent Cloud, Alibaba Cloud, or Azure are recommended to meet data security, compliance, and scalability requirements
Detailed Explanation of the Agent Development Process
Requirements Analysis and Definition(1-2 weeks)
- Core Tasks：
- Clarify the agent's goals, functions, application scenarios, and core value
- Sort out business requirements and define performance metrics (response time, accuracy, concurrency)
- Identify constraints: security standards, compliance requirements, budget limitations
- Work closely with business experts to gain an in-depth understanding of business processes
- Deliverables：
- Requirements specification document
- User story map
- Technical feasibility analysis report
- Team Configuration: 1 product manager, 1 business analyst, 1 architect
Data Engineering and Knowledge Construction(2-4 weeks)
- Core Tasks：
- Collect and clean training data, and establish data quality standards
- Build a domain knowledge base and integrate structured and unstructured data
- Data annotation and preprocessing, and prepare fine-tuning datasets
- Establish mechanisms for data updates and maintenance
- Key Technologies：
- Use data quality assessment tools to monitor data completeness
- Adopt federated learning or differential privacy technologies to protect data privacy
- Deliverables：
- Dataset documentation
- Knowledge base architecture design
- Data pipeline
- Team Configuration: 2 data engineers, 1 domain expert
Model Development and Training Optimization(3-8 weeks)
- Core Tasks：
- Select a base large model (GPT-4, Claude, Llama, etc.)
- Design model fine-tuning strategies and adopt efficient fine-tuning techniques such as LoRA and QLoRA
- Implement RLHF or DPO for value alignment
- Model evaluation and tuning, and hyperparameter optimization
- Technical Practices：
- Use the trl open-source library for RLHF training
- Use quantization, pruning, and distillation techniques to compress models
- Rapid iteration in Jupyter Lab or Google Colab environments
- Deliverables：
- Fine-tuned model weights
- Model performance evaluation report
- Alignment verification results
- Team configuration: 2 AI engineers, 1 ML engineer
System integration and architecture design(2-4 weeks)
- Core tasks：
- Design a modular layered architecture and define communication protocols
- Integrate models with tool systems (API, database, RPA)
- Develop API interfaces and user interfaces
- Implement memory management and context retention mechanisms
- Architecture pattern selection：
- Single agent: suitable for simple scenarios
- Multi-agent collaboration: use CrewAI or AutoGen to build complex workflows
- Microservices architecture: improve system scalability and fault tolerance
- Deliverables：
- System architecture diagram
- API documentation
- Technical design document
- Team configuration: 2 backend engineers, 1 frontend engineer, 1 architect
Testing and verification(2-3 weeks)
- Core tasks：
- Unit testing, integration testing, and end-to-end testing
- User acceptance testing (UAT), collecting real user feedback
- Security testing: red team testing to identify vulnerabilities
- Performance testing: evaluate response time and concurrency capability
- Testing tools：
- Use DeepEval, Promptfoo, Ragas to evaluate AI agent quality
- Adopt the HarmBench framework for automated red team testing
- Agent-SafetyBench for safety benchmark evaluation
- Deliverables：
- Test report
- Security assessment report
- Performance benchmark test results
- Team configuration: 2 test engineers, 1 security engineer
Deployment and launch(1-2 weeks)
- Core tasks：
- Choose a deployment platform: public cloud, private deployment, or hybrid cloud
- Configure the production environment and set up monitoring and alerts
- Implement blue-green deployment or canary release to reduce launch risk
- Prepare a rollback plan
- Deployment options：
- Cloud deployment: AWS Bedrock, Azure AI Studio, Tencent Cloud and other platforms provide fully managed services
- Local deployment: Use Ollama to run open-source models on private servers
- Containerization: Docker + Kubernetes for elastic scaling
- Deliverables：
- Deployment configuration document
- Monitoring dashboard
- Launch checklist
- Team configuration: 1 operations engineer, 1 DevOps engineer
Operations and continuous optimization(ongoing)
- Core tasks：
- Establish a monitoring system to track operating status and error logs
- Collect user feedback and analyze usage data
- Regularly retrain models to adapt to data drift
- Implement A/B testing and continuously optimize performance
- Best Practices：
- Adopt the AgentOps philosophy to achieve full lifecycle management
- Establish a data quality monitoring mechanism
- Conduct regular red team testing to ensure continuous security
- Team Configuration: 1 operations engineer, 1 data analyst, 1 AI engineer (part-time)
Development cycles and milestones for agents of different complexity levels
Single-turn Q&A chatbot(simplest)
- Development Cycle: 3 days to 2 weeks
- Typical Milestones：
- Day 1: Requirements confirmation, choose a platform (such as Coze or Dify)
- Days 2-3: Upload the knowledge base, configure prompts
- Days 4-5: Testing and tuning
- Days 6-7: Launch and deployment
- Deliverables: A chatbot link that can be used for direct conversation, knowledge base documents
- Team Configuration: 1 product manager or business person is sufficient, no professional development team is needed
- Technical Characteristics: Zero-code development based on an existing platform, only supports single-turn Q&A, no context memory
Multi-turn conversational agent with tool calling(medium complexity)
- Development Cycle: 4-8 weeks
- Typical Milestones：
- Week 1: Requirements analysis and tool API research
- Weeks 2-3: Data preparation and knowledge base construction
- Weeks 4-5: Core dialogue logic development, integrated tool calling
- Week 6: Implementation of multi-turn dialogue management
- Week 7: Testing and performance optimization
- Week 8: Deployment and launch
- Key Development Content：
- Dialogue state management, maintain context information
- Tool registration and invocation framework (such as using Dify's tool nodes)
- Memory system implementation, support long-term dialogue
- Intent recognition and entity extraction
- Deliverables：
- A conversational intelligent agent
- API interface documentation
- Dialogue flow chart
- Test report
- Team Configuration: 1 product manager, 1 AI engineer, 1 backend engineer,3 people in total
- Technology Stack: Python/LangGraph, vector database, RESTful API
Cross-modal autonomous system(high complexity)
- Development Cycle: 3-6 months or longer
- Typical Milestones：
- Weeks 1-2: System architecture design, determine the multi-agent collaboration mode
- Weeks 3-4: Interface definition for each submodule
- Weeks 5-8: Perception module development (vision/speech processing)
- Weeks 9-12: Implementation of the planning engine and memory system
- Weeks 13-16: Tool ecosystem development, integration with business systems
- Weeks 17-20: Multi-agent collaboration mechanism development
- Weeks 21-24: Safety alignment and red team testing
- Weeks 25-26: Performance optimization and stress testing
- Weeks 27-30: Pilot deployment and user feedback collection
- Technical Challenges：
- Cross-modal fusion: unified processing of text, image, and audio inputs
- Autonomous planning: decomposing complex tasks into hundreds of subtasks
- Multi-agent coordination: resolving communication, conflict, and consistency issues
- Safety alignment: ensuring autonomous behavior conforms to human values
- Deliverables：
- Complete system platform
- Multi-agent collaboration framework
- Safety assessment report
- Performance benchmark test data
- User operation manual
- Team Configuration: 1 project manager; 1 architect; 2 AI engineers; 2 backend engineers; 1 frontend engineer; 1 test engineer; 1 operations engineer;9 people in total.
- Technology Stack: LangGraph/CrewAI, multimodal large models, Kubernetes, microservices architecture
Development Efficiency Comparison Analysis
Adopting modern development tools and pre-trained large models can significantly shorten the development cycle:
- Traditional methods: Developing complex solutions takes 9-12 months
- Large model-driven: The same complexity can be shortened to 2-4 months
- Agent collaboration: Using predefined patterns can shorten multi-agent module development time by 36%-57%
- No-code platforms: A simple chatbot can be completed in 3 minutes to 1 hour
Agent Application Scenarios and Industry Cases
Financial Industry Applications
Intelligent Customer Service System
Business Requirements: Handle massive customer inquiries, reduce manual customer service costs, and improve response efficiency
Technical Implementation：
- Large language models: GPT-4, Claude, or finance-specific models such as BloombergGPT
- Tool calling: Integrate bank core system APIs, knowledge base retrieval, and ticketing systems
- System architecture: Multi-Agent collaboration, with the main customer service Agent handling conversations and sub-Agents executing tasks such as queries and ticket creation
- Deployment method: Public cloud deployment, providing external services through an API gateway
Key Business Metrics：
- A certain city commercial bank: per-transaction cost reduced by 60%, complaint rate decreased by 45%
- A certain bank's "Lingxi" system: resolution rate increased to 85%, interception rate 98%
- General effects: 24/7 service, response time reduced from hours to seconds
Risk Control and Anti-Fraud
Business Requirements: Identify suspicious transactions in real time and reduce financial risk
Technical Implementation：
- Multimodal detection: Analyze transaction text, user behavior, and device fingerprints
- Large models: FinRobot open-source platform, integrating time-series forecasting models
- Agent Design: The monitoring Agent scans transactions in real time, the analysis Agent generates risk assessment reports, and the decision Agent executes interception
Key Business Metrics：
- Fraud detection rate increased by 25%
- Risk assessment response time reduced by 80%
- False positive rate reduced by 30%
Intelligent Investment Advisory
Business Requirements: Provide personalized financial advice for individual investors
Technical Implementation：
- Large Models: Financial forecasting models such as FinGPT-Forecaster
- Tool Integration: Connect to real-time market data, fund databases, and risk assessment models
- Architecture: The planning Agent formulates investment strategies, the execution Agent completes transactions, and the monitoring Agent tracks holdings
Key Business Metrics：
- Investment strategy accuracy increased by 15%
- Average client asset return increased by 8%
- Service costs reduced by 70%
Healthcare Industry Applications
Assisted Diagnosis System
Business Requirements: Improve diagnostic efficiency and accuracy, and reduce missed diagnoses and misdiagnoses
Technical Implementation：
- Multimodal Understanding: Analyze medical images, medical record texts, and test reports
- Large Models: Medical-specific large models (such as Google Med-PaLM)
- Knowledge Base: Integrate medical guidelines, drug databases, and case libraries
Key Business Metrics：
- Top-tier hospital AI diagnostic system: diagnostic efficiency increased by 40%
- Early diagnosis and treatment rate increased by 25%
- Average doctor diagnosis time shortened by 30 minutes
Customer Service Industry Applications
Omnichannel Customer Service Center
Business Requirements: Uniformly manage customer requests across multiple channels such as phone, email, and social media
Technical Implementation：
- Large Models: GPT-4 or Azure OpenAI Service
- Tool Integration: CRM systems, order systems, and logistics query APIs
- Multi-Agent Architecture: The routing Agent assigns requests, and specialized Agents handle specific issues
Key Business Metrics：
- Microsoft Dynamics 365: first-call resolution rate increased by 20%, errors reduced by 60%
- 60% of customer interactions are automatically completed by Agents
- Customer satisfaction increased by 35%, and repeat issues reduced by 50%
Main Challenges and Response Strategies in Agent Development
Technical Challenges
Data Privacy and Security
Challenge Description：
- Training data may contain sensitive information, posing a risk of leakage
- Agents may expose private data when interacting with external APIs
- Strict compliance requirements (GDPR, Data Security Law)
Response Strategies：
- Technical Level: Adopt federated learning, differential privacy, and secure multi-party computation
- Data Level: Implement data de-identification, encrypted storage, and access control
- Process Level: Establish a secure data development environment and record data usage logs
- Evaluation Level: Use tools such as Agent-SafetyBench to assess privacy risks
Model Alignment and Value Safety
Challenge Description：
- Models may produce harmful, biased, or unethical outputs
- The behavior of autonomous agents is difficult to fully predict and control
- The "alignment tax" leads to decreased model performance
Response Strategies：
- RLHF Training: Align values through reinforcement learning from human feedback
- Safety Alignment: Adopt the Safe RLHF framework to decouple helpfulness and harmlessness optimization objectives
- Multi-Layer Protection: Set up safety layers at the input, output, and decoding stages
- Adversarial Training: Use red team testing to continuously discover alignment gaps
Scalability and Performance
Challenge Description：
- Large models have high inference costs and face heavy real-time response pressure
- Multi-agent coordination increases system complexity
- Data drift causes model performance to degrade over time
Response Strategies：
- Model Optimization: Quantization, pruning, and knowledge distillation reduce computational costs
- Architecture Design: Adopt a microservices architecture to support horizontal scaling
- Caching Strategy: Use Redis to cache high-frequency query results
- Continuous Monitoring: Establish a performance monitoring system and regularly retrain models
Interpretability and Transparency
Challenge Description：
- The decision-making process of large models is opaque, making errors difficult to trace
- Users do not trust "black box" decisions
- Regulations require AI systems to be explainable
Response Strategies：
- Explainable AI Techniques: Use methods such as LIME and SHAP to explain model predictions
- Chain-of-Thought Visualization: Show the model's reasoning steps
- Logging: Record the agent's decision-making process in detail
- Human-in-the-Loop: Introduce human review for key decisions
Future Trends in Agent Development
Direction of Technological Evolution
Superagency
Future agents will have stronger autonomy and generality, and will be able to handle complex cross-domain tasks. Research directions include:
- Scalable Agent Alignment: Ensure that superhuman-level AI systems remain aligned with human values
- Superalignment Technology: The Superalignment project proposed by OpenAI to address the safety challenges of superintelligence
Deep Multimodal Integration
Expanding from text interaction to a full-sensory experience:
- Unified vision-language-action models
- Robotic agents (Physical AI)
- Agents in virtual reality
Agent Ecosystemization
Entering the ecosystem development stage in 2025:
- Standardized protocols (such as MCP) promote tool interoperability
- Agent marketplace: tradable professional agent skills
- Agent collaboration networks: autonomously team up to complete tasks
Industry Application Outlook
Enterprise-Level AI Transformation
Gartner predicts that by 2027, more than 50% of enterprises will deploy multi-agent systems:
- From a single agent to agent teams
- Agent-as-a-Service business model
- Enterprise-grade security and compliance become core competitiveness
Deep industry customization
Vertical industry solutions become mainstream:
- Financial agents: compliance review, intelligent investment advisory, anti-fraud
- Healthcare agents: assisted diagnosis, drug discovery, health management
- Manufacturing agents: supply chain optimization, quality control, predictive maintenance
Advice for developers
- Embrace low-code platforms: quickly validate ideas and reduce trial-and-error costs
- Master prompt engineering: this is the core skill for communicating with AI
- Focus on security: consider alignment and ethical issues from the start of design
- Participate in open source communities: contribute code and learn best practices
- Keep learning: technology iterates quickly, so maintain a habit of learning
Development advice for ordinary users
Zero-foundation beginner path
Step 1: Choose an entry-level platform
RecommendedCoze(ByteDance) oriFlytek Xingchen Agent Development PlatformThese platforms provide:
- Visual interface, no programming required
- Rich templates and plugins
- One-click publishing to channels such as WeChat and Feishu
Step 2: Clarify the application scenario
Start from personal needs, for example:
- Personal knowledge base assistant: upload your own documents to build a dedicated Q&A bot
- Travel planning assistant: integrate maps, weather, and hotel APIs
- Learning tutoring assistant: customize subject tutoring agents for children
Step 3: Follow the minimum viable principle
- Implement core functions first; do not pursue big and complete features
- Use the platform's built-in large models; no need to train your own
- Iterate gradually through user feedback
Frequently asked questions
Q1: How much budget is needed to develop an agent?
- Zero cost: use free platforms (Coze free version, iFlytek Xingchen) to develop simple applications
- Low cost: a monthly subscription fee of 20-100 USD for more powerful models and more invocation counts
- Enterprise-grade: customized according to needs, with annual costs ranging from tens of thousands to millions of yuan
Q2: Can I develop agents without programming experience?
Absolutely. By 2025, no-code platforms have become very mature, and ordinary users can create powerful agents by dragging and dropping components and configuring parameters.
Q3: How long is the general agent development cycle?
- Simple scenarios: 1 day to 1 week
- Medium complexity: 1-2 months
- Enterprise-grade applications: 3-6 months or longer
Q4: How can the security of agents be ensured?
- Use the platform's built-in security review features
- Do not input sensitive information into public large models
- Conduct regular security testing
- Set up manual review at key decision points
Quick start checklist
- Register an account: register on Coze, Dify, or iFlytek Xingchen platforms
- Choose a template: Select the most matching scenario from the template library provided by the platform
- Configure the knowledge base: Upload documents or connect data sources
- Design the conversation flow: Use visual tools to draw the conversation path
- Test and optimize: Converse with the agent, identify shortcomings, and improve them
- Publish and share: One-click publish and share with friends to use
- Collect feedback: Establish feedback channels and continuously iterate
Agent development has entered the era of "democratization." The technology ecosystem of 2025 enables ordinary users to create powerful AI applications. From simple Q&A bots to complex multi-agent collaboration systems, developers can choose suitable tools and paths according to their needs and capabilities. Although faced with challenges such as data privacy, model alignment, and security, these issues can all be effectively managed by adopting best practices, using mature frameworks, and continuous learning.
In the future, agents will become fundamental components of the digital world, as ubiquitous as today's apps. Starting to learn and practice now is the best time to seize this wave of technology. Whether you are a technical expert or an ordinary user, you can find your entry point and create value in this field full of opportunities.