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 Duration 14 hours

Course Outline

Enterprise AI Agents with Tencent ADP

  • Definition and strategic value of enterprise-grade artificial intelligence agents
  • Tencent ADP features supporting agent construction, knowledge integration, and workflow automation
  • Distinctions between advanced agent architectures and standard conversational interfaces
  • Key organizational applications and considerations for deployment

Agent Architecture for Business Operations

  • Establishing functional roles, operational boundaries, input parameters, and output specifications
  • Evaluating the suitability of single-agent versus multi-agent configurations
  • Configuring prompts, tool interactions, and institutional business rules
  • Protocols for escalation, human oversight, and system reliability

Implementation of RAG and Knowledge Management

  • Principles of Retrieval-Augmented Generation (RAG) for accurate responses and secure knowledge access
  • Preparation of documents, regulations, and internal assets for retrieval systems
  • Architecting retrieval processes and response verification mechanisms
  • Ongoing assessment and refinement of output accuracy

Workflow Orchestration and System Integration

  • Translating business procedures into executable agent workflows
  • Integration of agents with APIs, internal services, and core enterprise systems
  • Management of decision logic, approval cycles, error handling, and contingency pathways
  • Coordination of transitions between workflow stages and specialized agent modules

Operational Safeguards and Compliance

  • Controls for data security, privacy, regulatory compliance, and policy enforcement
  • Mitigation of risks associated with inappropriate outputs, prompt injection attacks, and data leakage
  • Institution of approval gates, audit logging, and granular access permissions
  • Development of secure response protocols for high-consequence operational scenarios

Performance Monitoring and Continuous Optimization

  • Metric tracking for response quality, latency, resource expenditure, and process completion rates
  • Validation of agent behavior under simulated real-world business conditions
  • Diagnosis and resolution of common issues in RAG, workflow, and orchestration layers
  • Formulation of a strategy for pilot implementation and full-scale production adoption

Requirements

  • Foundational knowledge of generative AI concepts and common enterprise application scenarios
  • Practical experience with APIs, web applications, or cloud-based infrastructure
  • Basic proficiency in programming, system integration, or solution design

Target Audience

  • Solution architects and technical leads
  • AI engineers, application developers, and automation specialists
  • Product managers and innovation teams supporting enterprise AI initiatives

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