Building Enterprise AI Agents with Tencent ADP: RAG, Workflows and Operational Guardrails Training Course
Constructing Enterprise AI Agents with Tencent ADP: RAG, Workflows, and Operational Controls is a comprehensive curriculum focused on the design, development, and operation of enterprise-level AI agents utilizing Tencent ADP.
This instructor-led, live training session (available online or onsite) is designed for intermediate-level solution architects, AI engineers, developers, and technical product teams seeking to leverage Tencent ADP for the creation of enterprise AI agents featuring production-ready RAG, workflow automation, multi-agent coordination, and robust operational safeguards.
Upon completion of this training, participants will possess the capability to:
- Design AI agents within the Tencent ADP environment for specific enterprise applications.
- Construct RAG pipelines and knowledge workflows that enhance response accuracy and relevance.
- Orchestrate complex workflows and multi-agent interactions to support critical business processes.
- Implement guardrails, monitoring systems, and operational controls suitable for production environments.
Instructional Format
- Interactive lectures and facilitated discussions.
- Structured exercises and practical application activities.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For tailored training programs aligned with specific organizational needs, please contact our team to arrange details.
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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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