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Course Outline

Overview of Enterprise AI Agents with Tencent ADP

  • Defining enterprise AI agents and identifying where they deliver the most value.
  • Exploring Tencent ADP capabilities for agent development, knowledge integration, and workflow automation.
  • Distinguishing between sophisticated agent-based solutions and simple chat applications.
  • Reviewing common enterprise use cases and key delivery considerations.

Designing Agents for Business Processes

  • Establishing agent roles, boundaries, inputs, and expected outputs.
  • Selecting between single-agent and multi-agent architectural designs.
  • Structuring prompts, tools, and embedded business rules.
  • Planning for escalation paths, human review processes, and system reliability.

Developing RAG and Knowledge Workflows

  • Applying RAG concepts to ensure grounded answers and secure access to enterprise knowledge.
  • Preparing documents, policies, and internal content for effective retrieval.
  • Designing retrieval flows and patterns for response grounding.
  • Testing and iteratively improving answer quality over time.

Orchestrating Workflows and Integrations

  • Translating business processes into structured agent workflows.
  • Connecting agents to APIs, internal services, and broader enterprise systems.
  • Managing decision logic, approvals, retries, and fallback mechanisms.
  • Coordinating handoffs between workflow steps and specialized agents.

Implementing Operational Guardrails

  • Establishing guardrails for security, privacy, compliance, and policy enforcement.
  • Mitigating risks associated with unsafe outputs, prompt injection, and sensitive data exposure.
  • Incorporating approval checkpoints, audit trails, and strict access controls.
  • Designing safe response patterns for high-impact business scenarios.

Monitoring, Evaluation, and Continuous Improvement

  • Tracking key metrics such as quality, latency, cost, and workflow success rates.
  • Evaluating agent behavior across realistic business scenarios.
  • Diagnosing and resolving common issues in RAG, workflow, and orchestration layers.
  • Formulating a strategic implementation plan for pilot testing and production adoption.

Requirements

  • A solid grasp of generative AI principles and standard enterprise AI applications.
  • Professional experience interacting with APIs, web applications, or cloud-based platforms.
  • Familiarity with basic programming, system integration, or solution architecture practices.

Target Audience

  • Solution architects and technical leads.
  • AI engineers, application developers, and automation specialists.
  • Product managers and innovation teams driving enterprise AI initiatives.
 14 Hours

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