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Course Outline

Enterprise AI Agents via Tencent ADP

  • Understanding the definition of enterprise AI agents and the value they deliver.
  • Leveraging Tencent ADP capabilities for agent development, knowledge integration, and workflow automation.
  • Distinguishing between advanced agent-based solutions and standard chat applications.
  • Exploring common enterprise use cases and key delivery considerations.

Architecting Agents for Business Processes

  • Defining agent roles, operational boundaries, inputs, and expected outputs.
  • Selecting between single-agent architectures and multi-agent designs.
  • Structuring prompts, integrating tools, and embedding business rules.
  • Planning for escalation protocols, human-in-the-loop review, 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 establishing response grounding patterns.
  • Testing and iteratively improving answer quality over time.

Orchestrating Workflows and System Integrations

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

Implementing Operational Guardrails

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

Monitoring, Evaluation, and Continuous Optimization

  • Tracking metrics such as quality, latency, cost, and workflow success rates.
  • Validating agent behavior through realistic business scenario testing.
  • Diagnosing and resolving common issues in RAG, workflows, and orchestration.
  • Formulating an implementation roadmap for pilot testing and full production adoption.

Requirements

  • A foundational grasp of generative AI principles and typical enterprise AI applications.
  • Practical experience interacting with APIs, web applications, or cloud infrastructure.
  • Basic proficiency in programming, system integration, or solution architecture.

Target Audience

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

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