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Duration 14 hours
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.
