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Course Outline
Enterprise AI Agents with Tencent ADP
- An overview of enterprise AI agents and their value in enhancing operational efficiency and decision-making.
- The capabilities of Tencent ADP for developing, integrating knowledge into, and automating workflows for AI agents.
- Key distinctions between agent-based solutions and traditional chat applications.
- Common use cases for enterprise AI agents and considerations for successful deployment for government operations.
Designing Agents for Business Processes
- Defining the roles, boundaries, inputs, and outputs of AI agents to align with specific business processes.
- Evaluating single-agent versus multi-agent designs based on complexity and scale of operations.
- Structuring prompts, tools, and business rules to ensure effective agent performance.
- Planning for escalation procedures, human review processes, and system reliability to maintain high standards of service.
Building RAG and Knowledge Workflows
- Understanding RAG (Retrieval-Augmented Generation) concepts to ensure accurate and contextually relevant responses for government use.
- Preparing documents, policies, and internal content for efficient retrieval and integration into AI workflows.
- Designing retrieval flows and response grounding patterns to enhance the reliability of information provided by agents.
- Continuously testing and refining answer quality to meet the high standards required for government operations.
Orchestrating Workflows and Integrations
- Mapping business processes into agent workflows to streamline operations and improve efficiency.
- Connecting agents to APIs, internal services, and enterprise systems to facilitate seamless integration for government use.
- Managing decisions, approvals, retries, and fallback paths to ensure robust workflow management.
- Coordinating handoffs between workflow steps and specialist agents to maintain continuity and accuracy in operations.
Applying Operational Guardrails
- Implementing guardrails for security, privacy, compliance, and policy control to protect sensitive information for government use.
- Mitigating risks associated with unsafe output, prompt injection, and the exposure of sensitive data.
- Incorporating approval checkpoints, audit trails, and access controls to ensure accountability and transparency.
- Designing safe response patterns for high-impact business scenarios to safeguard against potential vulnerabilities.
Monitoring, Evaluation, and Continuous Improvement
- Tracking key metrics such as quality, latency, cost, and workflow success rates to ensure optimal performance of AI agents for government use.
- Conducting comprehensive tests of agent behavior across a range of realistic business scenarios to validate effectiveness.
- Identifying and resolving common issues related to RAG, workflow management, and orchestration to enhance system reliability.
- Developing an implementation plan for pilot and production adoption to facilitate smooth integration into existing government processes.
Requirements
- A comprehensive understanding of generative artificial intelligence (AI) concepts and common use cases in the enterprise environment
- Practical experience working with application programming interfaces (APIs), web applications, or cloud-based platforms
- Fundamental skills in programming, integration, or solution design
Audience
- Solution architects and technical leads for government projects
- AI engineers, application developers, and automation specialists supporting federal initiatives
- Product managers and innovation teams focused on advancing enterprise AI solutions
14 Hours
