Advanced Agentic AI for Data Management and Analytics Training Course

Course Introduction

Advanced Agentic AI for Data Management and Analytics Training Course provides a comprehensive exploration of how autonomous and semi-autonomous AI agents can transform modern data management, governance, engineering, analytics, and decision-making environments. Participants examine how agentic systems perceive data contexts, reason across complex workflows, use enterprise tools, execute multi-step tasks, and continuously improve analytical processes while operating within defined controls and policies.

The course focuses on the practical intersection of agentic artificial intelligence, data platforms, analytics, machine learning, automation, and enterprise data governance. Participants learn how AI agents can support data discovery, quality assessment, metadata management, integration, transformation, orchestration, anomaly detection, analytical modeling, and reporting. Emphasis is placed on designing reliable agent workflows that produce measurable improvements in productivity, accuracy, scalability, and operational efficiency.

A major component of the training addresses the architecture of advanced agentic data solutions, including foundation models, retrieval-augmented generation, tool calling, memory, planning, reasoning, multi-agent collaboration, APIs, vector databases, knowledge graphs, and enterprise data platforms. Participants explore how these technologies can be combined to create intelligent systems capable of coordinating complex data tasks while maintaining traceability, security, human oversight, and appropriate organizational controls.

The course also examines emerging issues surrounding autonomous AI in data environments, including hallucination management, agent reliability, data lineage, privacy, cybersecurity, model governance, responsible AI, bias, explainability, access control, and regulatory compliance. Rather than treating agentic AI as a standalone technology, the program positions it within the broader data management lifecycle and shows how organizations can responsibly integrate autonomous capabilities into existing analytical and operational ecosystems.

Through practical frameworks, use cases, architectural discussions, implementation strategies, and advanced analytical scenarios, participants develop the ability to identify suitable opportunities for agentic automation and distinguish them from workflows that require conventional software, human judgment, or hybrid approaches. The training emphasizes measurable business outcomes, enabling learners to connect AI-enabled data capabilities with improved decision quality, faster delivery, reduced manual effort, stronger governance, and more responsive organizational operations.

By completing Advanced Agentic AI for Data Management and Analytics Training Course, participants gain a structured understanding of how to design, deploy, govern, evaluate, and continuously optimize agentic AI solutions for enterprise data environments. The program is suitable for professionals seeking to move beyond basic generative AI experimentation toward scalable, governed, production-oriented agentic systems that can support sophisticated data management and analytics use cases.

Duration

10 days

Who Should Attend

• Data managers responsible for improving enterprise data quality, governance, accessibility, and operational efficiency.

• Data engineers seeking to incorporate intelligent agents into pipelines, integration workflows, orchestration, and data platforms.

• Data analysts who want to automate analytical preparation, exploration, interpretation, and recurring reporting activities.

• Business intelligence professionals developing AI-enabled dashboards, insights, decision-support workflows, and analytical services.

• Analytics managers responsible for scaling advanced analytics capabilities across business and operational functions.

• AI and machine learning professionals designing autonomous or semi-autonomous systems for enterprise data applications.

• Data architects responsible for designing secure, scalable, interoperable, and intelligent enterprise data ecosystems.

• Chief data officers and data leaders evaluating agentic AI opportunities, governance requirements, and organizational transformation strategies.

• IT managers and technology leaders overseeing AI adoption, data modernization, platform integration, and digital transformation programs.

• Data governance professionals responsible for metadata, lineage, stewardship, policies, standards, controls, and regulatory requirements.

• Risk, compliance, and audit professionals assessing the governance and control implications of autonomous AI-driven data processes.

• Business leaders seeking practical understanding of how agentic AI can improve data-driven decisions, productivity, and organizational performance.

Course Objectives

• Explain the core concepts, architectures, capabilities, and limitations of agentic AI and distinguish autonomous agents from conventional automation and generative AI applications.

• Design agentic AI architectures that combine foundation models, tools, APIs, retrieval systems, memory, planning mechanisms, and enterprise data platforms for practical business use cases.

• Apply agentic AI techniques to data discovery, profiling, classification, cataloguing, metadata generation, and other activities across the modern data management lifecycle.

• Develop approaches for using AI agents to identify, diagnose, prioritize, and remediate data quality issues while maintaining appropriate human review and governance controls.

• Evaluate how autonomous agents can support data engineering tasks including ingestion, transformation, pipeline monitoring, orchestration, testing, and operational troubleshooting.

• Apply retrieval-augmented generation, vector search, semantic retrieval, and knowledge graph techniques to improve the accuracy and contextual relevance of data-driven AI responses.

• Design multi-agent workflows in which specialized agents collaborate on complex data management and analytics tasks while maintaining clear roles, permissions, communication, and accountability.

• Implement responsible approaches to agentic AI governance covering security, privacy, access management, explainability, auditability, human oversight, model risk, and regulatory compliance.

• Develop methods for evaluating agent performance using appropriate metrics for accuracy, reliability, latency, cost, robustness, safety, task completion, and business impact.

• Identify emerging agentic AI trends and assess their implications for data architecture, analytics operating models, workforce capabilities, enterprise platforms, and organizational transformation.

• Create practical strategies for integrating agentic AI with existing databases, cloud platforms, data warehouses, data lakes, APIs, business intelligence environments, and enterprise applications.

• Formulate implementation roadmaps that prioritize high-value use cases, manage operational risks, establish governance controls, and scale successful agentic AI initiatives sustainably.

Comprehensive Course Outline

Module 1: Foundations of Advanced Agentic AI for Data Management

• Evolution from traditional automation and generative AI toward autonomous, goal-oriented agentic data systems.

• Core components of intelligent agents, including perception, reasoning, planning, action, memory, feedback, and tool utilization.

• Differences between single-agent, multi-agent, workflow-based, and human-in-the-loop architectures for enterprise data operations.

• Business value, limitations, adoption considerations, and organizational implications of agentic AI in modern data environments.

Module 2: Agentic AI Architecture and System Design

• Designing agentic architectures that integrate foundation models, orchestration layers, enterprise tools, and governed data services.

• Planning and reasoning patterns for enabling agents to execute complex multi-step data management and analytics workflows.

• Tool calling, function execution, API integration, and controlled access mechanisms for connecting agents with enterprise systems.

• Architectural considerations for scalability, resilience, observability, interoperability, performance, maintainability, and operational control.

Module 3: Intelligent Data Discovery and Metadata Management

• Using AI agents to discover structured and unstructured data assets across distributed enterprise environments and repositories.

• Automated metadata extraction, enrichment, classification, tagging, business glossary alignment, and semantic documentation processes.

• Agent-supported data cataloguing and intelligent search across databases, warehouses, lakes, documents, APIs, and analytical assets.

• Emerging approaches to active metadata management and continuously updated data knowledge layers driven by intelligent agents.

Module 4: Agentic Data Quality and Observability

• Applying autonomous agents to data profiling, validation, anomaly identification, rule generation, and continuous quality monitoring.

• Intelligent root-cause analysis for detecting upstream causes of missing, inconsistent, duplicated, outdated, or inaccurate data.

• Designing agent-assisted remediation workflows with escalation rules, approval checkpoints, audit trails, and human intervention.

• Emerging data observability practices combining AI agents, quality signals, lineage information, operational telemetry, and contextual reasoning.

Module 5: Agentic Data Engineering and Pipeline Automation

• Applying intelligent agents to data ingestion, transformation, validation, scheduling, dependency management, and pipeline orchestration.

• AI-assisted generation and optimization of SQL, transformation logic, data tests, documentation, and engineering workflow components.

• Autonomous pipeline monitoring that detects failures, investigates dependencies, proposes corrective actions, and communicates operational impacts.

• Future directions for self-healing data pipelines and adaptive engineering systems that continuously respond to changing data conditions.

Module 6: Retrieval-Augmented Generation for Enterprise Data

• Principles of retrieval-augmented generation and its role in improving factual grounding for enterprise data and analytics agents.

• Designing retrieval pipelines using embeddings, vector databases, metadata filters, hybrid search, and semantic relevance techniques.

• Connecting agents to authoritative enterprise sources while controlling access, freshness, provenance, context quality, and retrieval scope.

• Advanced grounding strategies for reducing hallucinations and improving reliability when agents interact with complex organizational data.

Module 7: Knowledge Graphs, Semantic Layers, and Agent Memory

• Integrating knowledge graphs and semantic models to provide agents with relationships, entities, business concepts, and contextual information.

• Designing short-term, long-term, episodic, semantic, and operational memory mechanisms for sophisticated agentic workflows.

• Combining knowledge graphs, vector retrieval, metadata repositories, and semantic layers to strengthen enterprise data reasoning.

• Emerging research and implementation issues surrounding persistent agent memory, knowledge freshness, conflicts, provenance, and controlled forgetting.

Module 8: Multi-Agent Systems for Data and Analytics

• Designing specialized agent teams for data engineering, quality management, governance, analysis, visualization, and business interpretation.

• Agent coordination patterns including delegation, collaboration, negotiation, supervision, task decomposition, and result verification.

• Managing communication protocols, shared context, permissions, dependencies, and failure handling across multi-agent environments.

• Emerging multi-agent architectures and enterprise use cases for distributed analytical reasoning and coordinated data operations.

Module 9: Agentic Analytics and Intelligent Decision Support

• Using AI agents to automate data preparation, exploratory analysis, statistical investigation, insight generation, and analytical storytelling.

• Building conversational analytical workflows that translate natural-language questions into governed queries and interpretable results.

• Agent-supported forecasting, scenario analysis, anomaly investigation, root-cause analysis, and decision-support processes for business users.

• Emerging autonomous analytics capabilities and the changing relationship between human analysts, AI agents, dashboards, and decision systems.

Module 10: Agentic AI for Data Governance and Stewardship

• Applying intelligent agents to policy interpretation, data stewardship, business rule enforcement, and governance workflow automation.

• Automating lineage analysis, ownership identification, policy mapping, classification, retention assessment, and governance documentation.

• Designing human oversight structures that preserve accountability when AI agents perform sensitive governance and data management activities.

• Emerging governance challenges involving autonomous decision-making, policy conflicts, regulatory changes, accountability, and organizational control.

Module 11: Security, Privacy, and Responsible Agentic AI

• Protecting enterprise data and AI agents through identity management, authorization, least-privilege access, encryption, and secure tool execution.

• Addressing prompt injection, data exfiltration, malicious tool use, insecure retrieval, unauthorized actions, and other agent-specific security threats.

• Implementing privacy-aware architectures for sensitive data while supporting legitimate analytical access, processing, and organizational requirements.

• Responsible AI principles covering fairness, transparency, explainability, accountability, human oversight, safety, and responsible autonomous action.

Module 12: Agent Evaluation, Testing, and Reliability Engineering

• Developing evaluation frameworks for measuring agent accuracy, task completion, reasoning quality, groundedness, reliability, and business usefulness.

• Testing autonomous workflows for hallucinations, unexpected actions, tool failures, data errors, adversarial inputs, and boundary-condition scenarios.

• Establishing observability practices for monitoring agent decisions, tool calls, latency, costs, errors, outcomes, and operational behavior.

• Emerging evaluation approaches for long-horizon agents, non-deterministic systems, continuous learning, adaptive behavior, and production reliability.

Module 13: Data Platforms, Cloud Integration, and Enterprise Deployment

• Integrating agentic AI with cloud data warehouses, data lakes, lakehouses, databases, APIs, business intelligence platforms, and enterprise applications.

• Deployment patterns for secure agent services across cloud, hybrid, private, and distributed enterprise infrastructure environments.

• Managing scalability, computational costs, model selection, latency, workload scheduling, resilience, and operational service requirements.

• Emerging trends in agent-ready data platforms, AI-native data infrastructure, unified data environments, and intelligent cloud services.

Module 14: Advanced Agentic AI for Analytics Engineering

• Using agents to generate, optimize, test, document, and maintain analytical models and transformation workflows at enterprise scale.

• Automating metric definitions, semantic models, analytical documentation, query optimization, and consistency checks across reporting environments.

• Applying agentic workflows to recurring reports, dashboard development, analytical investigations, and business intelligence maintenance.

• Emerging analytics engineering practices shaped by autonomous coding, intelligent testing, semantic reasoning, and continuously adapting analytical systems.

Module 15: Emerging Issues, Trends, and Future Directions

• Agentic AI trends including computer-use agents, autonomous workflows, agent-to-agent communication, and increasingly capable reasoning systems.

• Emerging challenges involving model autonomy, controllability, alignment, regulatory oversight, workforce transformation, and organizational accountability.

• Future developments in agentic data ecosystems, including self-optimizing platforms, autonomous governance, intelligent data products, and AI-native operations.

• Strategic assessment of technological uncertainty, adoption barriers, infrastructure requirements, workforce skills, and long-term enterprise implications.

Module 16: Implementation Strategy, Use Cases, and Capstone Planning

• Identifying high-value agentic AI opportunities and prioritizing use cases according to business value, feasibility, risk, and data readiness.

• Developing implementation roadmaps covering architecture, technology selection, governance, operating models, change management, and measurable outcomes.

• Designing practical capstone solutions that integrate agentic AI with data management, analytics, governance, and enterprise workflow requirements.

• Establishing continuous improvement frameworks for scaling successful agents, measuring impact, managing risks, and adapting to emerging technologies

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.