Course Introduction
Advanced Agentic Data Engineering and Autonomous Analytics Training Course provides an in-depth exploration of how intelligent AI agents are reshaping data engineering, analytics automation, data operations, and enterprise decision-making. The program examines how autonomous and semi-autonomous systems can reason through complex data tasks, interact with tools and platforms, execute multi-step workflows, identify operational issues, and generate analytical outputs while operating within clearly defined governance and security boundaries.
The course focuses on the convergence of agentic artificial intelligence, modern data engineering, analytics engineering, machine learning, cloud data platforms, and business intelligence. Participants learn how agents can assist with data ingestion, transformation, pipeline development, quality validation, metadata management, orchestration, observability, SQL generation, analytical modeling, and insight discovery. Particular attention is given to building practical workflows that combine AI reasoning with reliable engineering practices and measurable operational outcomes.
Participants explore the architecture behind advanced agentic data engineering systems, including foundation models, tool calling, retrieval-augmented generation, vector databases, semantic layers, knowledge graphs, memory systems, orchestration frameworks, APIs, and data platforms. The course demonstrates how these components can work together to enable agents to plan tasks, interact with enterprise systems, interpret results, recover from failures, and coordinate multiple activities across the data lifecycle.
A significant emphasis is placed on autonomous analytics and the transformation of conventional analytical workflows. Participants examine how AI agents can translate business questions into governed queries, prepare datasets, perform exploratory analysis, detect anomalies, investigate root causes, develop analytical narratives, and support forecasting and scenario analysis. The program also considers how human analysts can collaborate with autonomous systems while retaining appropriate responsibility for interpretation, validation, and high-impact decisions.
The course addresses critical technical and organizational issues associated with autonomous data systems, including hallucinations, unreliable reasoning, prompt injection, unauthorized tool use, data privacy, security, access control, model evaluation, lineage, explainability, auditability, cost management, and regulatory compliance. Participants learn how to establish evaluation and monitoring frameworks that measure agent accuracy, task completion, reliability, latency, resource consumption, and business value while identifying situations where human intervention remains essential.
By completing Advanced Agentic Data Engineering and Autonomous Analytics Training Course, participants develop the knowledge required to design, evaluate, deploy, and govern sophisticated AI-enabled data engineering and analytics workflows. The program prepares professionals to move from isolated AI experimentation toward production-oriented solutions capable of improving engineering productivity, accelerating analytical delivery, strengthening data reliability, and supporting scalable, intelligent, and governed enterprise data operations.
Duration
10 days
Who Should Attend
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Data engineers responsible for designing, developing, maintaining, and optimizing enterprise data pipelines and platforms.
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Analytics engineers seeking to automate analytical transformation, testing, documentation, modeling, and delivery workflows.
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Data analysts interested in using autonomous AI agents for advanced analysis, investigation, reporting, and insight generation.
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Business intelligence professionals developing intelligent reporting, dashboards, decision-support systems, and conversational analytics solutions.
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Data architects designing scalable architectures that integrate AI agents with modern cloud and enterprise data environments.
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Machine learning engineers developing intelligent systems that interact with enterprise data, tools, applications, and analytical workflows.
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AI engineers responsible for building, integrating, evaluating, and deploying agentic AI systems for data-intensive business applications.
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Data platform engineers managing warehouses, data lakes, lakehouses, databases, APIs, orchestration platforms, and enterprise data infrastructure.
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Data scientists seeking to automate data preparation, experimentation, analytical investigations, model workflows, and recurring analytical processes.
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Data governance professionals overseeing metadata, lineage, quality, stewardship, policies, standards, and responsible use of enterprise data.
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Technology managers and digital transformation leaders evaluating autonomous AI opportunities across data engineering and analytics functions.
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Chief data, analytics, technology, and AI professionals developing strategies for responsible adoption and scaling of autonomous data capabilities.
Course Objectives
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Explain advanced agentic AI concepts and distinguish autonomous data engineering systems from traditional automation, generative AI, and conventional analytical applications.
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Design agentic data engineering architectures that combine foundation models, orchestration, tools, APIs, retrieval systems, memory, and enterprise data platforms.
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Apply AI agents to data ingestion, transformation, validation, testing, orchestration, documentation, monitoring, and troubleshooting across modern engineering environments.
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Develop autonomous workflows capable of decomposing complex engineering tasks, selecting appropriate tools, executing actions, validating results, and escalating exceptions.
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Apply retrieval-augmented generation, vector search, semantic retrieval, and knowledge graphs to provide agents with reliable and contextually relevant enterprise data information.
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Design autonomous analytics workflows for data preparation, exploratory analysis, anomaly detection, root-cause investigation, forecasting, visualization, and analytical storytelling.
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Integrate agentic systems with databases, cloud warehouses, data lakes, lakehouses, APIs, orchestration tools, business intelligence platforms, and enterprise applications.
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Implement robust approaches for evaluating agent accuracy, groundedness, reliability, task completion, latency, cost, safety, and operational performance in production environments.
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Identify and mitigate agent-specific risks including prompt injection, unauthorized tool execution, data leakage, hallucination, excessive autonomy, and inappropriate analytical actions.
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Establish governance frameworks covering access control, data lineage, auditability, human oversight, privacy, regulatory requirements, responsible AI, and autonomous decision boundaries.
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Evaluate emerging developments in autonomous analytics, self-healing data pipelines, multi-agent systems, AI-native data platforms, and intelligent analytics engineering.
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Develop practical implementation strategies that connect agentic AI investments with data quality, engineering productivity, analytical agility, operational efficiency, and measurable business outcomes.
Comprehensive Course Outline
Module 1: Foundations of Agentic Data Engineering
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Evolution of data engineering from scripted automation toward intelligent, goal-oriented, and autonomous agent workflows.
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Core capabilities of data agents, including reasoning, planning, memory, tool use, observation, execution, and result validation.
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Differences between generative AI assistants, workflow automation, autonomous agents, and human-supervised engineering systems.
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Business and technical opportunities for applying agentic AI across modern data engineering and analytics operating models.
Module 2: Architecture of Autonomous Data Systems
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Designing agentic architectures that connect foundation models with databases, APIs, orchestration tools, metadata systems, and analytical platforms.
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Planning mechanisms that allow agents to decompose complex data engineering objectives into executable and verifiable subtasks.
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Tool selection, function calling, permissions, context management, and controlled execution within enterprise data environments.
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Scalability, reliability, observability, interoperability, fault tolerance, and lifecycle considerations for production agentic systems.
Module 3: Intelligent Data Ingestion and Integration
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Using autonomous agents to identify appropriate data sources, understand schemas, and coordinate structured and unstructured data ingestion.
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Agent-assisted integration of APIs, databases, files, event streams, cloud applications, and heterogeneous enterprise data sources.
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Intelligent schema matching, field mapping, transformation recommendations, and automated handling of evolving source structures.
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Emerging approaches to adaptive ingestion pipelines that respond intelligently to changing sources, formats, volumes, and operational conditions.
Module 4: Autonomous Data Transformation and Pipeline Development
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Applying AI agents to generate, optimize, document, validate, and maintain SQL and data transformation workflows.
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Agent-assisted pipeline design covering dependencies, transformations, testing, scheduling, orchestration, and deployment requirements.
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Autonomous identification of inefficient transformations, redundant processing, bottlenecks, and opportunities for pipeline optimization.
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Emerging self-building and self-optimizing data pipelines that dynamically adapt to workload patterns and changing data requirements.
Module 5: Agentic Data Quality and Observability
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Using AI agents for automated profiling, validation, anomaly detection, quality rule generation, and continuous data quality assessment.
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Autonomous investigation of data incidents through lineage, pipeline dependencies, metadata, historical patterns, and operational signals.
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Designing intelligent remediation workflows that recommend or execute corrective actions under appropriate approval and governance controls.
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Emerging AI-driven data observability capabilities for proactive detection, diagnosis, prediction, and prevention of data reliability problems.
Module 6: Retrieval-Augmented Data Engineering
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Fundamentals of retrieval-augmented generation for grounding autonomous agents in trusted technical and organizational data sources.
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Designing vector, keyword, semantic, and hybrid retrieval architectures for engineering documentation and enterprise knowledge.
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Connecting agents to schemas, metadata, data dictionaries, lineage repositories, technical documentation, and governed knowledge sources.
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Advanced grounding strategies for improving factual accuracy, source attribution, context relevance, and reliability during autonomous execution.
Module 7: Semantic Layers, Knowledge Graphs, and Agent Memory
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Applying semantic models and knowledge graphs to represent relationships among data assets, business concepts, systems, metrics, and processes.
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Designing agent memory architectures that preserve relevant task context, historical interactions, operational knowledge, and analytical information.
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Combining vector retrieval, knowledge graphs, metadata repositories, and semantic layers to support sophisticated data reasoning.
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Emerging challenges involving memory persistence, knowledge freshness, provenance, conflicting information, context contamination, and controlled retention.
Module 8: Multi-Agent Data Engineering Workflows
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Designing specialized agents for ingestion, transformation, quality, governance, documentation, monitoring, analytics, and operational support.
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Coordination patterns for task delegation, collaboration, verification, supervision, negotiation, and result consolidation across multiple agents.
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Managing shared context, communication, permissions, dependencies, state management, and failure recovery within multi-agent environments.
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Emerging multi-agent architectures for distributed data engineering operations and coordinated enterprise analytics workflows.
Module 9: Autonomous Analytics and Intelligent Data Exploration
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Using AI agents to automate data preparation, exploratory analysis, statistical investigation, visualization, and analytical interpretation.
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Converting natural-language business questions into governed analytical workflows, database queries, calculations, and interpretable outputs.
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Autonomous anomaly investigation, trend identification, segmentation, correlation analysis, forecasting, and root-cause exploration.
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Emerging autonomous analytics capabilities and their implications for analysts, business intelligence teams, decision-support processes, and organizations.
Module 10: Agentic Analytics Engineering and Business Intelligence
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Applying AI agents to analytical models, metric definitions, semantic layers, documentation, testing, and business intelligence development workflows.
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Automating recurring reports, dashboard preparation, query generation, analytical documentation, and consistency validation across reporting environments.
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Using autonomous agents to investigate dashboard anomalies and explain significant changes in business and operational metrics.
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Emerging AI-native analytics engineering practices involving autonomous development, testing, optimization, documentation, and maintenance.
Module 11: Autonomous Data Governance and Security
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Applying intelligent agents to metadata management, lineage analysis, classification, stewardship, policy enforcement, and governance documentation.
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Implementing identity, authorization, least-privilege access, secure tool execution, secrets management, and controlled agent interactions.
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Addressing prompt injection, malicious instructions, data exfiltration, unauthorized actions, and other security threats affecting autonomous systems.
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Emerging governance models for autonomous data operations, including accountability structures, human oversight, policy enforcement, and regulatory adaptation.
Module 12: Testing, Evaluation, and Reliability Engineering
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Developing comprehensive evaluation frameworks for agent accuracy, task completion, groundedness, reasoning quality, reliability, and safety.
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Testing autonomous workflows against hallucinations, incorrect queries, tool failures, data anomalies, adversarial inputs, and unexpected scenarios.
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Implementing observability for agent decisions, tool calls, latency, token consumption, costs, errors, outcomes, and operational behavior.
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Emerging evaluation techniques for long-running agents, non-deterministic systems, adaptive workflows, continuous improvement, and production reliability.
Module 13: Cloud Data Platforms and Enterprise Integration
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Integrating autonomous agents with cloud warehouses, lakehouses, data lakes, databases, APIs, orchestration systems, and enterprise applications.
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Designing secure deployment patterns across cloud, hybrid, private, distributed, and multi-platform enterprise infrastructure environments.
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Managing model selection, computational resources, inference costs, latency, workload scaling, resilience, and service-level requirements.
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Emerging AI-native data platforms and intelligent infrastructure designed specifically to support autonomous engineering and analytics operations.
Module 14: Autonomous Analytics, Forecasting, and Decision Support
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Designing agents that combine governed data access, analytical methods, statistical reasoning, visualization, and business context for decision support.
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Applying autonomous workflows to forecasting, scenario modeling, sensitivity analysis, anomaly investigation, and operational performance assessment.
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Establishing validation and human-review mechanisms for analytical recommendations, predictions, and other consequential outputs.
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Emerging autonomous decision-support systems and evolving boundaries between human analysts, AI agents, dashboards, and intelligent applications.
Module 15: Emerging Trends and Critical Issues in Agentic Data
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Examining computer-use agents, autonomous coding systems, agent-to-agent communication, reasoning models, and increasingly capable data agents.
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Assessing emerging concerns involving controllability, autonomy, transparency, workforce transformation, regulatory requirements, and organizational accountability.
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Exploring self-healing pipelines, autonomous data products, adaptive data platforms, AI-native operations, and continuously optimizing analytical ecosystems.
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Evaluating technological uncertainty, adoption barriers, infrastructure demands, workforce skills, implementation risks, and future enterprise opportunities.
Module 16: Implementation Strategy and Capstone Applications
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Identifying high-value opportunities for autonomous data engineering and analytics based on business impact, feasibility, data readiness, and risk.
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Developing implementation roadmaps covering architecture, technology selection, governance, operating models, workforce capabilities, and change management.
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Designing practical capstone solutions that integrate autonomous agents with data engineering, analytics, governance, quality, and enterprise workflows.
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Establishing continuous improvement programs for monitoring outcomes, optimizing agents, managing risks, controlling costs, and scaling successful solutions.
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.
