Course Introduction
Agentic Data Streaming and Real-Time Intelligence Training Course provides a practical framework for combining continuous data streams with intelligent, goal-directed AI agents capable of interpreting events, reasoning over changing information, and initiating appropriate actions. The course examines how streaming architectures, real-time analytics, machine learning, and agentic AI can work together to create responsive decision-support and automation systems that continuously sense operational conditions and adapt their behavior.
Participants explore the architecture of agentic streaming systems, including event producers, message brokers, stream processors, real-time data stores, retrieval systems, AI agents, tools, policies, workflows, and human approval mechanisms. The training explains how agents can consume live events, maintain relevant context, evaluate conditions against objectives, invoke analytical tools, coordinate with other services, and produce or execute actions while operating within defined constraints and governance controls.
The course develops practical capabilities for processing and interpreting high-velocity event data in real time. Participants examine event-time processing, stream enrichment, windowing, state management, event correlation, anomaly detection, contextual retrieval, and continuous feature generation. They learn how to combine historical information with current events so that intelligent systems can distinguish isolated signals from meaningful patterns and make more context-aware decisions as operational conditions change.
A central component of the programme focuses on agentic reasoning and real-time decision workflows. Participants learn how to design agents that monitor events, formulate analytical questions, retrieve relevant information, select tools, evaluate results, and initiate predefined workflows. The course emphasizes bounded autonomy, ensuring that agents operate within explicit policies, permissions, escalation rules, confidence thresholds, and human-in-the-loop processes rather than treating autonomous action as an unrestricted capability.
The training incorporates emerging developments in large language models, multimodal AI, real-time retrieval-augmented generation, model context protocols, event-driven agents, multi-agent coordination, and intelligent workflow orchestration. Participants examine how these technologies can support incident response, fraud monitoring, customer intelligence, predictive maintenance, cybersecurity operations, supply chain visibility, and dynamic business processes. Attention is also given to hallucination, latency, model drift, prompt injection, data poisoning, privacy, security, explainability, and operational reliability.
By the end of the course, participants will be able to understand, design, and evaluate agentic systems that operate over continuously changing data. They will gain practical knowledge of connecting streaming platforms with intelligent agents, analytical models, tools, knowledge stores, and business workflows. The programme emphasizes production readiness, observability, evaluation, governance, responsible automation, and measurable outcomes so that real-time intelligence can move from experimental prototypes into dependable organizational applications.
Duration
5 days
Who Should Attend
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Data engineers responsible for building real-time streaming platforms, event pipelines, and continuously operating data architectures.
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Data scientists developing real-time machine learning, anomaly detection, predictive analytics, and intelligent decision-support solutions.
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AI and machine learning engineers building agentic systems that interact with live data, tools, applications, and operational workflows.
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Software engineers developing event-driven applications, distributed services, AI agents, APIs, and real-time automation systems.
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Enterprise and solution architects designing integrated streaming, AI, cloud, event-driven, and intelligent automation architectures.
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Analytics engineers creating real-time data products, streaming transformations, contextual datasets, and analytical features for intelligent systems.
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Technology managers evaluating the organizational applications, architecture, governance, and operational requirements of agentic AI.
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Operations leaders seeking real-time intelligence for incident management, process monitoring, resource allocation, service delivery, and operational automation.
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IoT and industrial analytics professionals working with sensor streams, connected equipment, telemetry, predictive maintenance, and edge intelligence.
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Financial and risk professionals applying real-time intelligence to transaction monitoring, fraud detection, risk signals, and automated investigation workflows.
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Digital product leaders interested in intelligent systems that continuously interpret customer behavior, application events, and operational signals.
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Cloud, DevOps, and platform professionals supporting scalable, observable, secure, and resilient AI and streaming infrastructure.
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Data platform administrators responsible for streaming systems, access controls, data security, reliability, monitoring, and platform governance.
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Researchers and analysts working with high-velocity data and emerging AI methods for continuous monitoring, experimentation, and decision support.
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Managers and executives seeking to understand how agentic data systems can transform real-time intelligence, automation, and organizational decision processes.
Course Objectives
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Develop a comprehensive understanding of agentic data streaming and how real-time event processing can provide continuously updated context for intelligent AI-driven decisions.
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Design conceptual architectures that connect event producers, streaming platforms, processing engines, real-time stores, AI agents, tools, workflows, and human oversight mechanisms.
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Apply event-driven processing techniques including filtering, enrichment, aggregation, windowing, event correlation, state management, and contextual data preparation.
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Understand how AI agents can monitor continuous events, reason over current and historical information, select analytical tools, and initiate bounded actions within defined operational policies.
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Integrate large language models, retrieval systems, structured data, APIs, analytical tools, and streaming information to create context-aware real-time intelligence workflows.
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Design real-time agentic workflows with appropriate autonomy boundaries, confidence thresholds, permissions, escalation mechanisms, human approvals, and failure-handling procedures.
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Apply machine learning and AI techniques to streaming use cases such as anomaly detection, predictive maintenance, fraud monitoring, customer intelligence, and operational risk analysis.
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Evaluate the performance and reliability of agentic streaming systems using latency, accuracy, relevance, action quality, tool-use success, operational resilience, and business outcome measures.
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Address security, privacy, governance, prompt injection, data poisoning, hallucination, model drift, auditability, explainability, and other risks associated with real-time agentic systems.
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Develop practical implementation strategies for moving agentic streaming solutions from experimentation to production through observability, evaluation, governance, continuous monitoring, and controlled automation.
Comprehensive Course Outline
Module 1: Foundations of Agentic Data Streaming and Real-Time Intelligence
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Principles of continuous data, event-driven architectures, real-time analytics, intelligent agents, and automated decision-support systems.
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Evolution from batch analytics and conventional streaming toward agentic systems capable of interpreting events and initiating contextual actions.
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Core components of agentic streaming environments, including events, streams, state, models, agents, tools, knowledge stores, policies, and workflows.
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Real-time intelligence applications across finance, supply chains, manufacturing, telecommunications, cybersecurity, customer operations, and digital services.
Module 2: Streaming Architecture for Agentic Systems
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Designing architectures that connect event producers, brokers, stream processors, real-time databases, vector stores, analytical engines, and AI agents.
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Event schemas, data contracts, metadata, serialization, schema evolution, and context representation for reliable agent interactions.
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Event-driven patterns for triggering agents, coordinating workflows, invoking tools, and connecting analytical outputs with operational systems.
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Architectural considerations involving latency, throughput, scalability, availability, state management, fault tolerance, cost, and system complexity.
Module 3: Real-Time Data Ingestion, Processing and Context Engineering
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Ingesting live data from applications, databases, APIs, IoT devices, transactional systems, sensors, logs, and external event sources.
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Applying streaming transformations, enrichment, filtering, aggregation, windowing, joins, and event correlation to prepare agent-ready information.
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Event-time processing, late-arriving data, out-of-order events, duplicate events, replay, retention, and state management in continuous data environments.
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Building contextual data layers that combine current events, historical records, metadata, business rules, and relevant knowledge for intelligent reasoning.
Module 4: Agentic Reasoning Over Continuous Event Streams
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Designing agents that continuously observe events, interpret changing conditions, identify relevant context, and determine whether further action is required.
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Agent planning and reasoning patterns for decomposing real-time problems into analytical tasks, tool calls, retrieval operations, and workflow actions.
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Managing short-term and persistent agent memory when interpreting sequences of events and maintaining operational context over time.
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Establishing bounded autonomy through policies, permissions, confidence thresholds, approval requirements, escalation paths, and predefined action boundaries.
Module 5: Real-Time Retrieval, Knowledge and Tool Integration
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Integrating retrieval-augmented generation with streaming data, operational databases, knowledge repositories, documents, APIs, and enterprise information systems.
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Designing real-time retrieval workflows that identify relevant historical context and combine it with newly arriving events for better analytical responses.
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Connecting agents with calculators, analytical models, databases, APIs, workflow systems, search services, and operational tools through controlled interfaces.
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Evaluating retrieved information for relevance, freshness, provenance, completeness, and consistency before allowing it to influence agent decisions.
Module 6: Streaming Machine Learning and Real-Time Intelligence
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Applying real-time machine learning to anomaly detection, classification, forecasting, predictive maintenance, fraud monitoring, and behavioral intelligence.
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Designing streaming feature pipelines that continuously update analytical variables and provide current information to predictive and agentic models.
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Combining machine learning predictions with language-model reasoning, business rules, contextual retrieval, and event-driven decision workflows.
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Monitoring model performance, data drift, concept drift, prediction quality, false positives, false negatives, and changing operational conditions.
Module 7: Agentic Automation and Event-Driven Decision Workflows
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Designing workflows in which live events trigger analysis, agent reasoning, tool execution, notifications, approvals, and controlled operational actions.
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Applying agentic automation to incident management, customer support, supply chain exceptions, financial monitoring, maintenance, and operational service processes.
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Coordinating multiple specialized agents for monitoring, investigation, analysis, recommendation, validation, and action within structured workflows.
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Establishing human-in-the-loop mechanisms for high-impact actions, ambiguous situations, exceptions, policy conflicts, and low-confidence agent outputs.
Module 8: Advanced Agentic Streaming Architectures and Emerging Technologies
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Exploring multi-agent systems, event-driven agents, intelligent orchestration, model context protocols, and emerging architectures for real-time AI applications.
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Applying multimodal AI to combine streaming text, images, audio, video, sensor signals, and structured events within real-time intelligence workflows.
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Examining edge intelligence and distributed agentic processing for applications requiring low latency, local inference, connectivity resilience, or privacy-aware computation.
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Designing adaptive architectures that balance model capability, processing latency, computational cost, reliability, scalability, and operational requirements.
Module 9: Security, Governance and Responsible Agentic Intelligence
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Protecting streaming and agentic systems through authentication, authorization, encryption, secrets management, least-privilege access, and secure tool execution.
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Addressing prompt injection, malicious events, data poisoning, unauthorized tool use, sensitive information exposure, and other agent-specific security risks.
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Establishing governance for agent decisions through audit trails, action logs, model documentation, data lineage, policies, approvals, and accountability structures.
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Evaluating responsible AI considerations including privacy, fairness, explainability, uncertainty, human oversight, reliability, and appropriate limits on autonomous action.
Module 10: Evaluation, Observability and Production Implementation
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Measuring agentic streaming performance through latency, event-processing reliability, reasoning quality, retrieval accuracy, tool-use success, and action outcomes.
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Designing observability frameworks covering event pipelines, agent traces, model calls, tool invocations, state transitions, errors, resource consumption, and business outcomes.
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Testing agentic systems through simulations, adversarial scenarios, replayed events, edge cases, failure injection, red-team exercises, and controlled production pilots.
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Developing implementation roadmaps covering use-case selection, architecture, data readiness, agent design, governance, deployment, monitoring, continuous evaluation, and organizational adoption.
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.
