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SECTION 1: Why Revisit Automata Today

Hierarchy of Computational Models

MCG ASSIGNMENT (Metadata)

"Why address automata again today — introduction and motivation"

SECTION 2: Three paradigms of automata control

Paradigm 1: Classical Automation

Paradigm 2: Orchestrated AI

Paradigm 3: Multi-agent Systems

SECTION 3: MCP and A2A: Modern protocols for AI orchestration (NEW)

MCP — Model Context Protocol

A2A — Agent-to-Agent Protocol

MCP vs A2A — Comparison

SECTION 4: Governance: How to safely manage agents

Four Pillars of Secure AI Orchestration

Explore Phase Rules

Sandbox and Isolation

Cost Limits

SECTION 5: Observability and Monitoring of Multi-Agent Systems

Key Observability Tools

What to log in every agent step

Agent drift detection

SECTION 6: Anti-hallucination checklist for code review (NEW)

Checklist: Code and dependencies

Checklist: Tests

Checklist: Security

Checklist: Scope and Responsibility

SECTION 7: Practical scenarios

Scenario 1: GitHub Workflow for AI Development

Scenario 2: Preparation and Distribution of a Mini-course

Scenario 3: Customer Support with Escalation

SECTION 8: ADVANCED: Decision Matrix: Which Paradigm to Use?

Primary Decision Questions

Extended Criterion: Security and Compliance

SECTION 9: 30-Day AI Orchestration Pilot

Pilot Tracker — Record Template

Post-Pilot Decision

"What exactly to measure and why" — metrics for the pilot tracker (time, cost, quality, risk).

Reflective survey

Reflection Survey

Glossary of Terms

Welcome to the course "Three Paradigms of AI Orchestration: From Automata to Multi-Agent Systems." Artificial intelligence is evolving at a breakneck pace, and the transition from simple automations to fully autonomous multi-agent systems represents a revolution in the way we work and create software.

Welcome to the course "Three Paradigms of AI Orchestration: From Automata to Multi-Agent Systems." Artificial intelligence is evolving at a breakneck pace, and the transition from simple automations to fully autonomous multi-agent systems represents a revolution in the way we work and create software. This course will guide you through historical developments and the cutting-edge technologies shaping today's AI orchestration. You will discover why it is crucial today to re-understand automata theory and how to apply it to modern AI agents as an analytical metaphor. We will explore in detail the three main paradigms: classical automation, orchestrated AI (where the LLM proposes and the human approves), and advanced multi-agent systems with dynamic task delegation. You will learn to work with the latest protocols, such as the Model Context Protocol (MCP) and the Agent-to-Agent Protocol (A2A), and understand when and how to use them correctly. Great emphasis is placed on security and governance – you will master the four pillars of secure AI orchestration, learn to implement sandbox environments, set cost limits, and effectively monitor so-called agent drift. The course also offers practical real-world scenarios, including AI integration into GitHub workflows, customer support automation with human escalation, and content creation. Finally, you will take away a specific decision matrix for choosing the right paradigm and a plan for launching a 30-day pilot project in your organization. Prepare for the future and become an expert in the management and orchestration of artificial intelligence!

Three Paradigms of AI Orchestration: From Automata to Multi-Agent Systems

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            <p>Moderní technologické prostředí prochází zásadní transformací, v níž se hranice mezi deterministickým softwarem a autonomními systémy postavenými na velkých jazykových modelech (LLM) postupně stírají. Tento kurz se zaměřuje na hluboké pochopení hierarchie výpočetních modelů, přičemž se vrací k základům teorie automatů jako nezbytnému analytickému nástroji pro řízení současné umělé inteligence. Cílem je poskytnout účastníkům jasný rámec pro pochopení toho, jak se stavy, vstupy a přechody klasických stavových strojů promítají do komplexních architektur moderních AI agentů, a proč je právě dnes kritické znovu definovat principy orchestrace těchto systémů bez nánosu marketingového hypu.</p>

<p>Obsah kurzu systematicky provází třemi klíčovými paradigmaty řízení: od rigidní klasické automatizace řízené pevnými pravidly, přes hybridní modely orchestrované AI, až po pokročilé multi-agentní systémy založené na dynamické delegaci úloh. Zvláštní pozornost je věnována nově vznikajícím standardům, jako jsou Model Context Protocol (MCP) a Agent-to-Agent Protocol (A2A), které definují budoucnost bezpečné interoperability mezi nezávislými AI entitami a podnikovými daty. Kromě teoretických základů kurz klade důraz na praktickou rovinu governance, bezpečnosti a observability, včetně metodiky pro realizaci řízeného pilotního projektu, což umožní vývojářům, produktovým manažerům i technologickým lídrům nasazovat AI řešení s vysokou mírou spolehlivosti, kontroly nákladů a etické odpovědnosti.</p>
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