Agentic AI Architectures with Patterns, Frameworks and MCP

discover what AI agents are, a digital employee that perceives, reasons, and acts to achieve goals via tools or APIs, guided by an agentic loop of perception, reasoning, and action.

Contrast generative AI with agentic AI, showing how proactive agents pursue goals through an agentic loop, using LLMs for reasoning and chain-of-thought, with human-in-the-loop examples.

Determine when to use ai agents versus simple tools by balancing simplicity, determinism, and cost, then apply the three-step framework for open-ended or under-specified, dynamic tasks.

Explore real-world use cases for AI agents with open-ended goals and autonomous tool use. See how agents perform multi-step tasks across enterprise automation, data science, programming, research, and system integration.

Explore the core challenges of AI agents, from non-determinism and security guardrails to cost, latency, observability, and hallucination, and learn architectural strategies for robust, secure, and reliable systems.

Design your first simple ai agent, a weather agent, using the lm as a reasoning engine to call a weather api and decide umbrella needs for London.

Identify the seven core components of AI agents—the brain LM, perception, planning, memory, action, learning, and communication—and learn how to build robust agents with guardrails.

Understand how the foundation model serves as the agent's brain, enabling reasoning through prompt engineering with chain of thought and react patterns.

Explore how the perception module translates multi-modal, unstructured data into clean text for the foundation model, triggering the perceive, reason, and act loop.

Learn how an agent uses planning to decompose complex goals into a sequence of tasks, producing a blueprint called an eplan, and contrast static and dynamic (react) planning.

Build a memory notebook architecture with short term memory (context window) and long term memory (vector, SQL, key-value, and document databases) linked by the retrieval augmented generation pattern.

Link the brain, planning, and memory to an action module that executes tools via secure function calling, with least privilege and human-in-the-loop guardrails.

Add a learning loop with a critic and learner to enable the agent to pursue self-improvement by evaluating actions, providing feedback, and updating memory, planning, and utility.

Design the agent’s voice as the translation layer turning internal results into human-friendly output. For other agents, use structured JSON APIs to enable fast, precise communication.

Design a research assistant agent using a five-step recipe—soul (system prompt), brain (lm), hands (web search), memory, and loop orchestration—to autonomously write a short factual report on a complex topic.

Trace the agentic loop of perception, reasoning, action, and learning to show how a digital employee processes a user request from perception through learning.

Design a ChatGPT agent with the agentic loop by implementing perception with normalization and speech-to-text, then reasoning with memory and a web search tool via orchestrator, guiding action and learning.

Design a health agent using the agentic loop and human-in-the-loop pattern to monitor ICU patients, perceive vital data, reason with EMR context, act via tools, and learn from doctor's feedback.

Define AI agent versus genetic AI and explain multi-agent architectures as the factory blueprint, highlighting limitations of a single agent and the need for coordinated teams.

The lecture contrasts ai agent and agentic ai. A single ai agent excels at task execution, while agentic ai emphasizes system orchestration and coordination patterns.

Explore the concept of a multi-agent system with autonomous agents, a shared environment, and a common goal, and learn how a genetic architecture enables an agent-as-a-service approach for scalable solutions.

Compare orchestration and choreography in agentic AI architectures, pairing centralized planning and task delegation with decentralized event-driven coordination. Learn trade-offs in simplicity, scalability, and debugging.

Design orchestration and choreography for agentic AI architectures, using a central orchestrator vs. decentralized event-driven agents that react to shared events.

Explore the four core challenges of agentic AI systems—system-wide observability, emergent behavior, inter-agency communication, and goal conflict over resources—along with tracing, guardrails, and negotiation mechanisms.

Learn how traditional, deterministic workflows contrast with agentic, stateful, llm-driven pipelines that route tasks at runtime, enabling adaptive, non-deterministic ai applications.

Discover the anatomy of a modern genetic workflow: state as shared memory, nodes as modular workers, and edges as LM-driven routing that directs tasks.

Explore sequential handoff, concurrent patterns, magnetic orchestrator pattern, and group chat debates to design adaptive, stateful agentic workflows. Connect nodes and edges, define capabilities, and build flexible genetic applications.

Explore code-first frameworks for stateful workflows, avoiding spaghetti code. Use land graph framework’s graph model to define state as Python classes and wire nodes and edges to coordinate multi-agent workloads.

Learn how low-code and no-code platforms enable rapid visual workflows with drag-and-drop interfaces, weighing vendor lock-in, black-box debugging, and cost at scale against code-first frameworks for core product features.

Build an agentic IT helpdesk workflow that triages and routes tickets to hardware or software specialists using a Microsoft Agent Framework graph.

Explore AI agent frameworks like Lang Chain Length Graph Index, Microsoft agent frameworks, and cloud agent frameworks, and learn how generalist toolkits, specialist frameworks, and managed platforms enable scalable workflows.

Discover the generalist toolkit with LangChain and LangGraph, embracing composability of models, prompts, tools, and parsers, and choose LandGraph for stateful, custom agent workflows.

Explore Lama index as a data-first agent framework, building rich, multi-layered indexes and specialized query engines to handle complex data-intensive tasks.

Discover how the Microsoft Agent Framework (MEF) unifies Autogen and Semantic Kernel to enable enterprise-grade multi-agent collaboration with a graph-based workflow engine, checkpointing, and human-in-the-loop capabilities.

Explore cloud agents as a service from AWS, Azure, and Google, weighing buy versus build and noting autoscaling, security, and the risks of black box reasoning.

Discover why agentic design patterns replace prompt engineering with blueprints for scalable genetic AI. Learn core and multi-agent patterns, including tool use planning, reflection, router specialists, and human-in-the-loop safeguards.

Explore four genetic design principles: goal directed, reactive, stateful, and autonomous, and see how they drive agent reasoning, memory, perception, and autonomous action.

Discover how to connect a language model to real tools using the tool use pattern, with schema, the LM chooser, and the orchestrator managing JSON tool calls.

Explore the planning pattern to decompose a high-level goal into an explicit, ordered plan. Execute steps sequentially with chain-of-thought prompts and manage the plan in a stateful orchestrator, handling failures.

Apply the reflection pattern to insert a writer critic model that evaluates the first draft against the goal, enabling metacognition and an inner self-improvement loop.

Learn how the react compound pattern interleaves reasoning, acting, and observing in a tight loop to adaptively plan and use tools.

Learn multi-agent design patterns and architectures, avoiding monolithic traps, and leverage specialization, parallelization, and debate to build a coordinated digital team of specialized agents.

Learn the router and specialist pattern, a delegation model where a router classifies user intent and delegates tasks to specialized agents such as support and inventory.

Explore the handoff pattern, the assembly line of a sequential workflow where an orchestrator passes a task through agents A, B, and C. It emphasizes reliability for known stable processes.

Learn the group chat debate pattern, a digital meeting room where specialist agents interact in a shared chat history and a moderator guides debate to refine solutions for complex problems.

Master the swarm pattern, a MapReduce–style AI architecture where a queen decomposes massive lists into thousands of parallel subtasks and performs map and reduce phases.

Explore control and safety patterns for agentic ai, implementing guardrails around non-deterministic systems with human in the loop and ejection logic to ensure safe autonomy in enterprise environments.

Apply the human in the loop pattern to ensure high-risk actions are approved by a human, using a deterministic threshold in the orchestrator before the agent executes a tool.

Explore the ejection pattern, a hardcoded deterministic rule that overrides the agent’s non-deterministic loop, using a pre-filter escape hatch to switch to live chat, stop, or policy-safe responses.

Examine the traditional rag (retrieval augmented generation) limitations as a passive, linear pipeline that cannot self-correct, then introduce the genetic rack as a more capable pattern for complex reasoning.

Explore the agentic RAG shift from passive pipelines to a looped system where a reasoning agent actively plans, queries, retrieves data, reflects, and autonomously refines answers.

Compare traditional RAG and genetic RAG to highlight a trade-off: traditional RAG is fast and cheap for simple lookups, while genetic RAG offers self-correcting, iterative reasoning at higher cost.

Explore how agentic RAG uses the active researcher loop—memory, reasoning, planning, action, perception, and reflection—to run a dynamic, multi-loop research task.

Explore three agentic RAG use cases: complex comparative research, automated due diligence, and literature reviews. See how agents decompose goals, plan actions, and synthesize findings.

Build an agentic enterprise architect using a quadrant vector database, vector search adapter, and azure openai embeddings to perform semantic search and generate grounded responses with an llm.

Explore how formal agent protocols enable interoperable agent communications across tools and platforms. Learn about schema, capabilities, discovery, and secure context exchange to decouple the brain from the tools.

The ACP is an open-source specification that aims to be a universal standard for all agent interactions, enabling task management, state synchronization, discovery, and artifact exchange.

Define a focused, lightweight A2A protocol for conversational collaboration, standardizing message schemas and speech acts to enable federated multi-agent systems to talk across different stacks.

Learn how the GUI protocol enables agent-driven interfaces that render rich interactive elements, like buttons and forms, for human in the loop safety and user experience.

Explore MCP, the modal context protocol, a lightweight standard that decouples agents from proprietary tools while letting them connect to tools, APIs, and data sources via manifest-based discovery.

Identify two architecture problems, collaboration between agents and integration with tools. Map them to ACP, 8088, and MCP to decide which protocol fits each need.

Understand why the Model Context protocol (MCP) matters for architects. Decouple agents from tools via a single MCP compliant interface, addressing the 90% agent problem and preventing vendor lock-in.

Explore the four-layer MCP architecture—host and client, protocol, server, and data source—and how the protocol using json-rpc enables secure, modular data access across tools.

Explore MCP core concepts, discovery and execution, through a field-by-field dive, focusing on the manifest.json, well-known path, and token-based security.

Explore the MCP lifecycle and its four-step flow—discovery, menu, order, and delivery—showing how a user prompt triggers manifest.json, HTTP post to the MCP server, tool execution, and data delivery.

Explore why MCP serves as a concise, agent-friendly adapter for OpenAPI, converting complex internal blueprints into a token-efficient, one-page quickstart for LLMs.

Explore MCP design patterns and case studies, focusing on the adapter and gateway patterns to integrate legacy systems and microservices through a single MCP gateway.