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Mastering Agentic Ai: From Prompt To Protocols To Production
Published 12/2025
Created by Vinit Singh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 150 Lectures ( 37h 42m ) | Size: 22.5 GB [/center]
Build Agentic AI with MCP-A2A Protocols: Advanced Prompting, RAG Memory, Tool Calling & Multi-Agent System to Production
What you'll learn
Master the core components of agentic AI, including perception, reasoning, action loops, and LLM limitations for building autonomous systems.
Apply advanced prompting techniques like Chain-of-Thought, ReAct, and Tree of Thoughts to enhance LLM reasoning and solve complex multi-step tasks.
Design RAG pipelines, vector databases, and episodic memory systems to enable agents with long-term knowledge retention and autonomous retrieval.
Build, test, and deploy scalable multi-agent systems using MCP/A2A protocols, with robust safety guardrails, evaluation metrics, and production optimizations.
Requirements
Key Prerequisite: Basic proficiency in Python programming and familiarity with large language models (LLMs) or APIs.
Accessibility Note: No prior agent-building experience required-ideal for motivated beginners with foundational AI knowledge; we'll ramp up on tools like Prometheus, Jaeger, ELK Stack, and DeepSeek API usage through practical examples.
Description
Dive into the Agentic AI Revolution and transform your skills from basic LLM prompting to deploying autonomous, scalable systems that think, act, and collaborate like never before. In this hands-on course, Mastering Agentic AI: From Prompt to MCP - A2A to Production, you'll leverage LLM APIs for client calls-adaptable to any provider, with practical examples using DeepSeek for its superior cost benefits-to build intelligent agents ready for the 2025 ecosystem-complete with MCP for tool interoperability and A2A for seamless inter-agent communication.Whether you're an AI engineer debugging complex workflows, a developer scaling ML pipelines, or a researcher pushing boundaries in autonomous systems, this course equips you with practical, production-grade expertise. Starting with foundational threat modeling and safe setups, you'll explore the agentic spectrum: from perception and reasoning engines powered by LLMs to action-reflection loops that mitigate hallucinations and tool misuse.Master advanced prompting paradigms-treat prompts as code with Chain-of-Thought (CoT), ReAct frameworks, and Tree of Thoughts (ToT)-optimized via flexible LLM API integrations for superior reasoning in multi-step tasks. Build robust memory layers: implement Retrieval-Augmented Generation (RAG) pipelines with vector databases, hybrid semantic/keyword search, and episodic memory management using decay and summarization techniques for context-aware agents.Extend your agents' capabilities through tool calling mechanics: design idempotent schemas, chain compositions, and integrate archetypes like coding assistants or computer-using agents (CUAs). Then, scale to multi-agent architectures-manager/worker hierarchies, debate systems, and pub-sub coordination-infused with Human-in-the-Loop (HITL) for ethical oversight.Testing and observability are non-negotiable: adapt unit/integration/E2E frameworks with "golden traces," track metrics like task success and token costs, and deploy tracing via LangSmith for eval suites, OpenTelemetry for semantic conventions, Prometheus for monitoring, Jaeger for distributed tracing, and ELK Stack for logging. Benchmark against AgentBench and set up regression gates for reliability.Finally, deploy with confidence: architect orchestrators handling backpressure, enforce guardrails like PII redaction and least-privilege permissions, and optimize for cost/latency via caching and batching. Uncover security in MCP/A2A protocols, from endpoint trust to prompt injection defenses.By course end, you'll have a portfolio of real-world projects: a deep-research agent synthesizing cited insights, a collaborative multi-agent swarm, and a production pipeline monitored end-to-end. No fluff-just code, evals, and deployments using Python, adaptable LLM APIs, and open-source stacks.Join thousands pioneering agentic AI in 2025. Enroll now and turn prompts into production powerhouses-your future in autonomous intelligence starts here!
Who this course is for
Target Audience: AI engineers, software developers, and data scientists eager to transition from traditional ML to building intelligent, autonomous agents with observability stacks like LangSmith, OpenTelemetry, Prometheus, Jaeger, and ELK, leveraging DeepSeek API for efficient LLM client integrations.
Additional Fit: Researchers exploring multi-agent collaboration; product managers shaping AI-driven products with robust monitoring, tracing, and API-driven workflows.
Ideal Professionals: Tech roles in startups or enterprises aiming to deploy production-ready systems, using tools like DeepSeek API for real-world LLM calls alongside end-to-end visibility and debugging.
Motivation Match: If you're passionate about leveraging LLMs for real-world automation-integrating advanced telemetry for safety and performance with DeepSeek API-and want to stay ahead in the 2025 agentic ecosystem, this is your roadmap from prompts to scalable deployment.
Mastering Agentic Ai: From Prompt To Protocols To Production
Published 12/2025
Created by Vinit Singh
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 150 Lectures ( 37h 42m ) | Size: 22.5 GB [/center]
Build Agentic AI with MCP-A2A Protocols: Advanced Prompting, RAG Memory, Tool Calling & Multi-Agent System to Production
What you'll learn
Master the core components of agentic AI, including perception, reasoning, action loops, and LLM limitations for building autonomous systems.
Apply advanced prompting techniques like Chain-of-Thought, ReAct, and Tree of Thoughts to enhance LLM reasoning and solve complex multi-step tasks.
Design RAG pipelines, vector databases, and episodic memory systems to enable agents with long-term knowledge retention and autonomous retrieval.
Build, test, and deploy scalable multi-agent systems using MCP/A2A protocols, with robust safety guardrails, evaluation metrics, and production optimizations.
Requirements
Key Prerequisite: Basic proficiency in Python programming and familiarity with large language models (LLMs) or APIs.
Accessibility Note: No prior agent-building experience required-ideal for motivated beginners with foundational AI knowledge; we'll ramp up on tools like Prometheus, Jaeger, ELK Stack, and DeepSeek API usage through practical examples.
Description
Dive into the Agentic AI Revolution and transform your skills from basic LLM prompting to deploying autonomous, scalable systems that think, act, and collaborate like never before. In this hands-on course, Mastering Agentic AI: From Prompt to MCP - A2A to Production, you'll leverage LLM APIs for client calls-adaptable to any provider, with practical examples using DeepSeek for its superior cost benefits-to build intelligent agents ready for the 2025 ecosystem-complete with MCP for tool interoperability and A2A for seamless inter-agent communication.Whether you're an AI engineer debugging complex workflows, a developer scaling ML pipelines, or a researcher pushing boundaries in autonomous systems, this course equips you with practical, production-grade expertise. Starting with foundational threat modeling and safe setups, you'll explore the agentic spectrum: from perception and reasoning engines powered by LLMs to action-reflection loops that mitigate hallucinations and tool misuse.Master advanced prompting paradigms-treat prompts as code with Chain-of-Thought (CoT), ReAct frameworks, and Tree of Thoughts (ToT)-optimized via flexible LLM API integrations for superior reasoning in multi-step tasks. Build robust memory layers: implement Retrieval-Augmented Generation (RAG) pipelines with vector databases, hybrid semantic/keyword search, and episodic memory management using decay and summarization techniques for context-aware agents.Extend your agents' capabilities through tool calling mechanics: design idempotent schemas, chain compositions, and integrate archetypes like coding assistants or computer-using agents (CUAs). Then, scale to multi-agent architectures-manager/worker hierarchies, debate systems, and pub-sub coordination-infused with Human-in-the-Loop (HITL) for ethical oversight.Testing and observability are non-negotiable: adapt unit/integration/E2E frameworks with "golden traces," track metrics like task success and token costs, and deploy tracing via LangSmith for eval suites, OpenTelemetry for semantic conventions, Prometheus for monitoring, Jaeger for distributed tracing, and ELK Stack for logging. Benchmark against AgentBench and set up regression gates for reliability.Finally, deploy with confidence: architect orchestrators handling backpressure, enforce guardrails like PII redaction and least-privilege permissions, and optimize for cost/latency via caching and batching. Uncover security in MCP/A2A protocols, from endpoint trust to prompt injection defenses.By course end, you'll have a portfolio of real-world projects: a deep-research agent synthesizing cited insights, a collaborative multi-agent swarm, and a production pipeline monitored end-to-end. No fluff-just code, evals, and deployments using Python, adaptable LLM APIs, and open-source stacks.Join thousands pioneering agentic AI in 2025. Enroll now and turn prompts into production powerhouses-your future in autonomous intelligence starts here!
Who this course is for
Target Audience: AI engineers, software developers, and data scientists eager to transition from traditional ML to building intelligent, autonomous agents with observability stacks like LangSmith, OpenTelemetry, Prometheus, Jaeger, and ELK, leveraging DeepSeek API for efficient LLM client integrations.
Additional Fit: Researchers exploring multi-agent collaboration; product managers shaping AI-driven products with robust monitoring, tracing, and API-driven workflows.
Ideal Professionals: Tech roles in startups or enterprises aiming to deploy production-ready systems, using tools like DeepSeek API for real-world LLM calls alongside end-to-end visibility and debugging.
Motivation Match: If you're passionate about leveraging LLMs for real-world automation-integrating advanced telemetry for safety and performance with DeepSeek API-and want to stay ahead in the 2025 agentic ecosystem, this is your roadmap from prompts to scalable deployment.
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