[100% Off] Mastering Agentic Ai: From Prompt To Protocols To Production
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; well 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 from prompt engineer to production architect, building autonomous systems that perceive, reason, and orchestrate complex workflows at scale. In Mastering Agentic AI: From Prompt to MCP-A2A to Production (37+ hours), you’ll master LLM API integrations—provider-agnostic with DeepSeek examples for cost optimization—to architect intelligent agents leveraging MCP (Model Context Protocol) for universal tool interoperability and A2A (Agent-to-Agent) for distributed coordination in the 2025 ecosystem.
Whether you’re an AI engineer debugging multi-step reasoning chains, a backend developer scaling ML infrastructure, or a research scientist pushing boundaries in autonomous systems, this course delivers battle-tested, production-grade expertise. Starting with threat modeling and least-privilege security from Day 1, you’ll navigate the agentic spectrum: from perception modules and LLM reasoning engines to action-reflection loops that suppress hallucinations and enforce safe tool execution.
Master advanced prompting as code: implement Chain-of-Thought (CoT) for step-by-step reasoning, Self-Consistency for multi-path validation, Tree of Thoughts (ToT) for parallel exploration, and the ReAct framework (Reasoning + Acting) for tool-augmented problem-solving. Optimize via flexible LLM API calls, A/B testing, and versioned prompt management with automated eval suites.
Build hierarchical memory architectures: deploy Retrieval-Augmented Generation (RAG) pipelines with vector embeddings, hybrid semantic-keyword search, rerankers for precision, and episodic memory with decay/summarization for context window management. Store and query via Pinecone, Weaviate, or Chroma for long-term agent recall.
Extend capabilities through function calling: design idempotent tool schemas, implement error handling with exponential backoff, compose tool chains for complex workflows, and integrate advanced archetypes like coding assistants (GitHub Copilot-style) and Computer Use Agents (CUAs) for GUI automation—all sandboxed for safety.
Scale to multi-agent orchestration: architect manager-worker hierarchies with task decomposition, debate systems for consensus-driven decisions, blackboard architectures for shared memory, pub-sub messaging for asynchronous coordination, and Human-in-the-Loop (HITL) approval gates for high-stakes actions. Build specialized teams where agents negotiate, delegate, and self-correct.
Testing and observability are first-class citizens: adapt unit/integration/E2E frameworks with golden traces for regression testing, track task success rates, token costs, latency p95/p99, and safety violations. Deploy LangSmith for trace visualization, OpenTelemetry for semantic GenAI conventions, Prometheus for metrics aggregation, Jaeger for distributed tracing, and ELK Stack (Elasticsearch-Logstash-Kibana) for centralized logging. Benchmark against AgentBench, GAIA, and ToolBench with automated CI/CD regression gates.
Deploy with production resilience: design orchestrator patterns with queue-based backpressure, enforce guardrails via input validation, PII redaction with regex/NER, output filtering, and fine-grained tool permissions. Optimize for cost/latency: implement semantic caching (Redis/Momento), request batching, prompt compression, and cold-start mitigation. Secure MCP/A2A protocols: validate endpoint trust, defend against tool poisoning, mitigate prompt injection paths, and enforce rate limiting.
By course completion, you’ll ship a production portfolio: a deep-research agent with multi-source synthesis and citations, a collaborative multi-agent swarm with debate consensus, and a monitored production pipeline with dashboards, alerts, and auto-scaling. Pure Python implementations, adaptable LLM APIs (OpenAI, Anthropic, DeepSeek), LangChain/LlamaIndex frameworks, and open-source stacks (Docker, Kubernetes, Temporal).
Tech Stack Covered:
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Prompting: CoT, ReAct, ToT, Self-Consistency, Few-Shot
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Memory: RAG, Vector DBs (Pinecone/Weaviate), Hybrid Search, Rerankers
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Tools: Function Calling, Tool Chaining, Idempotency, Sandboxing
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Multi-Agent: Manager-Worker, Debate, Blackboard, Pub-Sub, HITL
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Protocols: MCP, A2A, REST APIs, WebSockets
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Observability: LangSmith, OpenTelemetry, Prometheus, Jaeger, ELK
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Production: Docker, Kubernetes, Redis Caching, Rate Limiting, PII Redaction
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Security: Threat Modeling, Prompt Injection Defense, Least-Privilege, Guardrails
Join thousands pioneering production agentic systems in 2025. No theory fluff—just code, evals, deployments, and real-world architectures. Enroll now and architect the autonomous intelligence powering tomorrow’s enterprises—your journey from prompt to production starts here!
37+ Hours | 7 Modules | Production-Ready | Security-First | API-Agnostic
