Mastering Agentic AI: From Prompt to Protocols to Production

Explore the evolution of AI from rule-based systems to agentic AI through a hands-on coding example, illustrating pattern matching, memory, tools, and goal-oriented planning.

Dive into how an agent processes environment data through six core components—perception, observation, memory, reasoning, planning, and action with reflection—in a closed-loop learning cycle.

Explore how LLMs serve as reasoning engines in agentic AI, using simple reasoning, chain-of-thought, and self-reflection prompts, while mitigating grounding errors, hallucinations, and tool misuse.

Develop a security-first agent by detecting and mitigating prompt injections, preventing data exfiltration, enforcing least privilege, and sandboxing risks, through modular threat modeling and defense in depth.

Explore how perception, reasoning, and action stages weave together using working memory, short-term memory, and long-term memory to drive planning, reflection, and episode memory, illustrated by a travel agent example.

Master the core components of the retrieval augmented generation pipeline, from loading and chunking to embeddings, vector storage, query enhancement, and final response generation, leveraging long-term memory.

Explore how retrieval augmented generation builds long-term memory by linking an LLM to a vector database via embeddings and the six-stage core RAG pipeline.

Chunk loaded data into optimized segments using fixed size, context aware, or semantic based strategies, balancing context preservation with retrieval efficiency for effective rag implementations.

The query stage converts user input into embeddings, searches a vector database, and returns an LLM-generated answer from retrieved chunks, using cosine similarity and ANN to deliver actionable knowledge.

Learn the six-step core RAG pipeline for long-term memory: load data, chunk data, create embeddings, store with metadata and index structures, augment queries by cosine similarity, and generate responses.

Learn the core rac/rag pipeline for long-term memory, from loading and chunking documents to embedding, storing in SQLite, and querying with hybrid semantic and keyword search to generate grounded responses.

Explore vector databases and hybrid search, learn how the hybrid score is calculated, and decide when to use keyword, semantic, or hybrid search to build a support agent.

Implement hybrid search with vector databases, applying reranking via a cross encoder, and blend semantic and keyword search to power an intelligent customer support agent.

Explore episodic memory in agentic AI, including encoding, indexing, and context-based storage as the agent's autobiography, with decay, summarization, pruning, and prioritization for relevant experience retrieval and personalized responses.

Explore episodic memory implementation with embeddings and a vector database, and apply memory management via decay and summarization for efficient recall.

Demonstrate a customer support agent that uses episodic memory and memory management, including decay, summarization, and retrieval with reranking.

Learn agentic rag, a retrieval augmented generation workflow that autonomously plans retrieval and dynamically selects sources. Verify and cross-check information with multiple sources to assess confidence and produce trustworthy reports.

Master agentic rag best practices, from planning with chain-of-thought to verification via reflection. Implement hybrid search with metadata filters for source selection, and apply episodic memory and encryption for security.

Master agentic rag by detailing autonomous retrieval and verification: plan retrieval strategies (including multi-hop for complex queries), source information, verify with cross-checks and bias detection, and generate grounded responses.

Build an autonomous agentic RAG system that retrieves and verifies information from internal database, arXiv, and social media sources with a planning engine, a verification engine, and episodic memory.

Extend LLM capabilities with tools by integrating the MCP protocol to access real-time data, run code, and perform complex calculations, turning the LLM into a capable agent.

Shows a weather agent with tool integration, episodic memory, vector db, and rag details, using react-based reasoning and secure input validation to fetch real-time Paris weather in Celsius.

Design and deploy a four-tool agent (weather, calculator, email, fact-check) to reason, access real-time data, use memory, and synthesize validated results.

Explore tool composition and chaining as foundational concepts in agentic AI design, and examine scoping and sandboxing. Apply these ideas to build a travel agent and implement prompt design.

Build a travel agent that coordinates flight, weather, and calendar APIs with security, sandboxing, and idempotency, using orchestration and episodic memory for reliable, automated travel planning.

Build a travel agent by applying tool composition, chaining, and safety, with a security sandbox, input validation, audit logging, rate limiting, and parallel execution guided by LM planning.

Build a secure, multi-tool e-commerce assistant by composing and chaining tools with a security-first framework, including product search, inventory, price analytics, and personalized recommendations.

Explore advanced agent archetypes by combining LLMs, memory, and tools to create coding and computer usage agents, and learn the MCP protocol, React framework, and secure, modular design best practices.

Develop advanced agents, coding assistant and general computer usage, using a three-tool workflow (generate, lint, execute) with session and long-term memory and a React-driven safety cycle.

Explore agent specialization and three multi-agent architectures—manager-worker, debate-based, and sequential/parallel pipelines—by building base and specialized agents and a unified system demo.

Explore multi-agent architectures for automated block production, implementing manager–worker, debate-based, and pipeline patterns. Learn end-to-end content creation with base and specialized agents covering research, writing, editing, and SEO.

Explore inter-agent communication patterns—direct messaging, shared blackboard, and pub-sub—applied to a real-time e-commerce order processing workflow with AI-enabled agents.

Build a testing framework for AI agents, covering unit, integration, and end-to-end testing. Mock MCP servers, arXiv api, and vector db to support deterministic runs and golden-trace validation.

Explore benchmarks and automated evaluation for a multi-agent system, implementing an agent bench across reasoning, tool use, planning, and interaction with MCP and A-2a protocols and safety and quality gates.

Develop production-ready agent architectures by leveraging orchestrators, pipelines, supervisors, and queuing systems to achieve scalable, fault-tolerant, low-latency performance and safe deployment, with observability using OpenTelemetry and the Prometheus-Jaeger-Elk stack.

Implement a production-ready agent architecture with an orchestrator coordinating data, research, and report agents through multi-stage pipelines, back pressure, priority queues, a token bucket limiter, and circuit breakers.

Implement robust guardrails in agentic ai using input validation, pii reduction, and output filtering with fine access control, observability, and MCP/A-2a/ANP protocols for a secure multi-agent research system.

Optimize cost and latency in multi-agent systems by applying caching, batching, and cold start management while monitoring observability and integrating MCP and A-2a protocols.

Master cost and latency in multi-agent AI systems by caching with in-memory, distributed, and document stores, plus batching and prewarming to prevent cold starts.

Apply caching and batching to cut cost and latency in multi-agent workflows, with observability across caching, batching, and TTL freshness.

Explore securing interoperability protocols with prompt injection defense, input validation, and endpoint trust. Implement tool poisoning detection, signature based checks, TLS and OAuth, and a secure MCP driven multi-agent workflow.