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.

Trace ai's evolution from rule-based weather bots to ml-based task managers and agentic ai with memory and tools, illustrating intent detection and adaptive behavior.

Mastering agentic AI guides you through the six core components of an agent—perception, memory, reasoning, and action—with hands-on coding, demos, and a reflection loop to improve performance.

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.

Demonstrates a basic MCP server with get user info and get department employees, and an inter-agent two-way protocol that discovers and delegates tasks to an HR agent.

Explore the 2025 agentic ecosystem by implementing MCP and A2A protocols, building a central communication bus, and orchestrating multi-agent workflows with service discovery, registration, and protocol interoperability.

Learn to craft prompts with three components—system prompt, delimiter, and JSON formatted output—and test them across neutral, technical, and friendly roles to produce structured, validated JSON using schema.

Explore advanced reasoning strategies—chain of thought, self-consistency, and tree of thoughts—and implement them via direct, few-shot, and zero-shot prompts, including hybrid backtracking and weighted voting.

Master prompt management by implementing versioning, evaluation, and A/B testing for a customer support agent. Define test cases, metrics, and a prompt manager to balance accuracy, latency, politeness, and security.

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.

Develop a personal assistant agent by integrating crypto price lookups, unit conversions, task scheduling, and QR code generation, with robust tool schemas, API fallbacks, and multi-tool orchestration.

Master the mechanics of function and tool calling for agentic AI by building an error‑aware workflow with tool schemas, ticketing integration, retries with backoff and jitter, and idempotent caching.

Apply the mechanics of function and tool calling to build a robust ecommerce order management agent with error handling, retry logic, exponential backoff with jitter, idempotency, and llm memory.

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.

Develop advanced agent archetypes by building a computer usage agent with a system monitoring tool and safe file management. Implement memory layers and a thought-action-observation loop for autonomous tool use.

Explore agent specialization and multi-agent architectures to boost workflow performance, using niche prompts, manager-worker and debate-based designs, plus sequential and parallel pipelines.

Learn to build multi-agentic systems with agent specialization and scalable collaboration, using llms, memory, and tools, through manager-worker, debate-based, and processing pipeline architectures.

Explore agent specialization and multi-agent architectures to divide labor, assign roles like planner, coder, and debugger, and coordinate shared tools for scalable, domain-specific task solving.

Explore how debate-based AI agents evaluate cloud providers—AWS, Google Cloud, and Azure—via a moderator that weighs cost, features, and integration to reach a consensus with equal standing.

Design prompts with role, behavior, and constraints using delimiters and JSON schema; implement multi-agent workflows with manager and coder, relying on episodic memory and sequential or parallel pipelines.

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 in multi-agentic AI systems, including direct messaging, the shared blackboard, and pub-sub approaches, and examine applying a shared blackboard to the coding agent with prompt design.

Discover inter-agent communication in multi-agent systems, enabling collaboration through information exchange, instruction passing, and results sharing to support task coordination, data security, scalability, and collaborative decision making via MCP standards.

Direct messaging enables point-to-point communication to a specific recipient, supporting synchronous or asynchronous delivery with explicit addressing. It offers simple implementation and low overhead, but scales poorly in large systems.

Explore how shared blackboard systems centralize communication by reading and writing to a common data store, enabling asynchronous, debate-based workflows among coder, debugger, and tester.

Design a secure multi-agent coding system with manager, coder, and debugger using a shared encrypted blackboard, json messages, aes-256, sandboxed agents, and role-based and field-based access control.

Define the manager agent workflow by setting role, responsibilities, and a blackboard schema (task id, agent id, data, status) to coordinate multi-agent execution from input to completed code.

Explore three inter-agent communication patterns—direct messaging, shared blackboard, and publish-subscribe—highlighting scalability, data conflicts, and computational demands.

Explore three inter-agent messaging patterns—direct messaging, shared blackboard, and pub-sub—through hands-on coding examples that implement and demo messaging among a multi-agent coding assistant.

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.

Introduces human-in-the-loop integration for multi-agent systems, detailing approval gates, intervention points, and real-time oversight to balance automation with human judgment.

Balance automation and human oversight in multi-agent customer support with HITL integration, coordinating a manager, ticketing, and response agent via the MCP protocol, with priority assessment on a shared blackboard.

Implement human-in-the-loop in a multi-agent system with approval gates, continuous feedback, and security layers to build trust, then explore practical HITL coding examples across two use cases.

Learn to build a human-in-the-loop content moderation pipeline, from environment setup and data structures to a moderation agent, approval gates, and feedback analysis, tested with three sample contents.

Explore the deep research agent pattern by combining LLMs, memory, and tool access to conduct thorough research through planning, browsing, synthesis, and citation phases.

Explore the deep research agent pattern, a phased, autonomous llm system with multi-agent collaboration across planning, browsing, synthesis, and citation to deliver verified, relevant ai trends research.

Apply the deep research agent pattern's synthesis phase to analyze and integrate browsed data using cross-referencing, validation, and fact-checking to produce structured insights and a research summary with dedicated agents.

Discover a testing framework for AI agents, covering unit, integration, and end-to-end tests, golden traces, and observability across the workflow of multi-agentic AI systems.

Explore a testing framework for ai agents to ensure reliability, interoperability (tool access, database access, agent-to-agent communication), and early error detection.

Adapt traditional unit, integration, and end-to-end testing for agentic AI systems, using mocking for LLMs, observability with OpenTelemetry and Jaeger, and golden traces to manage non-determinism.

Isolate agents and components for unit testing with mocked LLMs to ensure predictable JSON outputs, then validate integration, end-to-end, and golden traces testing of MCP and A2A protocols.

Leverage observability tools to test and debug a distributed multi-agent system, using Jaeger tracing, ELK, and Prometheus to monitor traces, logs, and metrics. Validate MCP, ANP, A2A against golden traces.

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 a new evaluation matrix for multi-agent agentic AI systems. The framework emphasizes reliability, efficiency, and safety while measuring task success, quality, token cost, latency, and safety violations.

Explore how MCP, A2A, and ANP protocols enable agent interaction, and how observability tools, Jagger, OpenTelemetry, Prometheus, and ELK stack, measure latency, token cost, and safety.

Implement a multi-agent customer support system with triage, resolution, and supervisor agents. Evaluate task and system metrics, safety, quality, customer satisfaction, and export json reports for analysis.

Trace and observe a multi-distributed genetic system by collecting traces, metrics, and logs to identify bottlenecks, validate performance against golden traces, and guide system improvement.

Explore LangSmith, a tracing, debugging, evaluation, and analytics platform for LangChain workflows. Visualize LLM calls, tool interactions, and chain executions, inspect inputs and outputs, and monitor performance metrics.

Explore OpenTelemetry, a vendor-neutral open-source framework for collecting traces, logs, and metrics in distributed Gen-AI multi-agent systems, with semantic conventions and interoperability across LangChain, Nemo, and custom code.

Integrate MCP and A2A with open telemetry to capture traces of tool calls and agent messages using standardized attributes like tool name, request ID, and message ID.

Maintain observability by capturing traces and spans with OpenTelemetry. Learn the three pillars—traces, logs, metrics—and apply Langsmith, OpenTelemetry, Jagger, Prometheus, and ELKstack in a practical multi-agentic AI tracing exercise.

Implement tracing and observability in a comprehensive multi-agent system by building OpenTelemetry and Lang Smith traces, capturing spans, LM calls, and protocol interactions for Jagger and Prometheus metrics.

Explore benchmarks and automated evaluation for multi-agent systems, using agent bench to assess performance, efficiency, and safety, then apply regression gates and integration with MCP and A2A protocols.

Explore the observability toolkit stack—traces, logs, and metrics—driven by OpenTelemetry for Gen‑AI observability, and apply it to evaluation pipelines using ELK, Prometheus, and Grafana for accuracy, latency, and cost.

Create an automated multi-agent evaluation pipeline where a manager delegates to a research and synthesis agent to fetch papers and summarize findings, with trace observability, tests, and gating metrics.

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.

Benchmark and automate evaluation for a multi-agent system by setting up the MCP and A-2a protocols, defining a test suite, and enabling CI/CD with regression gates.

Scale production-ready agent architectures with the orchestrator, pipelines, supervisor, and queuing system. Bind observability with open telemetry, MCP, A2A, and the Prometheus, Jagger, ELK stack for multi-agent research.

Explore production-ready multi-agent architectures with a focus on scalability, fault-tolerant deployment, and observability, using Langsmith and open telemetry to ensure low latency and maintainability.

Explore production-ready agent architectures by comparing orchestrators, pipelines, supervisors, and queuing systems. Learn how MCP, A2A, and ANP enable distributed agent communication, with observability stacks like Langsmith, OpenTelemetry, and Prometheus.

Explore observability tools for agentic AI systems, including OpenTelemetry, Langsmith, Prometheus, Grafana, and the ELK stack. Apply Gen-AI semantic conventions with OpenTelemetry and Langsmith to enable tracing and metric collection.

Explore an observability stack for multi-agent research systems, integrating OpenTelemetry, Jagger, Prometheus, ELK stack, and Langsmith with traces, queues, agents, and backpressure to deliver end-to-end research summaries.

Scale production-ready multi-agentic systems by using an orchestrator to manage workflows, implement flexible pipelines, monitor health with a supervisor, and apply Redis-based queues with backpressure and observability.

Explore production ready agent architectures by implementing an orchestrator-driven workflow with a supervisor, backpressure, rate limiting, circuit breakers, and observability for a parallel research and sequential synthesis pipeline.

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.

Explore robust guardrails as a multi-layer defense using input validation, output filtering, pii reduction, and fine-grained tool permissioning (rbac) to prevent prompt injection and enable continuous observability and evaluation.

Explore MCP security: tool access checks with vector database access, OAuth 2.1, bearer tokens, dynamic client registration, audit trails, plus A2A/ACP validation, role-based access, and ANP discovery.

Leverage Langsmith alongside OpenTelemetry for agent tracing, real-time monitoring, and failure detection, with self-hosting and cross-framework integration, then route spans to Jagger, ELK, and Prometheus for 24x7 observability.

Form an observability pipeline with Jagger, elk, and Prometheus to enable distributed tracing, centralized logging, and metric alerts for microservices, supporting request flow analysis and bottleneck detection.

Implement robust guardrails for a multi-agentic AI system by input validation and sanitization, output filtering and PII reduction, and fine-grained access control, while integrating observability with MCP, A2A, and ANP.

Develop robust guardrails by expanding input validation, adding PII redaction with confidence scores, and implementing dynamic role-based access with time-based constraints, reinforced by distributed tracing and an MCP gateway.

Explore cost and latency optimization for agentic systems using caching, batching, and cold start management, with pre-warming, observability, and MCP and A2A integration.

Reduce cost and latency in multi-agent systems by optimizing LLM token usage, tool calls, and inter-agent communication, using a repeatable cycle of caching, batching, and AB testing for scalability.

Explore four optimization strategies—caching, batching, cold start management, and integration with observability tools. Leverage MCP and A2A protocols to reduce token cost and latency while boosting throughput.

Group tasks into batches to reduce API calls, cost, and latency by collecting tasks in a Redis queue and sending when the batch size or a time limit is reached.

Minimize cold start latency by pre-warming environments to deliver fast responses and preserve user trust. Use serverless or containerized deployments with pre-loaded models and scheduled pings to maintain readiness.

Explore safety risks in interoperability protocols such as MCP, A2A, and ANP for agentic AI, including endpoint trust, tool poisoning, prompt injection, observability, and defense mechanisms.

Mitigate prompt injection by validating inputs, isolating system prompts, and employing content moderation. Enable observability with tracing, logging, and metrics to monitor AI security posture.

Integrate MCP, A2A, and ANP using OAuth, TLS, endpoint validation, and anomaly detection to secure tool access, messaging, and agent discovery.

Explore the observability stack for protocols with OpenTelemetry, Langsmith, Jagger, ELK, and Prometheus to trace security events, log incidents, and enable real time alerts, applied to multi agent research systems.

Secure a multi-agent research system with TLS, OAuth, endpoint verification, and OpenTelemetry tracing, coordinating manager, research, and synthesis agents through pedantic validation and schema checks.

Explore safety challenges in interoperable multi-agent systems, including endpoint rust, tool poisoning, and prompt injection, and apply observability, input validation, and OAuth with role-based access to secure production deployments.

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.

Implement a three-layer security framework for a multi-agent interoperability system, including prompt injection defenses, endpoint trust with certificate pinning, and tool poisoning detection, plus secure two-way authenticated communication.