Agentic AI: Production Grade AI Agents using CrewAI and AWS

Trace the evolution from pre-lms approaches to large language models, including transformers and attention, and explain how these enable agentic ai to plan and act, with hands-on cursor setup.

Explore how agentic ai autonomously decides, acts, and learns to achieve goals with minimal human intervention, with end-to-end use cases in software development and healthcare.

Explore enablers for agentic AI, focusing on the LLM's reasoning and planning. Use reflection, self-correction, and the react pattern to loop through thought, action, and observation with external tools.

Explore Cursor IDE's AI-powered code editor to generate Python code, run tests locally, and validate inputs while building a Fibonacci last-three-numbers class.

Unveil the purpose of agentic AI by exploring planning, reasoning, tool use, and task execution. Introduce crew AI for autonomous agents and use LLM on AWS bedrock to study observability.

Explore an ai agent example using system prompts, user prompts, and tools to fetch latest product data. See the react pattern, memory, and tool-driven reasoning that produce final responses.

Explore deploying agents with AWS Agent Core, learn version management, session isolation, and a custom authorizer to secure identity management, with emphasis on observability through metrics and traces.

Deploy an agentic AI app to AWS Agent Core, using an HTTP endpoint and IAM authentication; auto scales with sessions and uses Bedrock LM, CodeBuild, and observability.

Explore deploying the agentic application on AWS from Agent Core, with Docker images, ECR, and an IAM role. Observe CloudWatch observability and versioned endpoints with LM calls to Bedrock.

Explore how vector databases store embeddings and enable similarity-based search for rag workflows, using 1024-number vectors and pine cone's free serverless option.

Understand rag data flows, from pre-processing that parses PDFs into text and images and stores chunks in a vector store, to embedder, retriever, and augmented generator for the LLM.

Explore how agentic applications use retrieval augmented generation to choose rag data stores, retrieve data via tools, and augment prompts for the language model to generate the final response.

Demonstrates using AWS agent core memory to store and retrieve session-based short term memory with seven-day event expiration, enabling follow-up questions with conversation history and a retrieval-augmented generation flow.

Explore memory in genetic applications, from short-term to long-term memory, including how long-term memory stores user preferences and extraction strategies like summarization and semantic memory with crew AI flows.

Explore four popular single agent patterns for agentic AI—tool use, agentic rag, react, and plan and execute—highlighting how they reason, act, and adapt.

Learn to orchestrate multiple agents with key multi-agent patterns, including sequential execution, parallel execution, router pattern, and orchestrator worker pattern, highlighted through practical demos.

Compare structured workflow and autonomous orchestration as design philosophies, contrasting deterministic, event-driven processes with autonomous, non-linear agent collaboration for production-grade automation and exploratory tasks.

Learn how lab evaluations use Elm benchmarks to assess a language model's accuracy, coherence, relevance, and safety, with MLU and GPC as examples, and note agentic evaluations require custom data.

Online evaluation probes apps in live production, measuring success rates and user satisfaction with real time traffic while comparing LM as a judge and human annotation for scoring.

See a demo of human annotation, filtering traces over 90 seconds and annotating execution plans as not a good plan, somewhat optimal plan, or optimal plan to improve insights.

Demonstrates the critique agent pattern within an end-to-end production-grade AI workflow. Shows inline evaluation, intent analysis, and feedback loops between research and critique agents to improve report quality.

Study offline evaluation for production-grade agentic AI by testing in a sandbox with a test set of inputs and expected outputs, measuring latency and LM-based performance scores.

Explore how agentic ai are probabilistic and require evaluation mechanisms, including lm-based online assessments, offline assessments, human annotation, and a critic agent for online feedback.

Explore inter agent communication with a two-way protocol, tracing its origin from Google to the Linux Foundation, and compare it to MCP through demos.

Learn about the A-2a two-way protocol, an open standard for seamless, multi-turn inter-agent communication built on HTTP, JSON-RPC, OAuth, and API keys, with agent cards for discoverability.

Explore end-to-end a2a interaction flows, from client agent discovery and message exchanges with remote agents and tools to multi-turn task handling using context id session and message and task objects.

Explore the agentic ai via a two way protocol using the eight way inspector to fetch agent cards, inspect skills, and generate a research report through multi turn interactions.

Compare MCP and A-2a; MCP focuses on agent-to-tool integration and context access, while A-2a standardizes inter-agent communication with multi-turn task-based interactions, both coexisting.