🤖Applied Agentic AI for TPMs & Product Leaders

Establish the technical baseline with Python data structures, generative AI building blocks, and an intention-driven retention agent exercise[cite: 4, 5].

Understand how autonomous agents combine prompts, external tools, memory, and reflection to move from reflex responses to reasoning loops[cite: 5, 6].

Architect enterprise Retrieval-Augmented Generation (RAG) pipelines with semantic chunking, Pinecone vector search, Cohere reranking, and citation grounding contracts[cite: 6, 7].

Coordinate specialized agents using sequential chaining, parallel lenses, critic loops, and hierarchical dispatching while containing cost and latency explosions[cite: 8, 9].

Design conversational voice systems using native speech-to-speech architectures, stateful routing, and voice UX principles[cite: 9, 10].

Structure agentic systems into Control, Execution, and Reliability layers. Master build-vs-buy decisions, state management, and real-time enterprise integrations[cite: 11, 26, 37].

Establish observability using Langfuse and LangFlow. Implement LLM-as-a-Judge rubrics, trace token/cost budgets, and debug production hallucinations[cite: 12, 13, 40].

Understand when to fine-tune versus prompt engineer or RAG. Curate JSONL datasets, train models via OpenAI Fine-Tuning API, and evaluate performance with DeepEval[cite: 14, 15].

Design, prototype, and defend an end-to-end agentic AI program. Includes working low-code workflows, vector grounding, cost/ROI modeling, and strategy deck[cite: 3, 15, 37].

Master the 5-step Agentic System Design framework, trade-off triangles (cost vs. latency vs. quality), rollout staging, and MAANG+ interview case studies[cite: 3, 19, 27, 37].

Comprehensive MAANG+ TPM domain interview prep: program planning, risk management, execution trade-offs, and distributed systems design[cite: 3, 29, 32, 37].

Comprehensive timed evaluation assessing end-to-end agentic AI architecture, multi-agent orchestration, RAG systems, and TPM trade-offs[cite: 45, 46].