Spencer Schneidenbach is the President and CTO of Aviron Labs, an AI and software development firm based in the United States. He has been recognized as a Microsoft MVP for his AI expertise and contributions to the community.
", "headline": "", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/RnonY1aQR3elgSZEu4TG_new%20pic%20face%202024%20latest%20good%20high%20res%20square.jpg", "highlights": null, "linkedin_url": null, "twitter_handle": null, "highlights_html": null, "image_url_no_bg": null, "custom_social_link": null}], "learning_outcomes": [{"uuid": "6f75d2ab-bd8f-4cec-b24f-f5fb007285e1", "title": "Discuss the basics of RAG, embeddings, vectors, and search", "description": "Learn the basics of AI search primitives and techniques, and how they fit into the big picture."}, {"uuid": "2f4d123c-ece1-435a-812b-697fbd6924a9", "title": "Implement Agent Memory with PostgreSQL", "description": "Learn how PostgreSQL stores agent state, conversation history, and embeddings for context and retrieval."}, {"uuid": "0dc3b985-e2d5-4e54-9288-1f22c6a28d5b", "title": "Perform RAG searches with your AI Agent", "description": "Master building your own RAG-enabled AI agents using Microsoft's Agent Framework"}]}], "created_at": "2026-02-03T22:59:23.706662Z", "updated_at": "2026-02-10T18:50:54.561507Z", "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": 16313, "course_stripe_promo_code_id": null}, "school_event": {"id": 389, "slug": null, "title": "Building RAG-Enabled Agents w/ Postgres and Agent Framework", "description": null, "start_datetime": "2026-02-17T19:00:00Z", "end_datetime": "2026-02-17T20:00:00Z", "start_date": "2026-02-17", "start_time": "14:00:00", "duration_min": 60, "timezone": "America/Indianapolis", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 80}, "relatedWorkshops": [{"id": 4100, "school_id": 3671, "is_visible_on_discovery_page": true, "is_canceled": false, "is_delisted": false, "connected_course_id": 3847, "workshop_tags": [{"id": 31, "tag_type": "category", "label": "Data & engineering", "slug": "engineering-ll", "description": null, "parent_tag_id": null}, {"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 257, "tag_type": "internal_collection", "label": "Building AI Systems", "slug": "building-ai-systems", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}, {"id": 315, "tag_type": "topic", "label": "Agentic AI", "slug": "agentic-ai", "description": null, "parent_tag_id": null}], "published_content_page": {"id": 4939, "school_id": 3671, "slug": "373e26", "content_page_type": "workshop", "sections": [{"uuid": "0a8b0121-e31e-4900-81f4-96c9ebe05dce", "title": "Build an AI Agent with Memory, Internet and Document Search", "version": 1, "image_url": "https://d2426xcxuh3ht5.cloudfront.net/3vIyYC8TrGEgUdp1ch9W_Lighting%20Lessons%20(2).jpg", "is_visible": true, "topic_desc": "Most AI agents today look great in demos but fail in real-world use. This session teaches you to build a Sales AI agent that retrieve, analyze, and collaborate intelligently. You'll master multi-agent systems, optimized retrieval, and real-world deployment—focusing on practical, scalable AI over hype. Join us to build agents that actually work in production! 🚀", "brand_logos": {"label": "Worked at", "logos": [{"name": "Google", "type": "logo", "uuid": "ab49125f-2bb6-4a79-963e-328297658250", "file_type": "svg", "image_url": "https://asset.brandfetch.io/id6O2oGzv-/idjOb2YcqW.svg"}, {"name": "Stanford University", "type": "logo", "uuid": "44110d6c-ab5c-48db-87f4-67217317e330", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idPv3iQPET/idBFw-L5SX.svg"}, {"name": "UCLA", "type": "icon", "uuid": "6eb8d0e6-3cd8-4b0c-87b3-8f358842e649", "file_type": "jpeg", "image_url": "https://asset.brandfetch.io/id8jxrwm3u/id-xuACPIm.jpeg"}, {"name": "Walmart", "type": "logo", "uuid": "06f30a12-c84a-4704-9e02-8fd4622639e1", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idoGsFQrHx/idbH0XUj9G.svg"}, {"name": "University of Minnesota", "type": "icon", "uuid": "aa7c9164-d618-48ba-a10e-ab5bda398019", "file_type": "jpeg", "image_url": "https://asset.brandfetch.io/idjDRN9GJw/idKkDQYSmt.jpeg"}]}, "section_type": "main", "instructor_infos": [{"name": "Hamza Farooq", "uuid": "76c5492d-d2ae-499d-baaf-2e9f81dd74fa", "title": "Founder & Adjunct Professor | 15+ years | Google | Stanford | UCLA", "bio_html": "I am the founder of Traversaal.ai, an LLM-based startup dedicated to building scalable, customizable, and cost-efficient AI solutions for enterprises. My work focuses on practical, production-ready AI—far from the flashy, overhyped demos that fail in real-world environments.
With over 15 years of experience in machine learning, my career has spanned three continents and seven countries, working across industries like tech, telecommunications, finance, and retail. As a former Senior Research Manager at Google and Walmart Labs, I have led teams specializing in optimization, NLP, recommender systems, and time series forecasting.
Currently, I serve as an adjunct professor at UCLA, where I bridge the gap between academic research and real-world AI applications. My passion lies in educating the next generation of AI practitioners and enabling organizations to build enterprise-grade AI solutions that are scalable and efficient.
Beyond academia, I have been recognized as a Top AI Voice on LinkedIn multiple times, with my work even being featured in Times Square. Last year, Meta awarded me a $100K AI Grant for my contributions to the field—further validating my commitment to advancing AI research and deployment.
I regularly speak at conferences, lead training sessions, and consult on AI strategy, covering topics like large language models, deep learning, cloud computing, and AI deployment at scale. My mission is simple: help organizations cut through the noise and build AI solutions that actually work in production.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/ehl6l8TCSTnDuBnDZ6wV_hamza_profile.jpg"}], "learning_outcomes": [{"uuid": "c2c26c24-f938-49b2-b983-d2df821b7aa6", "title": "Empowering Decision-Making with AI Agents", "description": "Multi-agent systems automate workflows, enhance collaboration, and provide real-time support for complex tasks."}, {"uuid": "5b5c78aa-7bb2-415b-83ea-dbc7b85da9b6", "title": "Streamlining Knowledge Access with Enterprise RAG", "description": "RAG breaks down data silos, enabling seamless access to Enterprise Knowledge for smarter, faster decision-making."}, {"uuid": "419b71f3-e2da-4347-b32d-42d465372f6b", "title": "Explore Practical Applications of AI Agents", "description": "Get hands-on insights into building AI agents from scratch, understanding their architecture, and deploying them."}]}], "created_at": "2026-02-12T21:02:59.501196Z", "updated_at": null, "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": 3847, "course_stripe_promo_code_id": null}, "school_event": {"id": 4939, "slug": null, "title": "Build an AI Agent with Memory, Internet and Document Search", "description": null, "start_datetime": "2025-09-05T16:00:00Z", "end_datetime": "2025-09-05T17:00:00Z", "start_date": "2025-09-05", "start_time": "12:00:00", "duration_min": 60, "timezone": "America/New_York", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 836}}, {"id": 7136, "school_id": 12454, "is_visible_on_discovery_page": true, "is_canceled": false, "is_delisted": false, "connected_course_id": 19252, "workshop_tags": [{"id": 31, "tag_type": "category", "label": "Data & engineering", "slug": "engineering-ll", "description": null, "parent_tag_id": null}, {"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 137, "tag_type": "internal_collection", "label": "Instructor", "slug": "Instructor", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 299, "tag_type": "persona", "label": "For Data Scientists", "slug": "for-data-scientists", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}, {"id": 397, "tag_type": "internal_collection", "label": "Learn, Grow & Level Up", "slug": "maven-hq", "description": null, "parent_tag_id": null}, {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}], "published_content_page": {"id": 8223, "school_id": 12454, "slug": "85ea43", "content_page_type": "workshop", "sections": [{"uuid": "3d8f9bb1-df7e-49f7-b6b1-17eaa93920b4", "title": "RAG Explained: The Architecture Behind Agentic AI Systems", "version": 1, "image_url": "https://d2426xcxuh3ht5.cloudfront.net/82skxh6jTeWiJYji9lU2_RAG%20Explained%20Maven%20Lightning%20Session%20Thumbnail%20(1).png", "is_visible": true, "topic_desc": "RAG has become the foundation of most real AI applications today. Instead of relying only on a model’s training data, modern systems retrieve relevant information from documents, databases, and company knowledge. \n\nUnderstanding RAG helps you design reliable AI assistants, copilots, and agentic systems that actually work in real organizational environments.", "brand_logos": {"label": "Previously at", "logos": [{"name": "Google", "type": "logo", "uuid": "bd90e139-da96-44a4-9e87-f0e31be8b37d", "file_type": "svg", "image_url": "https://asset.brandfetch.io/id6O2oGzv-/idjOb2YcqW.svg"}, {"name": "Microsoft", "type": "logo", "uuid": "4d8ee352-5aa2-4853-98d1-564cd70b8545", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idchmboHEZ/idc7I0n_oy.svg"}, {"name": "Adobe", "type": "logo", "uuid": "381aa7bc-4df4-4770-9f9a-f3dbfe51f2cc", "file_type": "svg", "image_url": "https://asset.brandfetch.io/id_KsyK7J9/idqgHTh9pg.svg"}, {"name": "Columbia University", "type": "logo", "uuid": "ff982450-ba2d-4f92-8036-07c7bd3ecee9", "file_type": "svg", "image_url": "https://asset.brandfetch.io/id7Qv27obq/idaqb00VYd.svg"}, {"name": "Carnegie Mellon University", "type": "logo", "uuid": "abca0375-f465-4aec-8ea1-ca05da5cdea2", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idWWVARKQG/id9SgYMrdN.svg"}]}, "section_type": "main", "instructor_infos": [{"name": "Aishwarya Srinivasan", "uuid": "9d997e6f-0b1b-42f5-8a45-2f101cc85bd0", "logos": null, "title": "AI Entrepreneur | Ex-Google, Microsoft", "bio_html": "Most Followed Indian Woman in AI | Founder @ The Gen Academy | AI Leader | Ex - Fireworks AI, Google, Microsoft, IBM
Aishwarya has built one of the most trusted AI communities in the world with over 1 Million followers who rely on her to cut through the nice.
She spent a decade building AI at the companies that defined the modern stack Google, Microsoft, and IBM. As the head of AI Developer Relations at Fireworks AI, she worked directly at the interface businesses, builders and the infrastructure powering production-scale AI systems. She holds a Master's in Data Science from Columbia University.
Aishwarya's teaching philosophy is simple: explain the real thing, not the sanitised version. Her sessions are known for being brutally honest, technically sharp, and remarkably clear.
", "headline": "", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/JKsVW5XCTXeJM7DwaRet_Headshot.png", "highlights": null, "linkedin_url": null, "twitter_handle": null, "highlights_html": null, "image_url_no_bg": null, "custom_social_link": null}, {"name": "Arvind Narayan", "uuid": "04f300ab-426d-4a62-b4d6-5c5959df6dcf", "logos": {"label": "", "logos": []}, "title": "AI Engineering Lead | Founder", "bio_html": "Founder @ The Gen Academy | AI Solutions Architect | Ex - Adobe, Microsoft, IBM
Arvind brings a rare cross-company perspective on AI at scale, built across Microsoft, IBM, and Adobe — where he led data science teams and developed production-grade ML solutions tied to significant ARR growth.
He currently serves as AI Solutions Architect at Ema, a leading AI startup focused on agentic enterprise transformation, and is Founder of Eikos Health, an agentic scheduling platform for doctor-led clinics.
Arvind holds a Master's from Carnegie Mellon University. His teaching style emphasizes practical thinking, strong mental models, and translating AI concepts into production-ready solutions.
", "headline": "", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/fUt42QbITCiAaJGL7gAp_Screenshot%202026-03-09%20at%2011.00.24%E2%80%AFPM.png", "highlights": [], "linkedin_url": "", "twitter_handle": "", "highlights_html": null, "image_url_no_bg": null, "custom_social_link": ""}], "learning_outcomes": [{"uuid": "6ce3e527-f96f-4fe9-8e78-6a2ded89cc76", "title": "Understand the fundamentals of RAG", "description": "Learn the core RAG architecture, why it matters, and how retrieval improves LLM reliability."}, {"uuid": "e1fcab2d-7d79-476a-ae04-5cdf183004cf", "title": "Learn the key RAG patterns used in modern AI applications", "description": "Understand common RAG patterns and when to use them in copilots, assistants, and agent systems."}, {"uuid": "0314a6eb-1041-4328-a484-18796b455afc", "title": "Understand how RAG systems are engineered at scale", "description": "Learn how teams design production RAG systems including evaluation, latency, and reliability."}]}], "created_at": "2026-03-10T05:31:17.805289Z", "updated_at": "2026-03-10T06:01:37.182143Z", "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": 19252, "course_stripe_promo_code_id": null}, "school_event": {"id": 8223, "slug": null, "title": "RAG Explained: The Architecture Behind Agentic AI Systems", "description": null, "start_datetime": "2026-04-04T15:00:00Z", "end_datetime": "2026-04-04T15:30:00Z", "start_date": "2026-04-04", "start_time": "08:00:00", "duration_min": 30, "timezone": "America/Los_Angeles", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 7795}}, {"id": 3605, "school_id": 11275, "is_visible_on_discovery_page": true, "is_canceled": false, "is_delisted": false, "connected_course_id": 12327, "workshop_tags": [{"id": 31, "tag_type": "category", "label": "Data & engineering", "slug": "engineering-ll", "description": null, "parent_tag_id": null}, {"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 137, "tag_type": "internal_collection", "label": "Instructor", "slug": "Instructor", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}, {"id": 315, "tag_type": "topic", "label": "Agentic AI", "slug": "agentic-ai", "description": null, "parent_tag_id": null}, {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}], "published_content_page": {"id": 4440, "school_id": 11275, "slug": "be969c", "content_page_type": "workshop", "sections": [{"uuid": "d6a7672f-c7d8-42da-9697-f3dbf68a2577", "title": "Rethinking RAG from first principles for agents", "version": 1, "image_url": "https://d2426xcxuh3ht5.cloudfront.net/dZ0q4MXJQ3q9vWGrHV4K_Single Instructor (15).png", "is_visible": true, "topic_desc": "As AI evolves from chatbots to agents, mastering agentic RAG is essential for next-gen applications. Students will gain competitive advantage learning agent-first design, moving beyond traditional RAG to build autonomous, composable systems that unlock greater productivity.", "brand_logos": {"label": "worked with", "logos": [{"name": "Sourcegraph", "type": "logo", "uuid": "83ea1641-e670-4239-90ba-d7b1f67dd0a8", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idXufuq8Fq/idqhit7vgt.svg"}, {"name": "Stitch Fix", "type": "logo", "uuid": "e52fcf3b-6edc-4011-9bd1-25d906475ce4", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idTgrRl8nA/idVnuVYfek.svg"}, {"name": "Meta", "type": "logo", "uuid": "2519e784-23b7-4033-acee-98da5f9efa29", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idWvz5T3V7/idpvCl_Ipb.svg"}, {"name": "University of Waterloo", "type": "logo", "uuid": "abb9fe69-6827-4a57-8bb6-54d6e67302ed", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idIfIi7wMZ/ida2LuNMbf.svg"}, {"name": "New York University", "type": "logo", "uuid": "e444f34d-68e8-49a3-a888-30852c3dc94d", "file_type": "svg", "image_url": "https://asset.brandfetch.io/idMj8iq0ev/idShvt3ozm.svg"}]}, "section_type": "main", "instructor_infos": [{"name": "Jason Liu", "uuid": "0d88f6fe-40e5-4433-800d-4f6a9725dfe1", "logos": null, "title": "Consultant at the intersection of Information Retrieval and AI", "bio_html": "Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/6J9YUnrSeCIxZ24pyoVg_Jason.png", "highlights": null, "linkedin_url": null, "twitter_handle": null, "image_url_no_bg": null, "custom_social_link": null}, {"name": "Beyang Liu", "uuid": "1410edc0-bfe8-402b-a5fa-34c626da11f4", "logos": {"label": "", "logos": []}, "title": "CTO and co-founder of Sourcegraph", "bio_html": "As CTO and co-founder of Sourcegraph, Beyang led technical development of the first widely available code search engine to use semantic and trigram indexing and the first retrieval-augmented coding assistant, Cody. Both these products are now widely used in the Fortune 500. Prior to that, Beyang was an early engineer at Palantir where he helped introduce Apache Spark and conducted machine learning research under Professor Daphne Koller at Stanford. These days, Beyang is working on Amp, a coding agent built from the ground up to take full advantage of today's frontier LLMs.
", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/7WpR4rX0RUKZikXHu63B_Beyang Liu.jpeg", "highlights": [], "linkedin_url": "", "twitter_handle": "", "image_url_no_bg": null, "custom_social_link": ""}], "learning_outcomes": [{"uuid": "a06038cb-a94f-4606-9548-6c435be9174a", "title": "Agent vs. Chatbot Architecture Differences", "description": "Students will identify key structural differences between agent and chatbot systems regarding context control."}, {"uuid": "39c06fdc-5605-4d94-8b40-a514e7d0cb73", "title": "Agentic RAG Implementation Strategies", "description": "Students will design simpler retrieval systems optimized for agents over complex ranking engines."}, {"uuid": "9e587d8f-8315-4fbd-8d88-72e40a23c3d7", "title": "Multi-Agent System Composition Techniques", "description": "Students will apply multi-agent composition patterns to overcome context window limitations."}]}], "created_at": "2026-02-12T21:01:39.943369Z", "updated_at": null, "theme": null, "attrs": {"type": "workshop", "is_canceled": false, "is_delisted": false, "connected_course_id": 12327, "course_stripe_promo_code_id": null}, "school_event": {"id": 4440, "slug": null, "title": "Rethinking RAG from first principles for agents", "description": null, "start_datetime": "2025-07-22T18:00:00Z", "end_datetime": "2025-07-22T19:00:00Z", "start_date": "2025-07-22", "start_time": "14:00:00", "duration_min": 60, "timezone": "America/Toronto", "has_internal_recording": true, "is_recording_public": true}, "num_signups": 1102}}], "exploreCourses": [{"objectID": "10144", "school_id": 3671, "school_name": "Hamza Farooq", "school_slug": "boring-bot", "course_id": 10144, "course_name": "Agent Engineering Bootcamp: Developers Edition", "course_slug": "advanced-llm", "course_description": "Master Advanced Techniques for Building and Optimizing Agentic RAG Systems and Multi-Agent Workflows — Designed for Builders", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/URmm4vvLS1GXofMY4boq_Copy of Lighting Lessons (2).jpg", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Hamza Farooq", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/A49XTCbmRqGdgTdh59aw_profile_pic-removebg-enhanced.jpg", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/A49XTCbmRqGdgTdh59aw_profile_pic-removebg-enhanced.jpg", "uuid": "1bd3c4e1-5047-4ce1-a028-e5403366460c", "bio_html": "I am the founder of Traversaal.ai, an LLM-based startup dedicated to creating scalable, customizable, and cost-efficient language model solutions for enterprises.
With over 15 years of experience in machine learning, my journey has spanned three continents and seven countries, covering a diverse range of industries such as tech, telecommunications, finance, and retail.
As a former Senior Research Manager at Google and Walmart Labs, I have led data science and machine learning teams, focusing on optimization, natural language processing, recommender systems, and time series forecasting.
I am also an adjunct professor at UCLA, and Instructor for Stanford Continuing Studies where I bridge the gap between academic theory and real-world AI applications.
", "headline": "Founder | Ex-Google | Prof UCLA & UMN", "title": "Founder | Ex-Google |\nAdjunct UCLA & UMN, SCU | Venture Partner", "linkedin_url": "https://www.linkedin.com/in/hamzafarooq/", "twitter_handle": null, "custom_social_link": "https://substack.com/@boringbot", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/id6O2oGzv-/idjOb2YcqW.svg", "type": "logo", "file_type": "svg", "uuid": "71831ee4-c440-494f-b943-5160f636635c", "name": "Google"}, {"image_url": "https://asset.brandfetch.io/idoGsFQrHx/idfnjZQYMT.svg", "type": "logo", "file_type": "svg", "uuid": "feb0f776-03d1-4afa-8f74-c1faf464eb9a", "name": "Walmart"}, {"image_url": "https://asset.brandfetch.io/idjDRN9GJw/idKkDQYSmt.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "c1616b08-92ab-45c5-aa6d-7164a12c2430", "name": "University of Minnesota"}, {"image_url": "https://asset.brandfetch.io/id7iVZsi3j/idLGzrYc13.png", "type": "logo", "file_type": "png", "uuid": "8f81df14-18ca-4630-a365-c435cdf4a334", "name": "Stanford Continuing Studies"}, {"image_url": "https://asset.brandfetch.io/id8jxrwm3u/id-xuACPIm.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "12f4c1a1-3892-469b-9f7c-75cdbef07fb8", "name": "UCLA"}], "label": "Worked at:"}, "highlights": null, "highlights_html": "UCLA Anderson School of Management – Designed and taught Machine Learning System Design in the #1 globally ranked MSBA program (QS).
Author of \"Building an LLM Application from Scratch\" – A hands-on guide that’s become a practitioner’s bible for RAG and agentic systems.
Global Impact – Taught 10,000+ students worldwide through open education initiatives, with projects featured in Times Square twice.
Industry Expertise – Led AI projects with Google, and worldwide, including building secure Enterprise RAGs and regional language models
Bootstrapped Founder – CEO & Founder of Traversaal.ai, leading an 18-engineer global team building enterprise-grade AI agents
I build and teach AI, with a focus on practical, developer-first machine learning systems and modern AI infrastructure.
I am a Staff AI/ML Engineer - DX at Together AI, where I work at the intersection of large-scale models, tooling, and education to help engineers and teams effectively build with AI.
Previously, I worked on vector databases at Weaviate and earlier as a data scientist across applied industry roles.
I am an instructor with over 200,000 students across LinkedIn Learning, Coursera, and DeepLearning.AI, where I specialize in making advanced AI concepts accessible to practitioners. I’m deeply passionate about open source software, machine learning, and technical education, and to share that passion I’ve given talks at international conferences worldwide.
6 live sessions · 6 office hours · 1 demo day · 20+ hours
⚡ Agent Harness Design: Build a observable perceive–think–act loop in Python with structured tracing, evolving from simple LLM calls to full agent systems with tools, reflection, and planning
⚡ Agentic RAG Systems: Create advanced RAG pipelines with routing, multi-hop reasoning, Knowledge Graph, and semantic caching to improve accuracy and efficiency
⚡ Real-Time Voice Agents: Engineer a streaming STT → LLM → TTS pipeline with turn-taking, interruptions, and sub-second latency across modern voice platforms
⚡ Multi-Agent Orchestration: Design coordinated agent systems using MCP and A2A with patterns like hierarchical, debate, and orchestrator-worker workflows
⚡ Guardrails & Evaluation: Build reliable systems with safety guardrails and trajectory-based evaluation using LLM-as-judge and validated benchmark tasks
⚡ Production Deployment: Ship multi-agent systems with Google ADK, MCP, A2A, Llama Guard, and GCP monitoring
👉 This bootcamp is for engineers who ship real agent systems
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "23333c84-3ccd-493a-8658-0de652351133", "description_html": "Machine Learning Engineer exploring different techniques to scale LLM solutions
"}, {"uuid": "0f549ecc-2c77-4d4b-a459-c27d0920c519", "description_html": "Researcher, who would like to delve in to various aspects of open-source LLMs
"}, {"uuid": "131cf11c-e130-478b-9c53-5a76405f0a1a", "description_html": "Software Engineer, looking to learn how to integrate AI into their products
"}]}, "upmarket_clp_prerequisites": {"is_visible": false, "items": [{"uuid": "791eb04f-7e19-4cab-abc6-af85c0f85542", "name": "", "description": ""}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "Master Advanced Techniques for Building and Optimizing Agentic RAG Systems and Multi-Agent Workflows — Designed for Builders", "items": [{"uuid": "1d755fcf-3dc5-4528-b0bc-8674b2956e78", "title": "Build and Debug Production Agent Harness", "items": ["Outcome: Engineer ReAct agents with structured tracing, decision auditing, memory, and termination control.", "Framework: Implement perceive-think-act loops, planning agents, reflection, and tool-use architectures.", "Hands-on: Building a production-grade agent harness and unpacking what makes Claude Code powerful beyond the model."]}, {"uuid": "5cc3d0c4-f565-4061-9955-45cf0f14267d", "title": "Deploy and Optimize LLM Inference", "items": ["Outcome: Serve production LLMs with predictable latency, lower cost, and benchmarks you can defend.", "Deep dive: Master GPTQ, GGUF, QLoRA, AWQ, prefill, decode, KV caching, and speculative decoding.", "Infrastructure: Measure TTFT, inter-token latency, throughput, and the economics of each deployment choice."]}, {"uuid": "e53b140b-a579-4d89-8f9b-fa0643b620a5", "title": "Ship Agentic RAG with Graphs and Semantic Cache", "items": ["Outcome: Build retrieval systems that route, reason, adapt, and move beyond naive top-k search.", "Framework: Implement retrieval-as-a-tool, query planning, multi-hop reasoning, and semantic chunking.", "Advanced: Add Graph RAG and semantic caching to improve reasoning quality, cost, and latency."]}, {"uuid": "c30110b1-87fe-48cd-8815-70e67e3479b6", "title": "Build Voice Agents That Feel Alive", "items": ["Outcome: Engineer real-time voice agents with sub-second responses and natural conversational flow.", "Architecture: Build streaming STT, LLM, and TTS loops with turn-taking, end detection, and barge-in.", "Tools: Compare Deepgram, ElevenLabs, OpenAI Realtime, Vapi, and Retell across latency, quality, and cost."]}, {"uuid": "f575fe88-c670-4fca-83c5-94b6134aa5e7", "title": "Operate Multi-Agent Systems with Real Coordination", "items": ["Outcome: Design multi-agent systems with clear topology, communication protocols, and operational judgment.", "Patterns: Implement orchestrator-worker, hierarchical, debate, and handoff workflows for real use cases.", "Protocols: Connect agents with MCP and A2A, and evaluate when single-agent systems are the better choice."]}, {"uuid": "7cbc1001-2c73-4edc-95a7-350664dc4bc5", "title": "Evaluate, Guardrail, and Operate with Confidence", "items": ["Outcome: Ship agent systems with measurable quality, layered safety, and production-ready eval loops.", "Guardrails: Implement Llama Guard, NeMo Guardrails, validation, and prompt-injection defenses.", "Evals: Build trajectory-based evals with golden tasks, partial-credit grading, and LLM-as-judge scoring."]}]}, "vector_string": null, "vector": null, "score": 1.827132}, {"objectID": "15495", "school_id": 14003, "school_name": "Cheat at Search", "school_slug": "softwaredoug", "course_id": 15495, "course_name": "Cheat at Search with Agents", "course_slug": "cheat-at-search", "course_description": "Rethink search to put tireless agents at work for human users", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/8TD7KI2RQWhiyPfPHyeB_cheat-at-search-brain2.png", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Doug Turnbull", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/rDTq3o4ZSc6qb0PmwzFv_dougie-in-snuggie-8-bit.png", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/rDTq3o4ZSc6qb0PmwzFv_dougie-in-snuggie-8-bit.png", "uuid": "a012f0df-ce5b-4b90-abfe-c7b7d60f3da8", "bio_html": "In 2012, Doug got bit by the search bug and he's still trying to keep up. From full-text search, to Learning to Rank models, to search agents that generate their own code, he knows the endless landscape first hand. Yet Doug wants to deeply understand the what / how / why, and help teams use these technologies practically, distinguishing hype from reality.
", "headline": "http://softwaredoug.com", "title": "Snuggie and Search Enthusiast", "linkedin_url": null, "twitter_handle": null, "custom_social_link": null, "logos": {"logos": [], "label": null}, "highlights": null, "highlights_html": "He’s led search at Reddit, Shopify, and Wikipedia, authored Relevant Search and AI Powered Search, and advised 100+ organizations over the years - all in pursuit of the same question: how does search actually work?
Keyword search + an agent - is that all we need now?
The agentic world challenges traditional ways of building search. Traditionally we've built thick, complex search APIs - query understanding, rerankers, learning to rank, vector databases. Yet an agent can compete in deep research benchmarks with the dumbest of search tools.
This course pushes you to rethink search. We teach:
Replacing search with agents - A search agent learns about retrieval systems, tailoring searches, achieving competitive results
Guiding agents, not algorithms - Search developers won't train algorithms. They'll build harnesses
Steering with an LLM judge - Integrate human and clickstream feedback to guide the agent's judgment towards what users want
Autoresearching to smarter search - Use + build a coding agent that optimizes retrieval code like an ML model.
Come learn alongside search industry expert Doug Turnbull as we unpack how agents will rewrite the search industry.
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "61fe4ac0-6f1a-47d2-ae9f-b30ad07159ec", "description_html": "Search technologists that want to learn how to ensure RAG and agentic search win
"}, {"uuid": "239b3729-6f70-4bf1-a54b-6166b5b6468d", "description_html": "Startups eager to get a fast start with a modern approach to search, sidestepping old, outdated practices
"}, {"uuid": "f3f1e283-92e2-47f0-8e6f-9399278393f7", "description_html": "Data scientists that want to consider how agentic / LLM / RAG based search can be oriented towards user feedback and business outcomes
"}]}, "upmarket_clp_prerequisites": {"is_visible": true, "items": [{"uuid": "2d080d7f-2bab-41a3-a1d3-4cb93ef55066", "name": "Lexical Search basics (do you know what BM25 is)", "description": "We will build on top of existing, core search knowledge"}, {"uuid": "efa08f09-c791-4ee4-a2fc-d9ae53ebf749", "name": "Basic search evaluation (judgments + metrics)", "description": "We will use common search datasets that use metrics like NDCG, etc to measure search"}, {"uuid": "f762411b-e2ce-4352-9fee-ee55b4d54f51", "name": "Vector search basics (do you know what an embedding is?)", "description": "Vector search will be one tool in our toolkit we'll apply to the agentic tools that we build"}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "Rethink search to put tireless agents at work for human users", "items": [{"uuid": "79cd1921-491e-4640-a850-339928dc8d54", "title": "What makes a great search tool to an agent?", "items": ["Build search backends agents can reason about", "Why agents love simplicity over complexity", "Focusing tools on domain tasks, not \"search\""]}, {"uuid": "5a6450da-9dc6-4eff-bfee-91e93ba3bb1c", "title": "Use LLMs to help agents structure, organize, and find content", "items": ["Sidestep complex NLP to better understand queries", "Iterate on query understanding through agentic approaches", "Organize and structure content to be retrievable by an agent"]}, {"uuid": "9be3fa65-b762-4c02-be6a-cbfaa4abc0c4", "title": "Agentic results without the cost", "items": ["How to integrate agentic approaches into a traditional search stack", "Best practices to manage the agentic loop to save tokens, money, and prevent context rot", "Technical approaches to extracting agentic insights (ie code generation)"]}, {"uuid": "d9250325-eec8-4bc6-834d-35261dce9e06", "title": "Orienting agentic search towards the human, not the machi", "items": ["An agent will by default use its judgments to iterate on relevance on its own. But shouldn't it use humans?", "Integrating external feedback (user clicks, etc) into agentic relevance", "Beyond LLM as a Judge to exploring how to iterate and improve retrieval for agents and humans"]}]}, "vector_string": null, "vector": null, "score": 1.8263116}, {"objectID": "17433", "school_id": 15363, "school_name": "AI-Powered Search", "school_slug": "search-school", "course_id": 17433, "course_name": "AI-Powered Search: Modern Retrieval for Humans & Agents", "course_slug": "ai-powered-search", "course_description": "Learn the latest AI Search skills with the authors of \"AI-Powered Search\"", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/6CuZOz7iRwC6dJ03szwS_6.jpg", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Trey Grainger", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/b9WUgJuUS0CTF1uKoCMI_trey-maven.png", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/b9WUgJuUS0CTF1uKoCMI_trey-maven.png", "uuid": "4ccdaf7f-3a29-40ad-ac23-69f0415a154b", "bio_html": "Trey Grainger is lead author of the book AI-Powered Search (Manning 2025) and founder of Searchkernel, a software consultancy building the next generation of AI-powered search. He also serves as a technical advisor at OpenSource Connections. He previously served as CTO of Presearch, a decentralized web search engine, and as Chief Algorithms Officer and SVP of Engineering at Lucidworks, a search company whose technology powers hundreds of the world’s leading organizations. Trey is also co-author of the book Solr in Action (Manning 2014), as well as over a dozen other publications including books, journals, and research papers. Trey has 18 years of experience in search and data science focused on building self-learning search platforms integrating the most successful AI Search techniques.
", "headline": "AI Search Expert | Founder | Author", "title": "Founder, Searchkernel. Author, \"AI-Powered Search\". 18+ years leading search.", "linkedin_url": "", "twitter_handle": "", "custom_social_link": "", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/idskYMg_Lc/idsgrCljLm.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "b75ee738-3f6b-4bfc-9aa1-0f462b98b234", "name": "Lucidworks"}, {"image_url": "https://asset.brandfetch.io/idTCWdN3V2/idDlXTsZYw.svg", "type": "logo", "file_type": "svg", "uuid": "22f6351e-326f-43b6-8e4f-df7d6dbaa781", "name": "Presearch"}, {"image_url": "https://asset.brandfetch.io/id_QgB787m/idRRMNVm56.png", "type": "logo", "file_type": "png", "uuid": "01134be2-30de-434c-950c-5e6307cc3044", "name": "Optimized AI Conference"}, {"image_url": "https://asset.brandfetch.io/idRrcMU04X/idPe8dAyv6.svg", "type": "logo", "file_type": "svg", "uuid": "f09015eb-7439-485a-83c9-ef32ca843ed4", "name": "CareerBuilder"}, {"image_url": "https://asset.brandfetch.io/idJTONuDZB/idvnLUMzvj.jpeg", "type": "icon", "file_type": "jpeg", "uuid": "0d22dbbe-8b23-4c84-9db7-9c9679b4dc05", "name": "Searchkernel"}], "label": ""}, "highlights": null, "highlights_html": "Since 2010, Doug Turnbull has worked with companies such as Shopify, Careerbuilder, Wikimedia Foundation, Shipt, and LexisNexis on improving search experiences. Doug coaches and develops search teams from startups to Fortune 500 companies, setting up organizational practices to build relevant search. Doug wrote “Relevant Search” (2016) and co-authored “AI Powered Search” (2022). He also co-created the Elasticsearch Learning to Rank, bringing machine learning to the most popular search engine, which revamped Wikipedia and Yelp, driving intelligent search behind dozens of companies' search experiences.
", "headline": "Principal Search Engineer | Author", "title": "Principal Search Engineer, Author of \"AI-Powered Search\" & \"Relevant Search\".", "linkedin_url": "", "twitter_handle": "", "custom_social_link": "", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/idAgPm7IvG/id85L14isQ.svg", "type": "logo", "file_type": "svg", "uuid": "c9cf80b5-b0bc-426e-b0eb-d91807bb1e55", "name": "Shopify.com"}, {"image_url": "https://asset.brandfetch.io/id2VbA0wwD/idv8cqA42F.png", "type": "icon", "file_type": "png", "uuid": "d635656d-e32e-4a9f-b69e-e7edd208531b", "name": "OpenSource Connections"}, {"image_url": "https://asset.brandfetch.io/idkKwm0IT0/idklqyFZ_8.svg", "type": "logo", "file_type": "svg", "uuid": "705bfccc-13c4-47cf-ae4d-b681871bd8c9", "name": "Reddit"}, {"image_url": "https://asset.brandfetch.io/idAo3WRIoq/idpY-V37uF.svg", "type": "logo", "file_type": "svg", "uuid": "f5985ff7-9cc4-4b06-b676-00562d7208c1", "name": "Wikipedia"}, {"image_url": "https://asset.brandfetch.io/idvwtgSsRX/idESHQtUtB.svg", "type": "logo", "file_type": "svg", "uuid": "f2db2ed5-cce4-4751-bf87-e95fc887c870", "name": "Yelp"}], "label": "\n"}, "highlights": null, "highlights_html": "AI Search is among of the most in-demand AI Skills. AI systems need RAG, and implementing great retrieval can often have a bigger impact than prompt engineering for improving your AI and Agentic systems.
Most search systems barely scratch the surface of what's possible. While others struggle with irrelevant results and frustrated users, you'll master the cutting-edge techniques that power the world's most successful search experiences—from Reddit and Spotify to the Fortune 500.
Why This Course Exists
The Problem: Many traditional search systems are stuck in the 2010s. But modern AI courses hyper focus on text embeddings and ignore many of the fundamentals of information retrieval. The result? Engineers who can't bridge the gap between amazing demos and production systems that actually work.
Our Solution: Stop downloading random embeddings from Hugging Face or OpenAI and hoping for the best. Learn to systematically analyze your users' actual intent, integrate domain context, and build search that truly understands what people (and agents) need—not just what they type.
This course helps you build cutting-edge AI Search systems that actually work.
Ready to level-up your AI Search skills?
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "b7476dc1-88ae-4854-a239-1a2de5d2232d", "description_html": "AI Engineers optimizing search ranking to measurably improve user search satisfaction
"}, {"uuid": "e4ffbbb2-fbe6-4cf9-a902-87968c588f18", "description_html": "Search Practitioners that want to learn to systematically improve search relevance
"}, {"uuid": "7a159c00-ca40-4b27-99dd-90bfc26f2c42", "description_html": "Technology leaders hoping to plan search experiments to maximize clicks and conversions
"}]}, "upmarket_clp_prerequisites": {"is_visible": true, "items": [{"uuid": "21de8992-2ef8-4e3f-bac8-24efcfd14517", "name": "Basic Python Programming", "description": "Examples will be taught in Python, it's a good idea to have basic Python familiarity under your belt"}, {"uuid": "1cf5c579-e07d-41df-aa33-6718ad62fb85", "name": "(Included Complimentary with the Course): Search Fundamentals", "description": "For those new to search/retrieval, we provide an optional intro session covering core matching & ranking concepts (BM25, vector spaces, etc)"}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "Learn the latest AI Search skills with the authors of \"AI-Powered Search\"", "items": [{"uuid": "9545e36d-c629-4eaa-9850-e6405ef57853", "title": "Optimize your Agentic Search and Retrieval Augmented Generation (RAG) systems", "items": ["Understand emerging Agentic Search strategies, implement them with the Agentic Search loop, and be at the forefront of this paradigm shift.", "Optimize search for LLMs and agents, as well as the critical strategies (chunking, adaptive queries, guardrails, etc.)", "RAG represents the largest growing use case for search, and \"retrieval\" is by far the hardest part of RAG. "]}, {"uuid": "8dede459-118b-4f26-9c6d-0d061f46eb99", "title": "Go beyond the query to understand intent", "items": ["Learn how to integrate your content's context, your domain context, and your users' individual contexts to optimally interpret query intent.", "We'll cover query classification, query-sense disambiguation, and semantic query parsing, using multiple lexical and LLM-based strategies.", "Dig into emerging techniques like Wormhole Vectors, sparse lexical / expansion models (Semantic Knowledge Graphs and SPLADE), and MUVERA."]}, {"uuid": "cfde0238-91e7-47f9-9fe1-b4efccb4e0aa", "title": "Fill in holes in your modern information retrieval knowledge", "items": ["The field of information retrieval is vast. Learn the mental models that tie it all together. ", "Understand when to use bi-encoders vs. cross-encoders vs. knowledge graphs, and ANN vs. quantization vs. representation learning.", "Understand multimodal and hybrid search, search on semi-structured embeddings, and how to properly mix sparse + dense retrieval techniques."]}, {"uuid": "e78585b0-3729-4942-a29e-2b90eb009524", "title": "Improve relevance with crowdsourced ranking models built from click-stream data", "items": ["Leverage \"reflected intelligence\" from your use behavioral signals to automatically improve your ranking models.", "Learn to implement algorithms for signals boosting, matrix factorization, and knowledge graph learning", "Implement personalized search by building real-time user behavioral embeddings leveraging matrix factorization / collaborative filtering."]}, {"uuid": "549c0f6c-4579-4cf8-a7bd-7fc5b97bb79a", "title": "Optimize AI-Powered Search Models for Production", "items": ["Learn common Machine-Learned Ranking (Learning to Rank) models put into production, from LambdaMART to cross-encoders", "Get hands on experience performing feature engineering and representation learning to optimizing AI Search for production.", "Implement ANN and quantization strategies, reranking, semantic caching, and even local model serving for search and RAG."]}, {"uuid": "cd8c166b-eb72-47e5-bf42-043f56414e2b", "title": "Build ranking features & automate judgements with click models & Active Learning", "items": ["Learn how to systematically develop great ranking features using a combination of vector and lexical search indices.", "Generate automatic judgments from user click stream data using Click Models", "Overcoming ranking biases (presentation, position, & confidence bias) + uncover unexplored ranking features using SDBNs and Active Learning."]}]}, "vector_string": null, "vector": null, "score": 1.8244655}, {"objectID": "16876", "school_id": 14990, "school_name": "Carmelo Iaria", "school_slug": "carmelo-iaria", "course_id": 16876, "course_name": "Become an Agentic Architect", "course_slug": "agentic-architect", "course_description": "This is a project-based course. In 6 weeks, you will learn a repeatable framework and build your multi-agent AI application ready to launch.", "clp_hero_media": {"media_type": "image_url", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/ABACG2XiSgOanhSP14C3_Mastering%20Multiagent%20AI%20Systems%20with%20CrewAI.png", "youtube_video_id": "dQw4w9WgXcQ", "vimeo_video_id": null}, "instructor_infos": [{"name": "Carmelo Iaria", "image_url": "https://d2426xcxuh3ht5.cloudfront.net/hYK5HXqWT5mflHTEG9kt_carmelo%20iaria%20maven%20photo%20black%20background%20EDIT.jpeg", "image_url_no_bg": "https://user-assets.maven.com/instructor-no-bg/hYK5HXqWT5mflHTEG9kt_carmelo%20iaria%20maven%20photo%20black%20background%20EDIT.jpeg", "uuid": "73fd2a3a-a003-4a59-8a7e-30bd0d46e1eb", "bio_html": "I help engineers, product leaders, and AI builders move from fragmented experimentation to reliable multi-agent delivery. After 30+ years across Europe, the US, and Brazil, my focus is practical: helping teams design systems that ship with quality, not just demos that look good once.
I teach a repeatable operating model for agentic systems using AAMAD - from scope and context design to architecture, debugging, and delivery checkpoints. In this course, students build real capstone outcomes with coaching, reviews, and implementation support.
", "headline": "Pearson SME, O'Reilly teacher, ex-Cisco", "title": "Agentic Architect, Pearson SME, Startup Founder, 30 years of global experience", "linkedin_url": "https://www.linkedin.com/in/carmeloiaria/", "twitter_handle": null, "custom_social_link": "https://www.synapticai.consulting/", "logos": {"logos": [{"image_url": "https://asset.brandfetch.io/ida_xaMYlM/idHVJB4Rx2.svg", "type": "logo", "file_type": "svg", "uuid": "94709449-f093-4d61-a6b2-45adfb7c116b", "name": "Cisco"}, {"image_url": "https://asset.brandfetch.io/idnSdYFIv9/idFRaZ4Qkh.svg", "type": "logo", "file_type": "svg", "uuid": "3d482e94-67c3-41dd-8d6a-4c1a4ba4aaf5", "name": "Claro Brasil"}, {"image_url": "https://asset.brandfetch.io/idGmZRhyA9/idF0Ef8U9S.png", "type": "logo", "file_type": "png", "uuid": "3ccd1a95-4d08-49e0-bb68-997481a20194", "name": "Pearson"}, {"image_url": "https://asset.brandfetch.io/id-t1egjoa/idwSIOnmW9.svg", "type": "logo", "file_type": "svg", "uuid": "f4389a9c-37fc-41a2-80e5-14c0b96832cc", "name": "O'Reilly"}], "label": "\n"}, "highlights": null, "highlights_html": "Core Credentials
30+ years in technology leadership across Europe, Silicon Valley, and Brazil
Product leadership at Cisco (12 years) and innovation leadership at Claro Brasil (8 years)
AI education and certification SME with Pearson; contributor in O’Reilly learning ecosystem
Founder and AI strategy advisor helping organizations implement production-oriented agentic workflows
Built enterprise AI squads and delivery frameworks adopted in real client environments
Education & Expertise
MSEE Telecommunications, Politecnico di Torino
Data Scientist Professional, Datacamp
Human-Centered Service Design , IDEO
Several GenAI courses, Deeplearning.ai
This course is what you need to stay relevant — and protect your career.
Why this course matters
In 2026, engineering value is shifting from task execution to orchestration and systems thinking. Anthropic’s research shows that being a software engineer increasingly means orchestrating agents that write code, evaluating their output, and ensuring the system solves the right problems—not just writing more code yourself. The gap between early adopters and everyone else is widening fast.
You will leave with a production-ready capstone architecture, reusable templates, and a repeatable method you can apply immediately to your projects.
What alumni say
Builders from AT&T, HP, Morgan Stanley, T-Mobile, Cognizant, Experian have applied the method I teach to build amazing multi-agent applications.
Matt (Founder, SAVANT-AI): \"Five stars for course logic, course structure, and course value.\"
Alexa (Lead UX Designer, Caseware): \"I can already see how to bring it into my work.\"
Satyan (Group PM, AT&T): \"The concepts taught here make the difference between building a product that works and one that doesn't.\"
Next Cohort Enrollment closes May 31
NOTE: Apply for a Need-Base Scholarship by Sunday May 24th
"}, "upmarket_clp_target_audience": {"items": [{"uuid": "24530b0e-4898-4277-a631-02911657215c", "description_html": "Technical Builders (software, AI/ML, data, platform) moving from demos to reliable delivery..
"}, {"uuid": "0f8a50b2-1850-4988-9e10-6efc14b5ef19", "description_html": "Product and Business Owners (PMs, founders, consultants) responsible for outcomes, priorities, and execution quality.
"}, {"uuid": "5b0da355-5093-416d-9562-c7f76b2605b6", "description_html": "UX and Experience Leaders (UX, service design, research) shaping human-agent workflows, trust, and adoption.
"}]}, "upmarket_clp_prerequisites": {"is_visible": true, "items": [{"uuid": "2d0af03c-8c17-42c6-9c49-04ae9ad81b58", "name": "Experience & Background", "description": "Own delivery outcomes in tech/product/design/consulting. Read-level Python is helpful; pair with a technical teammate if needed."}, {"uuid": "7a5bc83d-d1f6-4968-88c3-d2b1b09a4726", "name": "APIs & Cloud Platforms", "description": "Comfort with Git/GitHub, API basics, and AWS (or similar). If rusty, budget extra setup time in Weeks 1-2."}, {"uuid": "08c19971-33c2-42a1-9d6e-ca856699dfae", "name": "Required Tools & Access", "description": "Agentic IDE (Cursor, Claude Code, GitHub Copilot), OpenAI API credits (~$20+), GitHub account, AWS access (recommended)"}]}, "upmarket_clp_outcomes": {"is_visible": false, "description": "This is a project-based course. In 6 weeks, you will learn a repeatable framework and build your multi-agent AI application ready to launch.", "items": [{"uuid": "10911d2e-ba79-4cae-9711-78b5a3522107", "title": "Define a solid foundation with clear research and project scope", "items": ["Ship rock‑solid project docs: research + requirements that AI agents can execute on.", "", ""]}, {"uuid": "ff3d5d59-bfe0-4a9a-a4fe-fd7ffbf1e86a", "title": "Architect your complete multiagent solution — from roles to workflows", "items": ["Architect a production‑ready multi‑agent system with clear roles, workflows, and docs.", "", ""]}, {"uuid": "f18a0ac8-aebf-4e85-a071-7375dfc5bf1d", "title": "Design and build a production-ready Agentic AI Application", "items": ["Design and build one production‑ready Agentic AI application, not just a prototype.", "", ""]}, {"uuid": "600500d0-2123-45b8-b86a-e5663220034f", "title": "Define and configure your multiagent crew — from backend logic to integrations", "items": ["Configure your full multi‑agent stack in CrewAI: Agents Personas, Tasks, Tools, and flows.", "", ""]}, {"uuid": "aa81d3fa-8322-4a2d-8837-90370ee444f6", "title": "Add enterprise-level quality: integrate human feedback, observability, and QA", "items": ["Add enterprise‑grade reliability: HITL, observability, and automated QA for agents.", "", ""]}, {"uuid": "788bca27-5d77-4b9f-92b0-dcefb620c1f6", "title": "Deploy, validate, and present your final Capstone solution with confidence", "items": ["Deploy and present a portfolio‑ready capstone you can show to clients and hiring managers", "", ""]}]}, "vector_string": null, "vector": null, "score": 1.8193274}], "workshopTags": [{"id": 159, "tag_type": "internal_collection", "label": "LL non-instructor", "slug": "ll-non-instructor", "description": null, "parent_tag_id": null}, {"id": 31, "tag_type": "category", "label": "Data & engineering", "slug": "engineering-ll", "description": null, "parent_tag_id": null}, {"id": 35, "tag_type": "category", "label": "AI", "slug": "ai-ll", "description": null, "parent_tag_id": null}, {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}, {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, {"id": 299, "tag_type": "persona", "label": "For Data Scientists", "slug": "for-data-scientists", "description": null, "parent_tag_id": null}, {"id": 315, "tag_type": "topic", "label": "Agentic AI", "slug": "agentic-ai", "description": null, "parent_tag_id": null}, {"id": 313, "tag_type": "topic", "label": "AI", "slug": "ai", "description": null, "parent_tag_id": null}], "videoChapters": [{"title": "Introduction and Casual Banter", "start_seconds": 0}, {"title": "Webinar Overview: RAG Agents with Postgres", "start_seconds": 156}, {"title": "Learning Objectives and Speaker Introduction", "start_seconds": 256}, {"title": "Defining AI Agents and Their Core Components", "start_seconds": 473}, {"title": "Code Demo: A Basic Customer Support AI Agent", "start_seconds": 660}, {"title": "The Role of Agent Frameworks and Multi-Agent Systems", "start_seconds": 957}, {"title": "Exploring Common AI Agent Patterns and Guardrails", "start_seconds": 1224}, {"title": "Demo: Sequential Agent Workflow with Observability", "start_seconds": 1526}, {"title": "Understanding RAG and Storing Chat History in Postgres", "start_seconds": 1892}, {"title": "Core RAG Concepts: Embeddings and Similarity Search", "start_seconds": 2190}, {"title": "Demo: Building a RAG System with Hybrid Search", "start_seconds": 2558}, {"title": "Advanced RAG Techniques: From Query Rewriting to Hyde", "start_seconds": 3042}, {"title": "Agentic Search: A Deeper Dive into Self-RAG", "start_seconds": 3571}, {"title": "Audience Q&A and Final Remarks", "start_seconds": 3812}], "navPages": [{"category": {"slug": "ai", "href": "/courses/ai", "title": "AI", "palette": {"--palette-category-base": "#460952", "--palette-category-highlight": "#931FAA", "--palette-category-border": "#EDA6FB"}}, "topics": [{"title": "Agentic AI", "slug": "ai/agentic-ai", "href": "/courses/ai/agentic-ai"}, {"title": "Coding with AI", "slug": "ai/ai-coding", "href": "/courses/ai/ai-coding"}, {"title": "AI Workflows", "slug": "ai/ai-workflows", "href": "/courses/ai/ai-workflows"}, {"title": "Claude Code", "slug": "ai/claude-code", "href": "/courses/ai/claude-code"}, {"title": "OpenClaw", "slug": "ai/openclaw", "href": "/courses/ai/openclaw"}, {"title": "Vibe Coding", "slug": "ai/vibe-coding", "href": "/courses/ai/vibe-coding"}, {"title": "AI Evals", "slug": "ai/evals", "href": "/courses/ai/evals"}, {"title": "AI Transformation", "slug": "ai/ai-transformation", "href": "/courses/ai/ai-transformation"}, {"title": "RAG & Search", "slug": "ai/rag-search", "href": "/courses/ai/rag-search"}, {"title": "MCP", "slug": "ai/mcp", "href": "/courses/ai/mcp"}, {"title": "AI for PMs", "slug": "ai/for-product-managers", "href": "/courses/ai/for-product-managers"}, {"title": "AI for Engineers", "slug": "ai/for-engineers", "href": "/courses/ai/for-engineers"}, {"title": "AI for Designers", "slug": "ai/for-designers", "href": "/courses/ai/for-designers"}, {"title": "AI for Marketers", "slug": "ai/for-marketers", "href": "/courses/ai/for-marketers"}, {"title": "AI for Founders", "slug": "ai/for-founders", "href": "/courses/ai/for-founders"}]}, {"category": {"slug": "product", "href": "/courses/product", "title": "Product", "palette": {"--palette-category-base": "#510B00", "--palette-category-highlight": "#B33600", "--palette-category-border": "#F7804D"}}, "topics": [{"title": "AI for PMs", "slug": "for-product-managers/ai", "href": "/courses/for-product-managers/ai"}, {"title": "Agentic AI", "slug": "for-product-managers/agentic-ai", "href": "/courses/for-product-managers/agentic-ai"}, {"title": "AI Evals", "slug": "for-product-managers/ai-evals", "href": "/courses/for-product-managers/ai-evals"}, {"title": "Vibe Coding", "slug": "for-product-managers/vibe-coding", "href": "/courses/for-product-managers/vibe-coding"}, {"title": "Product Sense", "slug": "for-product-managers/product-sense", "href": "/courses/for-product-managers/product-sense"}, {"title": "Product Discovery", "slug": "for-product-managers/product-discovery", "href": "/courses/for-product-managers/product-discovery"}, {"title": "User Research", "slug": "for-product-managers/user-research", "href": "/courses/for-product-managers/user-research"}, {"title": "Prototyping", "slug": "for-product-managers/prototyping", "href": "/courses/for-product-managers/prototyping"}, {"title": "Growth", "slug": "for-product-managers/growth", "href": "/courses/for-product-managers/growth"}, {"title": "Analytics", "slug": "for-product-managers/analytics", "href": "/courses/for-product-managers/analytics"}, {"title": "Tech Foundations", "slug": "for-product-managers/technical-foundations", "href": "/courses/for-product-managers/technical-foundations"}, {"title": "Strategy", "slug": "for-product-managers/strategy", "href": "/courses/for-product-managers/strategy"}, {"title": "Influence", "slug": "for-product-managers/influence", "href": "/courses/for-product-managers/influence"}, {"title": "Leadership", "slug": "for-product-managers/leadership", "href": "/courses/for-product-managers/leadership"}, {"title": "Career Growth", "slug": "for-product-managers/career-growth", "href": "/courses/for-product-managers/career-growth"}]}, {"category": {"slug": "engineering", "href": "/courses/engineering", "title": "Engineering", "palette": {"--palette-category-base": "#00332F", "--palette-category-highlight": "#00746B", "--palette-category-border": "#88B7B2"}}, "topics": [{"title": "AI for Engineers", "slug": "for-engineers/ai", "href": "/courses/for-engineers/ai"}, {"title": "Agentic AI", "slug": "for-engineers/agentic-ai", "href": "/courses/for-engineers/agentic-ai"}, {"title": "Coding with AI", "slug": "for-engineers/ai-coding", "href": "/courses/for-engineers/ai-coding"}, {"title": "Claude Code", "slug": "for-engineers/claude-code", "href": "/courses/for-engineers/claude-code"}, {"title": "OpenClaw", "slug": "for-engineers/openclaw", "href": "/courses/for-engineers/openclaw"}, {"title": "MCP", "slug": "for-engineers/mcp", "href": "/courses/for-engineers/mcp"}, {"title": "RAG & Search", "slug": "for-engineers/rag-search", "href": "/courses/for-engineers/rag-search"}, {"title": "AI Evals", "slug": "for-engineers/ai-evals", "href": "/courses/for-engineers/ai-evals"}, {"title": "Machine Learning", "slug": "for-engineers/ml", "href": "/courses/for-engineers/ml"}, {"title": "LLM Ops", "slug": "for-engineers/llm-ops", "href": "/courses/for-engineers/llm-ops"}, {"title": "Context Eng", "slug": "for-engineers/context-engineering", "href": "/courses/for-engineers/context-engineering"}, {"title": "Security", "slug": "for-engineers/security", "href": "/courses/for-engineers/security"}, {"title": "System Design", "slug": "for-engineers/system-design", "href": "/courses/for-engineers/system-design"}, {"title": "Leadership", "slug": "for-engineers/leadership", "href": "/courses/for-engineers/leadership"}, {"title": "Career Growth", "slug": "for-engineers/career-growth", "href": "/courses/for-engineers/career-growth"}]}, {"category": {"slug": "design", "href": "/courses/design", "title": "Design", "palette": {"--palette-category-base": "#38085F", "--palette-category-highlight": "#772CBB", "--palette-category-border": "#B5A2D0"}}, "topics": [{"title": "AI for Designers", "slug": "for-designers/ai", "href": "/courses/for-designers/ai"}, {"title": "Agentic AI", "slug": "for-designers/agentic-ai", "href": "/courses/for-designers/agentic-ai"}, {"title": "Vibe Coding", "slug": "for-designers/vibe-coding", "href": "/courses/for-designers/vibe-coding"}, {"title": "Prototyping", "slug": "for-designers/prototyping", "href": "/courses/for-designers/prototyping"}, {"title": "Figma", "slug": "for-designers/figma", "href": "/courses/for-designers/figma"}, {"title": "Design Systems", "slug": "for-designers/design-systems", "href": "/courses/for-designers/design-systems"}, {"title": "User Research", "slug": "for-designers/user-research", "href": "/courses/for-designers/user-research"}, {"title": "Product Discovery", "slug": "for-designers/product-discovery", "href": "/courses/for-designers/product-discovery"}, {"title": "UX", "slug": "for-designers/ux", "href": "/courses/for-designers/ux"}, {"title": "UI", "slug": "for-designers/ui", "href": "/courses/for-designers/ui"}, {"title": "Visual Design", "slug": "for-designers/visual-design", "href": "/courses/for-designers/visual-design"}, {"title": "Design Strategy", "slug": "for-designers/strategy", "href": "/courses/for-designers/strategy"}, {"title": "Influence", "slug": "for-designers/influence", "href": "/courses/for-designers/influence"}, {"title": "Leadership", "slug": "for-designers/leadership", "href": "/courses/for-designers/leadership"}, {"title": "Career Growth", "slug": "for-designers/career-growth", "href": "/courses/for-designers/career-growth"}]}, {"category": {"slug": "marketing", "href": "/courses/marketing", "title": "Marketing", "palette": {"--palette-category-base": "#431532", "--palette-category-highlight": "#943170", "--palette-category-border": "#C99EB5"}}, "topics": [{"title": "AI for Marketers", "slug": "for-marketers/ai", "href": "/courses/for-marketers/ai"}, {"title": "Agentic AI", "slug": "for-marketers/agentic-ai", "href": "/courses/for-marketers/agentic-ai"}, {"title": "Vibe Coding", "slug": "for-marketers/vibe-coding", "href": "/courses/for-marketers/vibe-coding"}, {"title": "Automation", "slug": "for-marketers/marketing-automation", "href": "/courses/for-marketers/marketing-automation"}, {"title": "Content Marketing", "slug": "for-marketers/content-marketing", "href": "/courses/for-marketers/content-marketing"}, {"title": "Demand Gen", "slug": "for-marketers/demand-generation", "href": "/courses/for-marketers/demand-generation"}, {"title": "Go-to-Market", "slug": "for-marketers/gtm", "href": "/courses/for-marketers/gtm"}, {"title": "Product Marketing", "slug": "for-marketers/product-marketing", "href": "/courses/for-marketers/product-marketing"}, {"title": "Positioning", "slug": "for-marketers/positioning", "href": "/courses/for-marketers/positioning"}, {"title": "Social Media", "slug": "for-marketers/social-media", "href": "/courses/for-marketers/social-media"}, {"title": "Brand", "slug": "for-marketers/brand", "href": "/courses/for-marketers/brand"}, {"title": "B2B Marketing", "slug": "for-marketers/b2b", "href": "/courses/for-marketers/b2b"}, {"title": "SEO & AEO", "slug": "for-marketers/seo-aeo", "href": "/courses/for-marketers/seo-aeo"}, {"title": "Strategy", "slug": "for-marketers/strategy", "href": "/courses/for-marketers/strategy"}, {"title": "Leadership", "slug": "for-marketers/leadership", "href": "/courses/for-marketers/leadership"}]}, {"category": {"slug": "leadership", "href": "/courses/leadership", "title": "Leadership", "palette": {"--palette-category-base": "#50090E", "--palette-category-highlight": "#A9202A", "--palette-category-border": "#D49C98"}}, "topics": [{"title": "AI for Leaders", "slug": "for-leaders/ai", "href": "/courses/for-leaders/ai"}, {"title": "Agentic AI", "slug": "for-leaders/agentic-ai", "href": "/courses/for-leaders/agentic-ai"}, {"title": "AI Transformation", "slug": "for-leaders/ai-transformation", "href": "/courses/for-leaders/ai-transformation"}, {"title": "AI Governance", "slug": "for-leaders/ai-governance", "href": "/courses/for-leaders/ai-governance"}, {"title": "Communication", "slug": "for-leaders/communication", "href": "/courses/for-leaders/communication"}, {"title": "Influence", "slug": "for-leaders/influence", "href": "/courses/for-leaders/influence"}, {"title": "Strategy", "slug": "for-leaders/strategy", "href": "/courses/for-leaders/strategy"}, {"title": "Management", "slug": "for-leaders/management", "href": "/courses/for-leaders/management"}, {"title": "People Operations", "slug": "for-leaders/people-operations", "href": "/courses/for-leaders/people-operations"}, {"title": "Exec Presence", "slug": "for-leaders/executive-presence", "href": "/courses/for-leaders/executive-presence"}, {"title": "Storytelling", "slug": "for-leaders/storytelling", "href": "/courses/for-leaders/storytelling"}, {"title": "Goal-setting", "slug": "for-leaders/goal-setting", "href": "/courses/for-leaders/goal-setting"}, {"title": "Personal Brand", "slug": "for-leaders/personal-brand", "href": "/courses/for-leaders/personal-brand"}, {"title": "Career Growth", "slug": "for-leaders/career-growth", "href": "/courses/for-leaders/career-growth"}]}, {"category": {"slug": "founders", "href": "/courses/founders", "title": "Founders", "palette": {"--palette-category-base": "#0C3209", "--palette-category-highlight": "#077202", "--palette-category-border": "#69C662"}}, "topics": [{"title": "AI for Founders", "slug": "for-founders/ai", "href": "/courses/for-founders/ai"}, {"title": "Agentic AI", "slug": "for-founders/agentic-ai", "href": "/courses/for-founders/agentic-ai"}, {"title": "AI Workflows", "slug": "for-founders/ai-workflows", "href": "/courses/for-founders/ai-workflows"}, {"title": "Vibe Coding", "slug": "for-founders/vibe-coding", "href": "/courses/for-founders/vibe-coding"}, {"title": "Prototyping", "slug": "for-founders/prototyping", "href": "/courses/for-founders/prototyping"}, {"title": "Product Sense", "slug": "for-founders/product-sense", "href": "/courses/for-founders/product-sense"}, {"title": "Positioning", "slug": "for-founders/positioning", "href": "/courses/for-founders/positioning"}, {"title": "Product Discovery", "slug": "for-founders/product-discovery", "href": "/courses/for-founders/product-discovery"}, {"title": "Management", "slug": "for-founders/management", "href": "/courses/for-founders/management"}, {"title": "Strategy", "slug": "for-founders/strategy", "href": "/courses/for-founders/strategy"}, {"title": "Go-to-Market", "slug": "for-founders/gtm", "href": "/courses/for-founders/gtm"}, {"title": "Personal Brand", "slug": "for-founders/personal-brand", "href": "/courses/for-founders/personal-brand"}, {"title": "Leadership", "slug": "for-founders/leadership", "href": "/courses/for-founders/leadership"}, {"title": "Fundraising", "slug": "for-founders/fundraising", "href": "/courses/for-founders/fundraising"}, {"title": "PMF", "slug": "for-founders/product-market-fit", "href": "/courses/for-founders/product-market-fit"}]}, {"category": {"slug": "more", "href": "/courses/more", "title": "More", "palette": {"--palette-category-base": "#1E2C2B", "--palette-category-highlight": "#4A6361", "--palette-category-border": "#B9C1BE"}}, "topics": [{"title": "Everyone", "slug": "for-everyone", "href": "/courses/for-everyone"}, {"title": "Operators", "slug": "for-operators", "href": "/courses/for-operators"}, {"title": "Data Scientists", "slug": "for-data-scientists", "href": "/courses/for-data-scientists"}, {"title": "Business Analysts", "slug": "for-business-analysts", "href": "/courses/for-business-analysts"}, {"title": "User Researchers", "slug": "for-user-researchers", "href": "/courses/for-user-researchers"}, {"title": "Customer Success", "slug": "for-customer-success", "href": "/courses/for-customer-success"}, {"title": "Project Managers", "slug": "for-project-managers", "href": "/courses/for-project-managers"}, {"title": "HR Professionals", "slug": "for-hr-professionals", "href": "/courses/for-hr-professionals"}, {"title": "Sales People", "slug": "for-sales-people", "href": "/courses/for-sales-people"}, {"title": "Lawyers", "slug": "for-lawyers", "href": "/courses/for-lawyers"}, {"title": "Finance", "slug": "for-finance-professionals", "href": "/courses/for-finance-professionals"}, {"title": "Investors", "slug": "for-investors", "href": "/courses/for-investors"}, {"title": "Real Estate", "slug": "for-real-estate-agents", "href": "/courses/for-real-estate-agents"}, {"title": "Educators", "slug": "for-educators", "href": "/courses/for-educators"}, {"title": "Creators", "slug": "for-creators", "href": "/courses/for-creators"}]}], "navTagLinks": [{"tag": {"id": 441, "tag_type": "topic", "label": "RAG & Search", "slug": "rag-search", "description": null, "parent_tag_id": null}, "link_slug": "for-engineers/rag-search"}, {"tag": {"id": 315, "tag_type": "topic", "label": "Agentic AI", "slug": "agentic-ai", "description": null, "parent_tag_id": null}, "link_slug": "for-engineers/agentic-ai"}, {"tag": {"id": 293, "tag_type": "persona", "label": "For Engineers", "slug": "for-engineers", "description": null, "parent_tag_id": null}, "link_slug": "for-engineers"}, {"tag": {"id": 299, "tag_type": "persona", "label": "For Data Scientists", "slug": "for-data-scientists", "description": null, "parent_tag_id": null}, "link_slug": "for-data-scientists"}], "now": "2026-05-19T22:55:22.519Z"}, "__N_SSG": true}, "page": "/p/[slug]/[...ignored]", "query": {"slug": "ee5094", "ignored": ["building-rag-enabled-agents-w-postgres-and-agent-framework"]}, "buildId": "qPlLjBFLQ9fnkFKJ7n7E-", "isFallback": false, "isExperimentalCompile": false, "gsp": true, "scriptLoader": []}}