Agentic AI for Product Managers
Building Agentic AI Products: From Strategy to Production for Product Managers
Master the Technical Skills to Build AI Agents That Actually Works
Stop Building AI Products That Fail in Production
The only course that teaches product managers to build, evaluate, and scale agentic AI systems that survive real-world usage. Go from AI-curious to technically fluent in 5 weeks.
Why 90% of AI Products Fail After Launch
Most product managers treat agentic AI like traditional software. They focus on features instead of evaluations. They ship without proper monitoring. They scale without understanding the underlying systems.
The result? AI products that hallucinate in production, break under load, and cost more than they generate.
You need different skills for AI products:
- Evaluation frameworks that catch problems before users do
- Prompt engineering that works at scale
- Multi-agent coordination and tool integration
- Production monitoring for probabilistic systems
Who Itβs For
- Product Managers who want to lead AI product development confidently
- AI PMs who need to master agentic systems, not just LLM basics
- Startup founders building AI-first products
- Tech leads & solution architects working alongside PMs to deliver AI agents
What Youβll Learn
Over 5 weeks, 10 live sessions, youβll gain end-to-end skills to take any Agentic AI product from concept to deployment.
You will:
β Spot and validate high-impact agent use cases
β Design agent architectures (single & multi-agent systems)
β Build advanced prompt pipelines with guardrails
β Implement production-ready RAG systems
β Integrate secure multi-agent coordination with MCP
β Evaluate agents using automated & human feedback loops
β Monitor, optimize, and operate agents in production with 99.9% uptime targets
Your Hands-On Weekly Projects
By the end, youβll have five portfolio-worthy projects:
Project 1 β Agent Evaluation Framework
Build comprehensive testing infrastructure with custom metrics, automated pipelines, and performance comparison across multiple LLMs.
Project 2 β Advanced Prompt Engineering System
Create production-grade prompt architectures with meta-prompting, A/B testing, safety guardrails, and version control.
Project 3 β Production RAG System
Implement end-to-end knowledge retrieval with advanced chunking, memory layers, and self-improving capabilities.
Project 4 β Multi-Agent System with MCP
Design coordinated agent workflows with secure tool integration, audit trails, and enterprise-grade monitoring.
Project 5 β Capstone Project
The Skills Gap That's Killing AI Products
Traditional PMs think: "If it works in demo, it works in production" AI PMs know: "If you can't evaluate it, you can't ship it"
Traditional PMs think: "Add more features to drive engagement"
AI PMs know: "Optimize the core agent behavior first"
Traditional PMs think: "Scale by adding servers" AI PMs know: "Scale by optimizing prompt efficiency and model selection"
This course bridges that gap with hands-on technical training.
What Makes This Different
π§ Actually Build Systems No theoretical frameworks. You'll implement 5 working AI systems using real APIs and production tools.
π Evaluation-First Development
Learn to test AI before shipping. Build quality gates that actually catch problems.
π Production-Ready Skills Monitor, debug, and optimize AI systems at scale. Understand what 99.9% uptime requires.
5-Week Intensive Curriculum
Week 1: Production Prompt Engineering
Learn: Advanced prompting techniques that scale to millions of users
Build: Prompt optimization system with A/B testing and guardrails
Master: Meta-prompting, chain-of-thought architectures, and safety layers
Week 2: Knowledge Systems & Memory
Learn: RAG architectures, memory layers, and self-improving agents
Build: Production RAG system with advanced retrieval and knowledge graphs
Master: Episodic, semantic, and procedural memory implementation
Week 3: Evaluation-Driven Development
Learn: Why traditional software thinking fails with probabilistic systems
Build: Comprehensive evaluation framework with automated testing pipelines
Master: Cost-performance analysis and model comparison methodologies
Week 4: Multi-Agent Coordination
Learn: Agent-to-agent communication, MCP protocol, and tool integration
Build: Multi-agent workflow system with secure tool access
Master: Coordination patterns, state management, and security frameworks
Week 5: Production Operations
Learn: Monitoring, optimization, and scaling strategies for AI systems
Build: Comprehensive monitoring dashboard with automated alerts
Master: Cost optimization, performance tuning, and incident response
Frequently Asked Questions
Do I need AI experience to take this course? Yes - this course assumes you understand AI fundamentals like LLMs, basic prompting, and how AI models work. We dive deep into advanced agentic AI concepts from day one. If you're new to AI, start with our AI PM course first to build your foundational knowledge.
Do I need coding experience? Basic programming literacy helps but isn't required. You'll need comfort with APIs and reading code, but we teach all AI-specific skills.
What if I can't attend live sessions?
All sessions are recorded and available within 24 hours. However, live interaction is core to the learning experience.
How is this different from other AI courses? Most courses teach AI theory. We teach you to build production AI systems. You graduate with 5 working projects and technical credibility.
What tools will I use? Direct API access to GPT-4, Claude, Gemini, plus vector databases (Pinecone), agent frameworks (LangChain), and monitoring platforms (LangFuse).
Course Run Dates:
September 13th - October 12th, 2025 (Currently Enrolling)
Class Schedule:
SATURDAY & SUNDAY: 10 AM - 12 PM PT.
Course Duration :
10 classes across 5 weeks, 90 mins class + 30 mins Q & A each.
Price
$2549
Course Overview
Class 1 - Sat, Sep 13, 2025
Foundations of Agentic AI
1. What is Agentic AI vs. traditional AI/ML
2. Key characteristics: autonomy, goal-oriented behavior, environment interaction
3. Types of AI agents (reactive, deliberative, hybrid, learning agents)
4. The "GPT-4 Barrier" and cost-performance evolution in AI agents
5. Production vs. prototype mindset: lessons from 2024's deployment wave
6. Current landscape and major players (OpenAI, Anthropic, Google, etc.)
7. Real production case studies from finance (Jane Street, BlackRock, Bloomberg)
8. The product manager's role in the AI agent ecosystem
Class 2 - Sun, Sep 14, 2025
AI Agent Architecture and Technical Fundamentals
1. Core components: perception, reasoning, planning, action
2. Large Language Models (LLMs) as the "brain" of modern agents
3. Tool integration and API connectivity
4. Memory systems (short-term, long-term, episodic)
5. Agent frameworks (LangChain, AutoGPT, CrewAI, etc.)
6. Local vs. cloud deployment architectures (Ollama, MLX, vs. hosted APIs)
7. SLMs vs. LLMs in Agent Design β trade-offs in performance, cost, and privacy, with decision frameworks for PMs.
Class 3 - Sat, Sep 20, 2025
Context Engineering and Prompt Design for PMs
1. Understanding prompts as the primary interface to AI agents
2. System prompts vs. user prompts vs. few-shot examples
3. Context window management and optimization strategies
4. Prompt flow and orchestration patterns for production systems
5. Prompt engineering frameworks (Chain-of-Thought, ReAct, etc.)
6. Testing and iterating on prompt performance
7. Prompt versioning and A/B testing for prompt optimization
8. Collaborating with engineers on prompt development
9. Tools and platforms for prompt management and versioning
Class 4 - Sun, Sep 21, 2025
RAG Systems and Knowledge Integration
1. RAG architecture: retrieval, augmentation, generation pipeline
2. Vector databases and semantic search fundamentals
3. Advanced RAG capabilities: storage scaling and performance optimization
4. Data preparation and chunking strategies for RAG
5. Content beyond text: multimodal RAG systems (images, audio, video)
6. Detailed retrieval pipeline control: re-ranking and quality improvements
7. Evaluation metrics for RAG systems (relevance, faithfulness, answer quality)
8. Advanced RAG patterns: hierarchical retrieval and hybrid search
9. Managing knowledge base updates and data freshness
10. RAG vs. fine-tuning trade-offs
11. Cost management in RAG: balancing quality and efficiency
12. Privacy and security considerations in knowledge retrieval
13. Agentic RAG
Class 5 - Sat, Sep 27, 2025
Multi-Agent Systems and Orchestration
1. When and why to use multi-agent architectures
2. Agent role definition and specialization strategies
3. Agent graph memory: persistent memory across agent interactions
4. Communication patterns between agents (sequential, parallel, hierarchical)
5. Hierarchical MCP: building layered agent communication systems
6. Orchestration frameworks and workflow management
7. Stateful vs. stateless agent interactions: design trade-offs
8. Conflict resolution and consensus mechanisms
9. Advanced orchestration: agent collaboration and negotiation patterns
10. Debugging and monitoring multi-agent interactions
11. Cost and complexity management in multi-agent systems
12. Production case studies: multi-agent systems in finance and enterprise
Class 6 - Sun, Sep 28, 2025
Identifying Opportunities and Building Teams
1. Framework for evaluating agent-suitable problems
2. Production readiness assessment: moving beyond demos
3. Automation vs. augmentation strategies and ROI assessment
4. Building the business case for agentic AI products
5. Cross-functional collaboration (engineering, data science, design)
6. Writing effective requirements for agent capabilities
7. Agile development for non-deterministic systems: new methodologies
8. Testing strategies: from unit tests to production monitoring
9. Risk management: addressing hallucination, bias, and reliability
Class 7 - Sat, Oct 4, 2025
Agent Evals
1. The critical role of evaluations in production AI systems
2. Binary evaluation dimensions: breaking down complex agent performance
3. Assertion-based testing frameworks for agent behavior
4. Data analysis and skew minimization in evaluation pipelines
5. User satisfaction and adoption metrics
6. Agent reliability measurement: uptime, consistency, accuracy
7. A/B testing strategies for non-deterministic AI systems
8. Guardrails and safety measurement: preventing harmful outputs
9. Continuous learning and improvement processes
10. Cost optimization: balancing performance with operational expenses
11. Production monitoring: observability for agent systems
12. Long-term value measurement
Class 8 - Sun, Oct 5, 2025
Agent Safety, Governance, and Responsible AI
1. The AI trust crisis: building customer confidence in agent systems
2. Advanced prompt security: injection attacks and mitigation strategies
3. Local vs. cloud prompt management: data privacy considerations
4. Building customer trust: transparency and explainability in agent design
Class 9 - Sat, Oct 11, 2025
Product Strategy, UX Design, and Advanced Capabilities
1. Human-agent interaction design patterns and conversational UX
2. Multimodal interaction design: voice, text, visual integration
3. Graceful failure handling: managing user expectations when agents fail
4. User onboarding for AI agents: building proper mental models
5. Designing for different technical literacy levels
6. Platform vs. product strategies and monetization models
7. Cost-performance trade-offs in product strategy
8. Function calling and tool integration architecture
9. Model Context Protocol (MCP): standardized agent-tool communication
10. Building and managing MCP servers for custom tool integration
11. Advanced MCP patterns: service discovery, authentication, and observability
12. Agents with persistent memory and learning capabilities
13. Advanced reasoning patterns: planning, reflection, and self-correction
14. Production deployment: infrastructure, scaling, and reliability
Class 10 - Sun, Oct 12, 2025
Capstone Demo
Instructors
Jyothi Nookula