The Agentic AI Engineering Bootcamp & Certification

Apply "Problem First" principles to decompose complex problems into scalable, autonomous AI-Native agent architectures.

Build robust single agents via OpenAI SDK with persistent memory structures and state management for reliable, long-term task execution.

Implement strict guardrails and permission layers to enforce operational boundaries, preventing hallucinations and ensuring safety in produc

Design stateful workflows with LangGraph to control cyclic graphs and manage coordination between specialized agent nodes.

Deploy role-specific agent swarms (Planner, Executor) via CrewAI and AutoGen to parallelize tasks and solve problems autonomously.

Build sophisticated handoff mechanisms to transfer context and structured outputs between diverse LLM models without losing state data.

Build standardized MCP servers to give agents secure access to local files and remote databases, decoupling tools from LLM providers.

Extend agent capabilities by coding custom "skills add-ons" and function wrappers to execute complex, domain-specific logic.

Integrate Tavily for real-time search and create custom tools wrapping external APIs to fetch live structured data.

Master Claude Code to autonomously generate complex agentic frameworks, reducing boilerplate and setup time.

Leverage Cursor/Codex to rapidly prototype and debug multi-agent code, using AI to fix logic errors in your orchestration layer.

Accelerate development by treating AI tools as active pair programmers that understand your full codebase and architectural style.

Build "Agentic RAG" pipelines that autonomously plan retrieval, verify source credibility, and synthesize data for accurate answers.

Optimize context windows and hybrid retrieval strategies to handle multi-turn queries without exceeding token limits or budgets.

Connect agents to unstructured data (PDFs, Notion) using reranking to maintain high signal-to-noise ratios in retrieved context.

Deploy agents to production with full observability using tracing tools to debug execution steps and identify bottlenecks in real-time.

Implement evaluation frameworks to benchmark agent performance, ensuring reliability before public release.

Secure agents with privacy-first governance, ensuring compliance with data standards and managing permissions for sensitive actions.