Agentic AI Accelerator

Agentic AI Accelerator

Executive Summary

The Agentic AI Accelerator is a 4-day, enterprise-grade production program that turns experienced developers, architects, and data engineers into builders of secure, auditable multi-agent AI systems. Delivered as two 2-day parts with a real-world sandbox intermission between them, the curriculum centers on the architectural pairing of LangGraph for deterministic, state-driven orchestration and Weaviate for enterprise semantic memory — layered with GitHub Copilot Agent Mode, LangSmith, and Ragas. Every module pairs conceptual grounding with a hands-on build, closing with a production-ready multi-agent workflow.
4
days, two 2-day parts
12
modules
5
core tools mastered
15–30%
target velocity improvement
AI Project Reality Check — 2026 Data

Most enterprise AI investments fail to deliver business value

Sources: Gartner I&O Survey (April 2026) · IBM CEO Global Study (2025) · RAND Corporation (2025)
AI projects launched
100%
All funded initiatives
Reach production deployment
53%
Gartner, 2024
Fully deliver on ROI
28%
★ Gartner, Apr 2026
Scale enterprise-wide
16%
IBM CEO Study, 2025
The #1 cited root cause: insufficient talent to move from prototype to production deployment. The Agentic AI Accelerator is built specifically to close that gap — pairing LangGraph and Weaviate to take agent systems the rest of the way to production.

What Makes It Different

  • Deep architectural focus on the LangGraph + Weaviate pairing — not another prompt-wrapper course
  • Two 2-day parts with a sandbox intermission for real-world prototyping between sessions
  • Full production tool chain: GitHub Copilot Agent Mode, LangSmith, and Ragas in one curriculum
  • ROI tracked with built-in tool telemetry, not subjective feedback
  • Enterprise guardrails, PII masking, and RBAC engineered into every graph from day one

What Graduates Can Build

  • Design deterministic, state-driven multi-agent workflows using LangGraph
  • Configure Weaviate for hybrid search, metadata filtering, and multi-tenant isolation
  • Build and host custom Model Context Protocol (MCP) servers with FastMCP
  • Implement human-in-the-loop approval gates and state time-travel debugging
  • Trace, debug, and root-cause live agent graphs with LangSmith
  • Quantify groundedness, faithfulness, and task completion rates with Ragas
Ready to close the gap between AI pilots and AI that ships?