Enterprise AI Workshop: Build Your First AI Agent in 2 Hours

Event Recap

Over 90 attendees joined live for our Enterprise AI Workshop in April. Aisha walked through building a complete document analysis agent from scratch — starting with a simple LLM call and progressively adding memory, tool use, and a human approval step. The most popular section was the cost estimation framework — attendees were surprised to learn that a well-designed AI agent handling 10,000 documents per month costs under $50 in API fees. The recording covers every step with full code available on GitHub.

Key Takeaways

• 1
  AI agents are just LLMs with tools — the complexity is in the orchestration, not the model
• 2
  Start with a simple chain-of-thought agent before adding memory or multi-step reasoning
• 3
  Human-in-the-loop checkpoints are non-negotiable for enterprise deployments
• 4
  Cost per document drops 10x when you add caching for repeated queries
• 5
  The hardest part of an AI agent project is defining the failure modes, not building the happy path

Highlights