Agentic AI Systems in Production

Apply the core agent loop and patterns — ReAct, plan-execute, reflection — to real problems.

Choose single- vs. multi-agent designs, and recognize when an agent is the wrong tool

Scope a real agent project with a clear spec: goal, tools, success criteria, failure modes

Design tool schemas and structured outputs an LLM can use reliably

Apply orchestration patterns — sequential, parallel, router — with error handling and retries

Wire multiple real tools into your agent and handle failures gracefully

Manage short- and long-term memory and context-window limits

Use retrieval and state machines to keep agents coherent across turns

Add a persistent memory layer to your own agent

Design offline and online eval strategies, including LLM-as-judge and trajectory eval

Build a regression suite and test set for your agent

Ship an eval harness you run on every change

Implement input/output guardrails and prompt-injection defenses

Add human-in-the-loop gates, audit logging, and cost controls

Make your agent safe to put in front of real customers

Instrument agents for observability, tracing, and cost monitoring in production

Apply a hyperscaler-aware GTM lens to position and sell agentic products

Present a shipped, production-grade agent to peers and an investor lens