Over the last few years, I’ve worked across multiple enterprise AI initiatives—data platforms, analytics modernization, GenAI pilots, and more recently, agentic AI systems. What I’ve consistently observed is this:
the hardest part of agentic AI is not building the agent—it’s running it in production.
Most teams today can spin up a working agent in hours. With frameworks like LangGraph, Strands, and others, you can wire reasoning, tools, and memory quickly. Demos look impressive. Proofs of concept get leadership attention.
Then reality hits.
Suddenly the conversation shifts from “What can the agent do?” to “How do we deploy this safely, scale it, secure it, observe it, and operate it without slowing the business?”
This is where many agentic AI initiatives quietly stall.
the hardest part of agentic AI is not building the agent—it’s running it in production.
Most teams today can spin up a working agent in hours. With frameworks like LangGraph, Strands, and others, you can wire reasoning, tools, and memory quickly. Demos look impressive. Proofs of concept get leadership attention.
Then reality hits.
Suddenly the conversation shifts from “What can the agent do?” to “How do we deploy this safely, scale it, secure it, observe it, and operate it without slowing the business?”
This is where many agentic AI initiatives quietly stall.
