You can’t bolt autonomous agents onto legacy REST APIs and expect magic. Here’s the infrastructure redesign that actually works.
8 min readJan 19, 2026
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The Agentic AI Paradox: Everyone’s talking, few are succeeding
Agentic AI dominates every enterprise technology conversation. Conference keynotes promise autonomous systems that will revolutionize operations. Vendor pitches showcase agents that handle customer service, process documents, manage workflows, and make decisions.
There’s no shortage of pilot projects. Every enterprise has at least one team experimenting with agents. Innovation labs are full of promising demos. Proof-of-concepts work beautifully…
You can’t bolt autonomous agents onto legacy REST APIs and expect magic. Here’s the infrastructure redesign that actually works.
8 min readJan 19, 2026
–
Press enter or click to view image in full size
image created by the author using AWS Nova Canvas model
The Agentic AI Paradox: Everyone’s talking, few are succeeding
Agentic AI dominates every enterprise technology conversation. Conference keynotes promise autonomous systems that will revolutionize operations. Vendor pitches showcase agents that handle customer service, process documents, manage workflows, and make decisions.
There’s no shortage of pilot projects. Every enterprise has at least one team experimenting with agents. Innovation labs are full of promising demos. Proof-of-concepts work beautifully in controlled environments with curated data. But ask about agents running in production, handling real customer load, making decisions without human oversight — and the success stories become remarkably scarce.
The default assumption is that these are AI problems — the models aren’t good enough, the training data isn’t comprehensive enough, the prompts aren’t optimized enough. Teams respond by switching models, tuning parameters, hiring prompt engineers, and running more experiments.