Guest Author
Midhula Mariyam Jeevan is a content writer specializing in AI, enterprise technology, software engineering, and SEO. She creates research-backed, accessible content that makes complex technologies easier to understand for both technical and business audiences. Her work focuses on emerging technologies, enterprise AI adoption, AI governance, and agentic systems, and she works closely with ThinkPalm's AI development services team.
For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments — and in a lot of deployments it's failing. The companies that end up benefiting from agentic AI won't necessarily be the ones that have given their agents the most flexibility. They're the ones who create AI agents with specific responsibilities and make sure they operate within clear rules.