"It's time to wake up from the fever dream of autonomous AI. For the past year, the enterprise software narrative has been dominated by a singular, intoxicating promise: agents. We were told that we were on the verge of deploying digital employees that could plan, reason, and execute complex workflows while we watched from the sidelines, which Lena Hall, Akamai senior director of developer relations, rightly pillories."
"This aligns perfectly with what I've been calling AI's trust tax. In our rush to adopt generative AI, we forgot that while intelligence is becoming cheap, trust remains expensive. A developer might be impressed that an agent can solve a complex coding problem 80% of the time. But a CIO looks at that same agent and sees a system that introduces a 20% risk of hallucination, data leakage, or security vulnerability into a production environment."
Enterprise AI agent deployments frequently fail due to unreliability rather than lack of model intelligence. Successful production agents perform simple, short workflows and often execute fewer than ten steps before returning control to a human. Intelligence is becoming cheap while trust remains expensive, creating a reliability gap where even an 80% success rate implies unacceptable risk for production environments because of hallucination, data leakage, or security vulnerabilities. Organizations should assign narrow, perfectly executable tasks to agents, enforce rigorous database-backed memory and access controls, and abandon attempts to deploy autonomous, open-ended digital employees.
Read at InfoWorld
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