The original title is "Autonomous AI agents in enterprise: architecture, use cases, and governance controls"

Short summary

An overview of autonomous AI agents in enterprise settings, covering architecture (LLM reasoning, vector memory, tool integration, feedback loops) and industry examples in finance, software development, and supply chain. The author emphasizes governance controls: scoped permissions, human-in-the-loop checkpoints, and immutable audit logs. Cites Gartner and McKinsey projections for agentic AI adoption and economic impact.

• •Autonomous agents combine LLM reasoning, memory, tool integration, and self-correction loops
• •Enterprise examples span fraud detection, coding agents, and supply chain automation
• •Three non-negotiable governance controls: scoped permissions, human checkpoints, immutable audit logs

Generated with AI, which can make mistakes.