Building AI Agents for Real-World Problems & Workflows

Building AI agents that work in production requires more than impressive demos. Real-world agents must coordinate across multiple systems, enforce policies, manage state, and know when to escalate to humans. Four recurring patterns emerge: multi-system workflow orchestration (e.g., employee onboarding), policy-governed action execution (e.g., IT support), exception handling in structured processes (e.g., invoice processing), and triage/routing at scale (e.g., customer service). Effective agents are narrowly scoped, integrate into existing workflows, apply rules consistently, and treat human oversight as a design requirement rather than an afterthought.