Building AI Agents for Real-World Problems & Workflows

The current AI agent narrative emphasizes autonomy and reasoning, but the real gap between demos and production is orchestration. Successful agents are coordination layers—they span multiple systems, enforce policies, manage state, and hand off to humans at well-defined boundaries. This talk distills patterns from real deployments (employee onboarding, IT support, invoice processing, customer service) to show that reliability comes from narrow scope, not from broad decision-making. The hard part isn’t model intelligence; it’s designing workflows that respect constraints, handle exceptions predictably, and integrate with existing infrastructure. Engineering teams should note that agent projects often fail because they try to replace human judgment entirely, rather than augment it. The operational consequences are significant: without clear control structures, agents create compliance risks and erode trust. The tradeoff is between flexibility and predictability—narrow agents are easier to govern but less general. For engineering leaders, the message is to invest in integration and governance design before scaling AI agents. Hype around fully autonomous agents ignores the messy reality of enterprise systems. The patterns here are anecdotal but consistent with production experience. Teams should evaluate agent tools not by reasoning benchmarks, but by how well they support policy-driven execution, state management, and human escalation paths.