Agentic AI Patterns that Actually Ship

AI agents are everywhere in headlines, but most fail in production. This article breaks down the proven patterns that work: reliable tool use with fallback mechanisms, human-in-the-loop validation for high-stakes decisions, and observability that lets you trace every agent decision.

The fundamental mistake most teams make is treating agents like finished products instead of probabilistic systems. An LLM-powered agent is a reasoning engine that makes decisions under uncertainty, which means it will make mistakes. The question isn't whether it fails—it's whether those failures are caught before they reach users.

We'll explore how Ramp automated expense categorization with agentic workflows, maintaining human oversight for edge cases. Ramp's system doesn't assume the agent knows every merchant category. Instead, it cascades through: automated classification with 95%+ confidence bypasses review, medium-confidence decisions route to a quick human check (15 seconds), and low-confidence categorizations are escalated with suggested categories from the agent's reasoning. This architecture turned what could be a high-error system into a 587% ROI win.

Real architectures don't rely on perfect LLM reasoning—they layer guardrails, monitoring, and graceful degradation. You need observable outputs that trace every decision: What did the agent see? What tool calls did it make? What was the outcome? Observability lets you catch subtle failures before they cascade. When merchants start reporting miscategorization, you'll want logs that show exactly why the agent chose that category.

The difference between demo and production isn't complexity; it's accountability. A demo slides past failures. Production systems need to own them, learn from them, and prevent them from happening again. Build agentic systems that fail visibly and safely, not silently.

About the Author

Durai Rajamanickam is a Business Transformation Leader and author of The AI Inflection Point: Volume 1 - Financial Services. With over two decades of experience, he specializes in AI-driven enterprise transformation, designing evidence-based ROI frameworks, and helping organizations modernize legacy systems with intelligent automation.

His work focuses on translating AI ambition into measurable business outcomes, with case studies spanning Ramp, Nubank, Coinbase, RBC, and Stripe—all showcasing AI ROI between 256% and 1,700%.

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