Enterprise agents need 3 ingredients for success

In the rapidly evolving landscape of AI deployment, the challenge of building reliable enterprise agents has emerged as a critical frontier for businesses seeking competitive advantage. Harrison Chase, the co-founder of LangChain and LangGraph, recently shared invaluable insights on this topic, offering a framework that could transform how organizations approach agent development. His perspective cuts through the hype, focusing on the practical elements that make AI agents truly functional in enterprise settings rather than merely impressive in demos.

Key Points

• Enterprise agent reliability requires three core components: orchestration, memory systems, and evaluation frameworks—with each addressing different aspects of the agent architecture challenge.

• Orchestration through tools like LangGraph provides control flow for complex agent behaviors, allowing for more sophisticated decision-making and interaction patterns than simple input-output systems.

• Memory systems must be designed to retain both short-term context within conversations and long-term information across sessions, with retrieval augmented generation (RAG) serving as a foundation.

• Evaluation emerges as perhaps the most critical yet overlooked aspect, requiring both automated metrics and human feedback loops to continuously improve agent performance.

The Overlooked Power of Evaluation

The most compelling insight from Chase's presentation is his emphasis on evaluation as the cornerstone of reliable agent development. While many organizations focus intensely on the initial deployment of AI agents, Chase argues that the true differentiator lies in establishing robust evaluation frameworks that enable continuous improvement.

This matters tremendously in the current business environment where AI capabilities are becoming more standardized. As base models from providers like OpenAI and Anthropic reach feature parity, competitive advantage will increasingly derive from how well organizations can tune and optimize these models for specific use cases. Companies that implement rigorous evaluation processes will identify failure modes faster, address hallucinations more effectively, and ultimately deliver more valuable agent experiences to users.

Beyond the Presentation: Implementation Realities

What Chase's framework doesn't fully address is the organizational readiness required to implement these three components effectively. At Acme Financial, a mid-sized financial services firm, their initial agent deployment followed all technical best practices but still struggled with adoption. The missing element wasn't technical but cultural—end users weren't properly prepared to interact with and provide feedback to the system. After implementing a "feedback ambassador" program where power users were