Building an AI Agent: More Than Just an LLM

A LinkedIn post by Okan Yıldız.

🤖 Building an AI Agent is not just about choosing an LLM.

The most successful AI agents are built by combining multiple layers that work together as a system. Many teams focus heavily on model selection, but in practice the LLM is only one component of the architecture.

A production-ready AI agent layers the model together with memory, knowledge/skills, tools, guardrails, orchestration, and — critically — evaluation and observability.

Discussion

The comments converged on evaluation/observability being the hardest part:

• Hrishikesh Durg: “In production, evaluation and observability are the hardest pieces to get right. Most agents can demonstrate capability in controlled environments. The real challenge is understanding why they fail, measuring reliability over time, and maintaining consistent outcomes as tools, data, and business rules change.”
• Kushan Perera: Evaluating non-deterministic workflows requires continuous automated regression pipelines rather than static prompts.
• Related comments tie into the Agent Development Kit pattern: CLAUDE.md (memory) + Skills (knowledge) + Hooks (guardrails) + Subagents + Plugins.