What's the minimum viable agent stack?

An LLM API call + one integration. A simple support agent can be built with just an Anthropic API key and a Zendesk webhook—no vector database, no orchestration framework, no separate observability layer. Start minimal, then add layers as complexity demands: add RAG when the agent needs knowledge base access, add orchestration when workflows become multi-step, add observability when you need to debug production issues.

AI Agent Stack

Written by Max Zeshut

Founder at Agentmelt · Last updated Sep 9, 2026

The complete technology architecture required to build, deploy, and operate an AI agent in production—typically comprising an LLM provider (the reasoning engine), an orchestration framework (workflow management), a vector database (for RAG/retrieval), integrations (CRM, help desk, databases), an observability layer (logging, monitoring, tracing), and a deployment platform (hosting, scaling, security). Understanding the agent stack helps teams make build-vs-buy decisions and identify where their existing infrastructure can be leveraged.

Example

A team building an AI support agent assembles a stack of: Claude as the LLM, LangGraph for orchestration, Pinecone for vector search on the knowledge base, Zendesk integration for ticket management, LangSmith for observability, and AWS Lambda for deployment. Each layer has alternatives—the stack decisions depend on existing infrastructure, scale requirements, and team expertise.

Frequently asked questions

What's the minimum viable agent stack?: An LLM API call + one integration. A simple support agent can be built with just an Anthropic API key and a Zendesk webhook—no vector database, no orchestration framework, no separate observability layer. Start minimal, then add layers as complexity demands: add RAG when the agent needs knowledge base access, add orchestration when workflows become multi-step, add observability when you need to debug production issues.

See it as a workflow

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