How to Build an AI Agent: Architecture, Tools, and Failure Modes

01 What Is an AI Agent?

An AI agent is a system that uses an LLM to decide what actions to take, executes those actions via tools, and iterates until a goal is achieved. Unlike a chatbot (which just responds to questions), an agent can: search the web, call APIs, read/write files, execute code, and chain these actions together. The LLM is the 'brain' that decides what to do; the tools are the 'hands' that do it. The architecture seems simple. In production, it's not.

02 The Failure Modes

Our analysis of 50 production agents found that infinite loops (28%) and wrong tool calls (24%) are the top failure modes. Infinite loops happen when the agent tries something, fails, tries the same thing again, and repeats. Wrong tool calls happen when the agent misunderstands its available tools and calls the wrong one or passes wrong arguments. Hallucinated data (19%) is the agent fabricating inputs or outputs. These failures aren't random — they're predictable consequences of giving an LLM autonomy without guardrails.

03 What Actually Works

The agents that survive in production share common patterns: strict schemas (the agent can only call tools with validated inputs), step limits (maximum 10-20 iterations before forced termination), human-in-the-loop checkpoints (for irreversible actions), and stateless design (each request starts fresh, no accumulated state to corrupt). The successful agents are not autonomous general-purpose systems — they're narrow, constrained workflows with an LLM making specific decisions at specific points. The 'autonomous agent that does everything' is still a research project, not a product.