How to Build an AI Agent: A Beginner's Guide
An 'agent' is a normal LLM call plus two things: tools it can invoke, and a loop that lets it use their results to decide what to do next. Demystified, with the actual failure modes to design around.
Strip away the hype and an "AI agent" is a specific, learnable pattern: an LLM given (1) a goal, (2) a set of tools it can call (search, run code, read a file, call an API), and (3) a loop — call the model, let it decide to use a tool or respond, execute the tool, feed the result back, repeat until it decides it's done. That's the whole mechanism. The engineering difficulty is in the details, not in some fundamentally different kind of AI.
The core loop
- The model receives the goal and a description of available tools (name, what it does, what parameters it takes).
- The model decides: respond directly, or call a tool with specific arguments.
- If it calls a tool, your code executes that tool and returns the result to the model as new context.
- The model sees the result and decides again: call another tool, or produce a final answer.
- Repeat until the model produces a final answer, or you hit a safety limit (max iterations, timeout).
Before building a multi-tool agent, build one with a single tool and verify the basic loop works end to end — model calls tool, tool result comes back, model uses it correctly. Debugging a broken loop with five tools at once is much harder than debugging one with one tool.
The failure modes that actually matter
| Failure mode | What it looks like | How to design around it |
|---|---|---|
| Infinite or excessive looping | The agent keeps calling tools without converging on an answer | Set a hard max-iteration limit and fail gracefully with a clear message when hit, rather than letting it run indefinitely |
| Tool misuse — wrong arguments or wrong tool for the task | The agent calls a tool with malformed or nonsensical parameters | Write precise tool descriptions with explicit parameter constraints; validate tool inputs before executing them, don't trust the model's output blindly |
| Excessive tool access (security) | An agent with broad tool permissions is a much bigger attack surface if manipulated via prompt injection | Grant only the minimum tools needed for the specific task — least privilege applies to agents the same way it applies to any system |
| Silent partial failure | A tool call fails and the agent proceeds as if it succeeded, or gives a confidently wrong final answer | Tool results must clearly signal success/failure; the agent's instructions should explicitly cover what to do when a tool call fails |
Worked example: designing a tool description
The quality of a tool's description directly determines whether the agent uses it correctly — this is prompt engineering applied to the tool definition itself, not just the task prompt.
I'm building an agent that needs a tool to search internal documentation. Here's a rough description: "search_docs(query) - searches the docs."
Task: Rewrite this as a precise tool description that minimizes misuse.
Constraints:
- State exactly what the query parameter should look like (a natural-language question? keywords? a specific format?).
- State what the tool returns, including what an empty/no-results case looks like.
- State when NOT to use this tool (e.g. if the answer might be in the current conversation already, don't re-search).
- Keep it concise — verbose tool descriptions cost context budget on every single turn of the loop, not just once.When you don't need an agent at all
A single well-structured prompt with no tool loop handles most tasks fine — summarization, code generation, explanation, review. Reach for an agent specifically when the task genuinely requires taking actions and reacting to their results (searching for information you don't have, running code and reacting to the output, multi-step workflows where each step depends on the last). Adding agent machinery to a task that's really just "generate text from context" adds complexity, latency, and failure surface for no benefit.
The underlying skill — precise task specification, explicit constraints, verifying output rather than trusting it — is the same discipline the Foundations program builds for single-turn prompting. Agent design is that discipline applied to a loop instead of a single call, not a separate skill to learn from scratch.