How to Design an AI Agent

What actually makes something an agent, not just a smart script?

An agent is a system where an LLM decides its own next step, chooses which tool to call, and adjusts its plan based on what comes back, rather than following a sequence a person wrote in advance. A script that calls an API in a fixed order is automation. An agent that reads a customer ticket, decides whether it needs the billing tool or the shipping tool, and changes its next move based on the result, is an agent. The distinction matters because an agent's unpredictability is the feature you are buying and the risk you are taking on at the same time.

Anthropic's own research on building effective agents makes a point worth repeating before any design decision: most production systems that people call agents are actually workflows, a predefined chain of LLM calls, and workflows are usually the right choice. An agent's extra flexibility only pays for itself when the task genuinely has branching decisions a fixed sequence cannot anticipate.

What tools should an agent actually have?

Give it the smallest set that covers the task, each one described clearly enough that the model does not have to guess when to use it. Anthropic's engineering guidance on writing tools for agents treats the tool description the same way a product team treats a UI label: ambiguous wording produces ambiguous behavior, and a tool that tries to do three things badly is worse than three tools that each do one thing well.

Two habits separate a tool set that works from one that quietly breaks in production:

• Name what the tool does and what it does not do, in plain language a model (and a human reviewing logs later) can act on without guessing.
• Return errors the model can actually use to recover, not a raw stack trace, so a failed call becomes a chance to retry differently instead of a dead end.

How do you add guardrails without killing the automation's value?

Guardrails are conditional, not blanket. The rule is not "a human reviews everything," because that erases the reason you built the automation in the first place. The rule is a specific trigger: this action, above this threshold, on this type of account, needs a person before it executes. Everything below that line runs on its own, and you review the log afterward rather than the action beforehand.

Get the threshold wrong in either direction and the system fails in a predictable way. Set the bar too low and every action needs approval, so the agent adds review work instead of removing it. Set it too high and a costly mistake ships before anyone sees it. The right threshold usually comes from what the action actually costs to undo: a message that is easy to correct can run automatically; a payment or a customer-facing commitment should not.

Builders Camp's AI Agents practical challenge is built around exactly this failure mode: a three-agent pipeline misclassified a major account as a churn risk and sent the alert straight to a customer success manager with no review step, because the whole point of the automation was skipping review. The fix was not scrapping the pipeline. It was writing a specific rule for which classifications, on which account sizes, needed a human to look before anything went out.

Guardrail type	What it protects against	Where it belongs
Human approval gate	Irreversible or high-cost actions: payments, deletions, external communications	Before the action executes, on a specific trigger condition
Scoped tool access	An agent using a tool outside its intended task	Baked into the tool set itself, not left to the prompt to enforce
Confidence or score threshold	Low-certainty outputs reaching a user or a downstream system untouched	Between the agent's decision and the action it is about to take
Post-hoc logging and review	Drift that only shows up in patterns across many runs, not any single one	After execution, reviewed on a cadence, not per action

Does an agent need memory, and how much?

It needs exactly enough to avoid two failure modes: forgetting a constraint you already gave it, and repeating a decision it already made. For a short task, that can be nothing more than the current conversation. For a longer-running process, it usually means a written summary the agent reads at the start of each run, similar to the way a new team member gets a short brief instead of the entire project history.

More memory is not automatically better. A context window stuffed with every past interaction gives the model more material to misread, not more reliability. Design memory around what the next decision actually needs, not around capturing everything that happened.

When should you not build an agent at all?

When the task is a fixed sequence with no real branching, when a wrong output is expensive and hard to walk back, or when a single, well-scoped prompt already handles it. Anthropic's guidance on this point is blunt: the simplest solution that works is the right one, and added complexity should only survive if it demonstrably improves outcomes over that simpler baseline. A three-step approval workflow with no decision points does not need an agent. It needs a workflow, which is easier to test, easier to explain to a stakeholder, and far easier to debug when something goes wrong at 2 a.m.

Who this guide is for, and who it is not for

This is for a product manager, founder, or team lead deciding whether an agent is the right shape for a real automation problem, and how to scope one if it is. It assumes you already have a specific task in mind, not a general interest in agents as a category. Builders Camp's AI Agents bootcamp is built for exactly this reader: product managers exploring agents for automation or internal tooling, builders who need their agent workflows to be reliable and safe, and company leaders trying to tell agents apart from simple automation.

It is not for someone who has not yet decided whether the underlying task needs AI at all, and it will not turn an unclear process into a clear one. An agent amplifies a well-scoped decision; it does not manufacture one. If the task itself is still fuzzy, that is a discovery problem to solve first, not an agent design problem.

Where agent design connects to the rest of the AI product job

An agent is only as trustworthy as the evals you run against it before shipping a change, and its answers are only as good as the retrieval setup feeding it context when the task calls for outside knowledge. If your agent needs to connect to real tools and data sources through a standard protocol rather than custom integrations, building an AI assistant with MCP covers that specific wiring. If you are scoping a full product around agentic behavior rather than a single workflow, how to build an AI product from scratch walks through the surrounding decisions this guide does not cover.

Builders Camp's AI Agents bootcamp runs 2 weeks, 3 live sessions, and covers exactly the decisions in this guide in more depth: tool design, orchestration, memory, and the human-in-the-loop patterns that keep an agent from becoming a liability the first time it meets a real edge case.