What actually makes something an "agent"
A lot of things get called AI agents that are really just a chatbot with a system prompt. The meaningful difference is whether the system can take actions in the world — read a file, call an API, send a message, query a database — and use the result of that action to decide what to do next, rather than producing a single response and stopping. We cover the underlying mechanism in more depth in How AI coding works; this piece is about designing that mechanism for a specific job.
Define the job before the architecture
The most common mistake in building an agent is starting with the tech — which model, which framework — before precisely defining what the agent is actually supposed to accomplish and, just as importantly, what it's explicitly not responsible for. "An agent that handles customer support" is not a spec. "An agent that answers questions from a fixed knowledge base and escalates anything about billing or refunds to a human" is closer to one.
Give it tools, not just a longer prompt
An agent's actual capability comes from what it's connected to — the ability to search documentation, query a database, send an email, run a calculation — not from how cleverly worded its instructions are. Design the smallest set of tools that lets it do its defined job, and be deliberate about what each tool can and can't do; a tool that can "look up an order" is safer than one that can "run any database query."
Design the loop: plan, act, observe, repeat
Most useful agents follow the same basic loop — decide the next step, take an action, look at what actually happened, and decide again based on that. Where this goes wrong is usually a missing observe step: an agent that takes an action and assumes it worked, without checking, will confidently compound a small mistake into a large one.
Guardrails: what it should never do without approval
Decide upfront which actions are reversible and low-stakes (safe to let the agent do autonomously) versus irreversible or high-stakes (sending a real email, charging a card, deleting data) — and require explicit human approval for the second category, at least until the agent has a real track record. This single design decision prevents most of the horror stories about agents going wrong.
Testing an agent is different from testing a function
A traditional function has a fixed input and a fixed expected output. An agent operates over open-ended natural language and can reach a correct outcome through different paths on different runs, which makes naive "exact match" testing unreliable. Test at the level of outcomes and guardrails instead: did it complete the actual job, did it stay within its defined tools, did it correctly escalate the cases it wasn't supposed to handle alone.
Where to go deeper
If you're building the agent as part of a larger product rather than a one-off script, treat it with the same security discipline as any other part of your system that touches user data or takes real actions — see AI coding security best practices. And our AI agents section has concrete use-case examples if you're still scoping what to build.