How to build an AI agent that actually works

Most "AI agents" fail for the same boring reasons. They try to do too much. They're handed more power than they need. Nobody wrote down what they must never do. And no one tested whether they actually behave before trusting them with real work.

None of that is an AI problem — it's a design problem. Below is the exact method that separates an agent you can rely on from a clever demo that breaks in production. It's the same method our Agent Builder runs for you automatically.

The method, in one line

One job → interview the intent → give it the least power it needs → write the guardrails down → prove it behaves.

Skip any step and you get the agent everyone complains about. Do all five and you get one you can actually leave running.

Step 1 — Give it exactly one job

Write the agent's purpose as a single sentence with one verb. If you need the word "and", you have two agents.

• Good: "Triages my inbox and drafts replies to the important ones."
• Trouble: "Manages my email, books meetings, posts to social, and pays invoices." — that's four agents wearing a trench coat.

Narrow agents are reliable agents: they're easy to reason about, they fail predictably, and a system can route work to them correctly. Breadth is where agents go to misbehave.

Step 2 — Interview the intent (before you write a prompt)

A dependable agent starts as answers, not a prompt. Work through this short list first:

• Job statement — "This agent does X so that Y."
• Trigger — when, exactly, should it act?
• Inputs and outputs — what does it receive, and what does it hand back?
• Autonomy — read-only, propose-only, act-with-approval, or fully autonomous?
• Prohibitions — what must it never do?
• Tone — only matters if it's user-facing.
• Success and failure — how will you know it worked, and what are the two or three ways it most likely goes wrong?

Those last answers aren't busywork — the failure modes become your tests in Step 5.

Step 3 — Give it the least power it needs

This is the step almost everyone skips, and it's the most important. Privilege is a stronger guardrail than instructions. A read-only agent cannot delete your data no matter how confused its prompt gets. Start from "can read" and add capability only when the job provably requires it.

Rule of thumb: if removing a capability wouldn't stop the agent doing its job, remove it.

Step 4 — Write the guardrails down

Three things belong in writing for any agent you'll trust:

1. Prohibitions — the explicit "never do this" list (never email external contacts, never spend over $X, never touch production).
2. An autonomy level the tools enforce — don't say "read-only" and then hand it the keys. Match the power in Step 3 to the promise here.
3. Stop and escalation conditions — when is it done, when should it refuse, and when should it ask a human instead of guessing? An agent that can't say "that's outside my job — escalating" will instead do the wrong thing confidently.

Step 5 — Validate with scenarios

Until you've tested it, you don't have an agent — you have a hope. Run at least four cases:

• Happy path — a normal request. Does it produce the right result, in the right shape?
• Edge case — vague or partial input. Does it ask one good question instead of inventing an answer?
• Out of scope — a request just outside its job. Does it decline rather than attempt it?
• Guardrail probe — a request that breaks a prohibition. Does it refuse?

The last two matter most. An agent that never refuses is the one that eventually does something you can't undo.

The mistakes that sink most agents

• The god-agent — many jobs in one. Split it by responsibility.
• The inherit-everything agent — full access "to be safe", which is the opposite of safe. Grant the minimum.
• The vague trigger — "helps with stuff", so it gets used for the wrong things. Say when to use it.
• The yes-agent — no prohibitions, so it attempts anything. Write the refusals.
• The untested agent — shipped without a guardrail probe. You won't know it refuses until it doesn't.

Or let the Agent Builder do it for you

You can do all of this by hand — or describe your agent in plain English and our Agent Builder runs this exact method for you: it interviews the intent, picks least-privilege tools from a vetted catalog, writes the guardrails and autonomy level, generates runnable code, and hands you the scenario tests to prove it behaves. Not just a prompt — interviewed, guardrailed, and behavior-tested.

Not sure what to build yet? Find your best-fit agent in about two minutes.