How to add an AI agent to your product without breaking it

An AI agent is the easiest thing in the world to demo and one of the harder things to ship. The demo works because you drove it down a happy path you already knew. Production is different: real users ask things you did not anticipate, the model occasionally invents answers, costs scale with usage, and a confident wrong answer can do real damage. None of that means you should not build one — it means you should build it deliberately. Here is how we approach adding an agent to a real product.

First, decide if you actually need an agent

The word "agent" gets stretched to cover everything. Be precise, because the right tool saves you months:

• A single LLM call (summarise this, classify that, draft this reply) is not an agent. If that solves your problem, do that. It is cheaper, faster, and far easier to keep reliable.
• Retrieval-augmented generation — answering from your own documents — is often what people mean when they say "AI agent," and it is usually simpler than a true agent.
• A real agent plans, chooses tools, and acts over multiple steps toward a goal. You want this when the task genuinely requires deciding what to do next based on intermediate results.

Single LLM call

• Summarise, classify, draft
• Cheapest & fastest
• Easiest to keep reliable

Retrieval (RAG)

• •Answers from your own docs
• •What most 'agents' really are
• •Simpler than a true agent

True agent

• •Plans & chooses tools
• •Acts over multiple steps
• •Use only when the task needs it

Most products that ask us for "an agent" need one of the first two. Starting there is not settling — it is engineering.

Scope it to a job, not a personality

The agents that fail in production are the ones asked to "be helpful" in general. The ones that work are scoped to a specific job with a clear boundary. Our autonomous lead-generation agent does not chat about anything; it extracts prospect data, drafts a personalised proposal, and delivers it. Narrow scope is what makes it reliable enough to run without a human watching each step.

Define, on paper, exactly what the agent is allowed to do, what it must never do, and what it should do when it is unsure. That last one matters most.

Build the guardrails before the cleverness

The difference between a toy and a product is what happens when the model is wrong. Plan for it from the start:

1. Constrain the tools. An agent can only cause harm through the actions you give it. Give it the minimum. A tool that reads is safe; a tool that deletes needs confirmation and limits.
2. Validate every output. If the agent returns structured data, validate it against a schema and reject what does not fit. Never pipe raw model output straight into something that acts on it.
3. Give it an "I don't know." An agent that escalates to a human when uncertain is worth more than one that always answers. Confidence without correctness is the whole risk.
4. Log everything. You cannot improve or debug what you cannot see. Every decision, tool call, and input should be traceable.

Control cost and latency on purpose

Two things surprise teams after launch: the bill and the wait. Both are designable.

For cost, route by difficulty. Use a small, fast, cheap model for the easy majority of requests and reserve a larger model for the genuinely hard ones. Cache aggressively — many user questions repeat. Cap the number of steps an agent can take so a confused loop cannot quietly run up a bill.

For latency, stream results so the user sees progress immediately, do independent work in parallel, and be honest about which parts must be real-time and which can happen in the background. Not every task needs an answer in 300 milliseconds.

Evaluate like you mean it

"It seemed to work when I tried it" is not evaluation. Before launch, build a set of real examples — including the weird and adversarial ones — with known-good answers, and measure the agent against them every time you change a prompt or a model. Without this, every "improvement" is a guess, and you will regress silently. A small evaluation set you actually run beats a large one you admire.

The launch that does not blow up

Ship the agent to a slice of traffic first, with a human able to see what it is doing and a fast switch to turn it off. Watch the logs, not the vibes. The failures you learn from in the first week with ten percent of users are far cheaper than the ones you discover with everyone.

Done this way, an AI agent stops being a risky novelty and becomes a dependable part of your product.

Frequently asked questions

Do I actually need an AI agent, or something simpler?

Usually simpler. A single LLM call handles summarising, classifying, or drafting; retrieval-augmented generation answers from your own documents. A true multi-step agent is only worth it when the task genuinely requires deciding what to do next based on intermediate results. Start with the simplest tool that solves the job.

How do you stop an AI agent from doing damage?

Guardrails before cleverness: constrain the tools it can use to the minimum, validate every output against a schema before acting on it, give it a way to say 'I don't know' and escalate to a human, and log every decision so you can debug and improve it.

How do you control the cost and latency of an AI feature?

On purpose. Route easy requests to a small, cheap, fast model and reserve a larger one for hard cases; cache aggressively since many questions repeat; cap the number of steps an agent can take; and stream results so users see progress instead of waiting.

How should I launch an AI agent safely?

Ship it to a slice of traffic first, with a human able to see what it's doing and a fast switch to turn it off, and evaluate it against a curated set of real examples every time you change a prompt or model. The failures you catch with 10% of users are far cheaper than the ones you find with everyone.