Shipping Your First AI Agent: A Field Guide
The gap between a slick AI demo and a reliable production agent is enormous, and it is where most projects quietly stall. A chatbot that dazzles in a sales meeting will happily hallucinate a refund policy, loop on a broken tool call, or burn through your token budget on a Tuesday afternoon. Shipping an agent that earns its keep is an engineering discipline, not a prompt-writing exercise.
Here is the field guide we hand new clients before we write a single line of orchestration code.
Pick a narrow, measurable job
The first mistake is scope. "An assistant that answers anything" has no success criterion and therefore can never be finished. "An agent that drafts replies to order-status questions and escalates everything else" can be measured, tuned, and trusted. Start with one workflow where a wrong answer is recoverable and a right answer saves real minutes.
An agent without an eval suite is a slot machine you are paying to pull.
Build the evaluation harness first
Before optimising prompts, assemble fifty to a hundred real examples with known-good outcomes. This becomes your regression test. Every prompt tweak, model swap, or tool change runs against the suite so you can see whether you actually improved or just moved the failures around. We treat eval pass-rate the way we treat unit-test coverage: it gates the release.
Tools, not just text
The leverage in modern agents comes from tool use, giving the model functions it can call to read a database, hit an API, or trigger a workflow. Keep each tool small, strongly typed, and idempotent. Validate every argument the model produces before execution, because the model will eventually pass a malformed payload.
- Return structured results the model can reason over, not raw HTML.
- Make every action reversible or require human confirmation for destructive ones.
- Log every tool call with inputs and outputs for debugging and audits.
Ground it in your data
Retrieval-augmented generation reins in hallucination by feeding the model relevant snippets from your own knowledge base at query time. The quality of retrieval matters more than the cleverness of the prompt: chunk documents sensibly, keep embeddings fresh, and always cite sources so a human can verify. An agent that says "I do not have that information" is far more valuable than one that confidently invents it.
Choose the right model for each step
One of the cheapest wins is refusing to use your largest, most expensive model for everything. A single agent task often decomposes into several steps with very different difficulty: a fast, cheap model can classify intent or extract fields, while the flagship model handles the genuinely hard reasoning. Routing each step to the smallest model that passes its evals can cut cost by half or more without users noticing. Build the routing in early, because retrofitting it once everything assumes the big model is painful.
Guardrails and graceful failure
Production agents need limits the demo never had. Cap tokens and tool iterations to prevent runaway loops. Add a confidence threshold below which the agent defers to a human. Strip prompt-injection attempts from untrusted input, and never let user-supplied text be treated as trusted instructions. And instrument cost per conversation so a pricing surprise shows up on a dashboard, not an invoice. The agents that survive in production are the ones engineered to fail safely and visibly, handing off cleanly when they are out of their depth rather than improvising past it.
- Define the one job and its success metric.
- Write the eval set before the prompt.
- Add tools incrementally, each behind validation.
- Ground answers in retrieval with citations.
- Ship behind guardrails, then widen scope on evidence.
Done well, a focused agent quietly absorbs a category of repetitive work and frees your team for the judgement calls only people should make. If you have a workflow that feels ripe for one, tell KadamTech about it and we will help you scope an agent that survives contact with real users.
