Most AI agent demos are theatre. A clean prompt, a happy path, and a screen recording. The version that runs in production for six months without paging someone at 2am looks very different.
After shipping agents for ops, research, support, and content workflows, we keep coming back to the same handful of lessons.
Start from a workflow, not a model
The wrong question is “what can GPT-4 do for us.” The right one is “which workflow is currently swallowing hours of senior time, and what would it take to hand most of it to an agent.” Pick the workflow first. The model is an implementation detail.
Tools matter more than prompts
A great prompt with weak tools produces a confident agent that hallucinates. A modest prompt with sharp, well-typed tools produces a reliable one. Spend the time designing the tool surface: clear names, narrow inputs, predictable outputs, useful error messages. Your agent only acts as well as the tools you give it.
Evals are not optional
If you cannot measure whether the agent is getting better or worse, you are flying blind. Even ten hand-curated test cases beat zero. Run them on every prompt change. Track regressions like you would track failing unit tests.
Guardrails belong on the outside
Trying to make the model “behave” through prompt engineering is a losing game. Put hard limits where you can enforce them: input validation, allowlists for tool calls, output schemas, rate limits, and human approval gates for anything irreversible.
Observability or it didn’t happen
Log every run. Capture the input, the tool calls, the model output, the latency, the cost. When something goes wrong - and it will - you need the trace. LangSmith, Helicone, or your own table in Postgres all work. Pick one and use it from day one.
Cost discipline
Token costs sneak up. A workflow that costs cents per run becomes hundreds of euros a day at scale. Cache aggressively, use smaller models where you can, and put a hard ceiling on tokens per run. The boring engineering wins here.
Ship narrow, then expand
The agents that survive started by doing one thing. A research summariser. A support triage helper. An invoice extractor. They earned trust by being right on the narrow case, and only then took on more. The “do everything” agent is almost always the one that gets quietly switched off.
Production-grade does not mean fancy. It means the system is honest about what it can do, observable when it fails, and cheap enough to run all year. Get those right and the rest is a matter of compounding improvements.
Tags