Why your first agent should be boring
Excitement kills production systems. Start with the most boring agent possible and let the business decide when to crank the voltage.
If you're about to build your first agent, the advice is counterintuitive: make it as boring as possible. No tools that write to the database. No multi-step orchestration. No fancy self-healing. One input, one output, one decision.
The LLM-as-demo bias
The trap: LLMs in demos do spectacular things. That seduces you into starting big — an agent that classifies, escalates, writes, sends emails, books calls. When production hits, each of those steps is an independent failure point. And no eval suite covers the combinatorics of cascading failures.
The first agent isn't an MVP. It's the mental infrastructure your team needs to trust LLMs.
What your first agent should actually have
- A structured input — not a free-text prompt
- A binary or categorical decision — not open-ended generation
- An explicit fallback — if confidence < threshold, escalate to a human
- Logs a PM can read — not raw JSON
Three months running something like that gets you:
- Real data to train the next version
- A baseline of cost / latency / quality
- A team that understands where the model is wrong
What comes next
Once your boring agent has 90 days in production without surprises, then you add the second step. And only one at a time. Excitement is the enemy of systems that last.