Building Production AI Agents: What Nobody Tells You
Everyone's building AI agents. Almost nobody's running them in production. The gap between a demo that works in a notebook and a system that handles real enterprise workloads is enormous, and it's not the gap most people think it is.
I've spent the last year building agent systems — tools that automate quoting workflows, parse vendor catalogs, and orchestrate multi-step processes across ConnectWise, Ingram Micro, and Pax8 APIs. Here's what actually matters when you move past the tutorial phase.
The evaluation problem is the real problem
Your agent works great when you test it with the five examples you have in your head. Then it hits production and encounters the sixth example — the one where a customer sends a PDF quote with line items in a format you've never seen, or where the API returns a 200 with an error buried in the response body.
You need evaluation frameworks before you need more features. I'm not talking about vibes-based testing where you eyeball the output and say "yeah, that looks right." I mean structured evaluations with defined metrics, edge case datasets, and regression tracking.
Build your eval suite first. Then build the agent. Every time you fix a failure mode, add it to the suite. This is the flywheel that makes agents actually reliable.
Context windows are not infinite
Yes, Claude and GPT-5 have massive context windows. No, you should not dump your entire knowledge base into every request. Context window size is a ceiling, not a target.
In practice, I've found that carefully curated context — retrieved via vector search, filtered by relevance, and structured with clear delimiters — outperforms "throw everything in and hope the model figures it out" by a significant margin. And it's cheaper. And faster.
The vector database isn't optional infrastructure. It's the core of your agent's ability to make good decisions.
Error handling is your entire product
In traditional software, errors are exceptions. In agent systems, errors are the normal case. The LLM will hallucinate. The API will return unexpected data. The user's input will be ambiguous.
Your agent needs to know when it doesn't know. Build explicit confidence scoring. Build fallback paths. Build human-in-the-loop escalation for cases where the agent can't proceed with high confidence.
The agents I've shipped that actually work in production spend more code on error handling and graceful degradation than on the "happy path" LLM calls.
The boring parts matter most
Nobody wants to hear this, but the most impactful work in production AI is: logging, monitoring, cost tracking, rate limiting, and caching. It's not glamorous. It's not going to get you Twitter engagement. But it's the difference between a system that runs reliably at scale and one that burns through your API budget in a weekend while silently producing garbage.
Track every LLM call. Log inputs, outputs, latency, token counts, and costs. Build dashboards. Set up alerts. This is production engineering — the AI part is almost incidental.
Ship, measure, iterate
The best agent is the one that's running in production and getting better every day. Don't wait for perfect. Ship something that handles the 80% case reliably, build the instrumentation to catch the 20% that fails, and iterate.
That's the actual playbook. Everything else is marketing.