Most AI agent demos are impressive. A chatbot that books meetings, an assistant that writes code, a support bot that resolves tickets — the demos look magical. But deploy that same agent into production with real users, real edge cases, and real consequences, and the magic evaporates fast.
We've shipped AI agents across healthcare, e-commerce, and financial services. Here's what we've learned about building systems that don't just demo well — they work reliably under pressure.
The Demo-to-Production Gap
The gap between a demo agent and a production agent is enormous, and it's not about the model. GPT-5, Claude Opus, Gemini 2.5 Pro — today's frontier LLMs are all impressively capable in controlled settings. The problems emerge at the edges: ambiguous inputs, adversarial users, unexpected data formats, latency spikes, and the hundred other things you didn't think to test.
In one deployment, our AI support agent handled 95% of test queries flawlessly. In production, that number dropped to 72% in the first week. Not because the model was worse — because real users ask questions in ways that test datasets never capture. They send voice-to-text messages with typos. They ask three questions in one sentence. They provide context that contradicts their actual question.
Lesson 1: Deterministic Guardrails Around Probabilistic Systems
The single most important architectural decision is wrapping your probabilistic AI components with deterministic guardrails. Never let the model be the sole decision-maker for anything with real consequences.
In practice, this means: the AI classifies the intent, but a rules engine validates the action. The model extracts data from documents, but a validation layer checks the output against known schemas. The agent drafts a response, but a filter checks for hallucinations against the source material.
This hybrid approach — AI for understanding and generation, rules for safety and validation — is what separates production-grade agents from demo-ware.
Lesson 2: Retrieval Quality Beats Model Quality
Teams obsess over which LLM to use. In our experience, the choice of retrieval strategy matters 10x more than the choice of model. A mediocre model with excellent retrieval will outperform a cutting-edge model with poor retrieval every single time.
For our healthcare support agent, we spent three weeks on retrieval pipeline optimization — chunking strategy, embedding model selection, metadata enrichment, re-ranking — and one afternoon on prompt engineering. The retrieval work was responsible for 80% of the quality improvement.
Key retrieval decisions that matter: chunk size (smaller is usually better for Q&A, ~200-400 tokens), overlap percentage (10-20%), embedding model (we've had the best results with specialized domain models over general-purpose ones), and metadata filtering (pre-filtering by category before semantic search dramatically improves relevance).
Lesson 3: Build for Graceful Degradation
Your agent will fail. The question is whether it fails gracefully or catastrophically. Every production agent needs a clear escalation path: if confidence is below a threshold, if the query touches a sensitive topic, if the user explicitly asks for a human — the handoff needs to be seamless.
We build a three-tier confidence system: high confidence (agent responds directly), medium confidence (agent responds but flags for review), low confidence (agent acknowledges the question and escalates immediately with full context). The thresholds are tuned per deployment based on the cost of errors.
Lesson 4: Observability Is Not Optional
You need to see every decision your agent makes. Every retrieval, every classification, every response generation — logged, searchable, and connected to user feedback. Without this, you're flying blind when things go wrong.
We instrument our agents with: input classification logs (what did the system think the user wanted?), retrieval traces (what documents were pulled and ranked?), generation metadata (what prompt was used, what was the token count, what was the latency?), and outcome tracking (did the user get their answer, did they escalate, did they come back with the same question?).
This observability data is also your training data for the next iteration. Every failure mode you identify becomes a test case. Every pattern you spot becomes a fine-tuning opportunity.
Lesson 5: Start Narrow, Expand Deliberately
The biggest mistake we see is trying to build a general-purpose agent from day one. The agents that succeed in production start extremely narrow — handling one specific workflow, for one specific user type, with one specific set of knowledge — and expand deliberately based on real usage data.
Our most successful deployment started with an agent that did exactly one thing: answer questions about appointment scheduling for a healthcare provider. It didn't handle billing questions, insurance inquiries, or medical advice. When a user asked about those topics, it said 'I can help with scheduling — for other questions, let me connect you with our team' and escalated cleanly.
After two months of data, we expanded to cover insurance eligibility checks. Then appointment preparation instructions. Each expansion was validated against real user queries we'd been logging, so we knew exactly what to build next.
The Bottom Line
Building AI agents that work in production is less about AI and more about engineering discipline. The model is the easy part. The hard parts are: error handling, observability, graceful degradation, retrieval quality, and the patience to start narrow and expand based on evidence.
If you're evaluating whether AI agents can work for your use case, the answer is almost certainly yes — but only if you're willing to invest in the engineering around the AI, not just the AI itself.