Building AI Agents That Actually Work in Production
Building AI agents is easy. Building AI agents that work reliably in production is a different discipline entirely.
After shipping several agent systems — from customer support bots to autonomous data extraction pipelines — here are the lessons that actually matter.
1. Make Every Step Observable
The single most important thing you can do is log everything. Every LLM call, every tool invocation, every decision point. When an agent fails (and it will fail), you need to know exactly what it decided and why.
We use structured logging with correlation IDs so every agent run can be traced end-to-end.
2. Design for Failure, Not the Happy Path
Agents operate in open-ended environments. APIs go down. LLMs return malformed JSON. External websites change their structure. Your agent system needs to handle all of this gracefully.
Build retry logic, fallback strategies, and human-in-the-loop escalation for cases the agent can't handle.
3. Start with Narrow, Bounded Tasks
The agents that succeed in production are those with narrow, well-defined tasks. "Summarize this support ticket" succeeds. "Handle all customer issues" fails.
Start small, prove reliability, then expand scope incrementally.
4. Test with Real Inputs, Not Cherry-Picked Examples
Demos always work because we pick inputs we know the agent handles well. Production fails because the real world is messy. Build a test suite with adversarial inputs: malformed data, edge cases, deliberately ambiguous requests.
5. Model Costs Add Up Fast
A single GPT-4 call costs fractions of a cent. At scale, those fractions become hundreds of dollars. Design prompts to be efficient, use cheaper models for simple subtasks, and implement caching for deterministic lookups.
Building agent systems that work at scale requires engineering discipline, not just prompt engineering. If you're thinking about adding AI agents to your product, get in touch — we've shipped these systems before.