Why AI agents need more than a good LLM to work in production
Appropriate-Head7342 · reddit · 2026-10-15
A long-form piece on deploying AI agents in real business environments, arguing the gap between a working demo and a reliable production agent is huge.
Key points:
- Data accessibility beats a more powerful model: business data scattered across CRMs, databases and spreadsheets must be mapped, along with permissions, before adding agents
- Not everything should be fully automated: split workflows into automated (low-risk, repetitive), human-approved (financial/customer impact) and human-led (judgment calls)
- Integration is the hardest part: auth failures, API limits, format drift — mitigate with tool permissions, input validation, error handling, retries and logging
- Reliability needs more than prompt engineering: structured evals and monitoring, tracking task completion rate, human intervention frequency, cost per task, failure/recovery rates
- Security belongs in the architecture: least-privilege access, controlled sensitive actions, auditability, plus defenses against prompt injection and data leakage
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