Agentic Workflows in Production: What Actually Breaks

Agent demos collapse three problems into one impressive video: planning, tool use, and recovery. In production, those three problems separate again and each fails differently. Planning failures look like the agent forgetting why it started a task. Tool failures look like silent retries against a rate-limited API. Recovery failures look like a confidently wrong final answer.

The teams running agents successfully in production almost always converge on the same pattern: short horizons, hard checkpoints, explicit memory contracts, and aggressive observability. They treat the LLM as the most expensive, least reliable subsystem in the stack and design around that constraint, not against it.

The agencies and product teams shipping these systems — the engineering-led ones that take custom AI delivery seriously, not the prompt-engineering bootcamps — are the early winners of this cycle.