How to Build Conditional Logic with AI: 5 Patterns (2026)
Learn how to use AI models to make dynamic routing decisions in your automation workflows — replacing rigid if/else trees with intelligent, context-aware branching.
AI workflows
How to design, build, and operate AI-powered workflows that hold up in production. The guides here are written by people who have shipped automation that survived its first outage.
Key takeaways
- Start with ONE workflow that touches revenue. Resist the "automate everything in week one" trap.
- Always ship a human-review branch before fully autonomous routing. The first mis-routed lead destroys trust faster than the time you saved.
- Cap any LLM loop (self-correction, retry, reflection) at 2–3 iterations. Past that, latency doubles and accuracy gains vanish.
- Log every (input, AI output, decision, outcome) row to Postgres. That log becomes your debug timeline AND your fine-tuning corpus.
Frequently asked questions about this category
What is an AI workflow vs. a regular automation?
A regular automation is deterministic: same input → same output every time. An AI workflow includes at least one step where an LLM makes a judgement call (classify, summarise, route, generate). The added flexibility comes with new failure modes — hallucinations, drift, cost spikes — that pure automation does not have.
Which tool should I use to build my first AI workflow?
n8n self-hosted on a $10 VPS for anything past 5,000 runs/month. Zapier for under 1,000 runs/month where the integrations matter more than the cost. Make sits in the middle when you want a visual builder without Zapier’s price ceiling.
How do I keep AI workflow costs under control?
Three rules: cap retries at 2, route the easy 80% to GPT-4o-mini or a local model, and add per-key rate limits in Redis. Most runaway bills come from uncapped retry loops over a frontier model.
Should I use an AI agent framework like LangChain or CrewAI?
Only for genuinely multi-step tasks with open-ended tool use. For 90% of business workflows, a flat n8n pipeline with one or two LLM nodes is cheaper, faster to debug, and easier to hand off.
How do I monitor an AI workflow in production?
Log to Postgres on every step, ship request errors to Sentry, and run a daily summary of (cost, latency p95, accuracy sample). Without all three, an AI workflow that silently degrades will not surface until a customer complains.
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