Five Patterns for Building Reliable AI Workflows

LLMs are non-deterministic. The same prompt can return different output on different runs, and models occasionally hallucinate, time out, or return malformed data. This is fine for a demo — it's a real problem when an AI workflow is running in production and affecting real users.

Here are five patterns we've seen work well for teams that need reliability.

1. Always use structured outputs

Free-form text output is difficult to work with downstream. If you need an LLM to produce data that another step will consume, define the schema explicitly and instruct the model to return JSON.

Return a JSON object with this exact structure:
{
  "summary": string,
  "action_items": string[],
  "priority": "low" | "medium" | "high"
}

Most modern models respect this reliably. Validate the response before passing it forward — if parsing fails, treat it as a retryable error.

2. Design for retries from the start

Transient failures are inevitable: rate limits, network timeouts, temporary API unavailability. Build your workflows to handle these without losing state.

The key is making each node idempotent — running it twice with the same input should produce the same result and have no unintended side effects. If you're writing to a database, use upsert semantics. If you're sending a notification, check first whether it's already been sent.

With idempotent nodes, retry logic becomes safe and simple.

3. Add a human gate before irreversible actions

Some actions can't be undone: sending an email to a customer list, deleting records, posting to a public channel. For these, insert a human-in-the-loop approval step before the action fires.

The workflow pauses, the approver gets a notification with the relevant context, and they approve or reject. Only then does execution continue.

This isn't about distrust of the AI — it's about appropriate oversight for high-stakes operations. A five-second approval step is cheap insurance against an expensive mistake.

4. Log inputs and outputs at every step

When something goes wrong in a multi-step workflow, the first question is: which step failed, and what data did it receive? If you're not capturing per-step inputs and outputs, this question can take a long time to answer.

Log everything. Storage is cheap. Debugging time is not.

Treat these logs as structured data, not just text — you'll want to query them. "Show me all runs where the sentiment analysis step returned 'negative' and the follow-up email step failed" is a useful query. It's only possible if both results are stored in a queryable format.

5. Separate the trigger from the logic

A common pattern is to write workflow logic that's tightly coupled to how it's triggered — the cron job that fetches data also processes it also sends the notification. When you need to test just the processing step, you have to simulate the whole chain.

Better: have your trigger (cron, webhook, manual run) produce a standardised input payload, and have your workflow logic operate on that payload. The trigger is just a delivery mechanism. The logic is independently testable.

This also makes it easy to add new trigger types later without touching the core logic.

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These patterns aren't AI-specific — they're just good software engineering applied to a new context. The teams we've seen build the most reliable AI systems are the ones who treat their workflows with the same rigour they'd apply to any production service.

If you're building workflows on CipherSense Agents, all of these patterns are supported natively: structured output validation, configurable retries, human-in-the-loop nodes, full step-level logging, and a clean separation between triggers and execution.