Building Reliable AI Workflows: Retries, Fallbacks, and Cost Control

Every team building with LLMs hits the same wall: the API that worked perfectly in development starts failing in production. Rate limits. Timeouts. Unexpected model outputs. A $200 bill from a runaway loop. Here's how to build AI workflows that handle all of this.

1. Automatic retries with backoff

LLM APIs are unreliable by nature. OpenAI returns 429s during peak hours. Anthropic occasionally 500s. Google's API has cold start latency. Your workflow engine needs to handle this transparently.

The pattern: retry with exponential backoff. First retry after 1 second, then 2, then 4. Add jitter to prevent thundering herd. Cap at 3 attempts per step. If all fail, mark the step as failed and let the workflow-level retry policy decide what happens next.

2. Provider failover

Don't depend on a single LLM provider. When your primary (say, Claude) is down, your workflows should automatically try the next provider in line (GPT-4o, Gemini). The key challenge is format normalization — each provider has different message formats for tool calls, system prompts, and function results.

The solution is an internal message format that's provider-agnostic. Your workflow state stores messages in this format. Each provider adapter translates to and from the native format. When a failover happens mid-conversation, the adapter handles the translation transparently.

3. Cost tracking and budget limits

An agent in a tool-use loop can burn through tokens fast. Without limits, a single misbehaving workflow can cost hundreds of dollars before anyone notices.

Every LLM call should record: model used, input/output tokens, computed cost. Aggregate this per workflow run and per workspace. Set a max budget — when a workspace hits its limit, new LLM steps are blocked with a clear error instead of silently running up the bill.

4. Human-in-the-loop approvals

Not every decision should be automated. High-stakes actions — sending an email, modifying a database, making a payment — should require human approval. The workflow pauses, sends the approval request to a dashboard, and resumes when someone approves or rejects.

This isn't just a safety feature. It's how you build trust. Users are more willing to give agents autonomy when they know there's a safety net for important decisions.

5. Prompt injection scanning

If your agent processes user input, it's a target for prompt injection. A malicious user can craft input that tricks the LLM into ignoring its system prompt, leaking data, or executing unintended actions.

Layer your defenses: fast heuristic scanning (regex patterns for common injection techniques) followed by ML-based detection for sophisticated attacks. Block flagged inputs before they reach the LLM.

6. Observability

When a workflow fails at 3 AM, you need to know why. Record every LLM call with its full prompt, response, token count, duration, and cost. Record every tool invocation with input/output. Build an immutable event timeline for each workflow run.

This isn't just for debugging. It's how you optimize. Which model is cheapest for this task? Which steps are bottlenecks? Where are tokens being wasted?

Putting it together

These aren't nice-to-haves. They're table stakes for production AI workflows. You can build all of this yourself, or you can use an execution runtime that handles it for you.