Designing AI workflows that survive real users
Orchestration, evals, and the ops discipline that separates demos from production systems.
AI demos are easy. AI workflows that work for real users — at scale, under adversarial input, across edge cases nobody modelled — are an entirely different engineering discipline. The gap between demo and production is where most AI projects fail, and it is almost never a model quality problem.
The eval harness comes first
Every AI workflow we build starts with the evaluation harness, not the model. Before writing a single prompt, we define what correct looks like: a set of representative inputs, expected outputs, and a scoring function. This sounds obvious. Most teams skip it, build around a model they like, and discover the problem six months into production when something quietly breaks.
Orchestration patterns that hold up
Single-model, single-prompt pipelines break under production load. Robust AI workflows use orchestration — routing inputs to different models or sub-pipelines based on complexity, confidence, or type. A classification step at the top of the pipeline routes simple requests to a cheaper model and complex ones to a more capable one. Latency drops. Cost drops. Quality holds.
- Route by confidence — low-confidence outputs go to human review, not the user
- Use structured outputs to make parsing deterministic
- Instrument every step, not just the final output
- Design the fallback path before the happy path
The ops discipline that is always missing
Production AI workflows need the same operational discipline as any other service: alerting on failure rates, logging inputs and outputs for debugging, cost monitoring per request, and a rollback path when a model update breaks evals. Most teams add these after something goes wrong. Add them before launch.
The best AI systems are boring in production. No surprises means the instrumentation is working.
Checklist before you ship an AI workflow
- Eval set exists with >50 representative cases
- Failure modes are enumerated and handled explicitly
- Human-in-the-loop queue exists for low-confidence outputs
- Cost per successful output is measured and acceptable
- Rollback path tested — what happens if the model endpoint is unavailable?
AI Business Automation
AI workflows, GenAI assistants, RAG, AI summaries, decision support, and intelligent dashboards — applied where they move a business outcome.
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