How AI Workflows Really Work (Part 1/2)

Why It Matters

Structured AI workflows give businesses predictable, cost‑effective automation, turning LLMs into reliable enterprise tools.

Key Takeaways

• AI workflows orchestrate LLM calls with predefined code paths.
• Sequential pattern chains LLM outputs for stepwise task refinement.
• Programmatic checks validate each LLM output before proceeding.
• Routing pattern classifies inputs to direct appropriate model usage.
• Mix cheap and expensive models to optimize cost and performance.

Summary

The video introduces AI workflows, systems where developers stitch together large language model (LLM) calls and external tools using predefined code paths. Unlike ad‑hoc prompting, the developer explicitly defines each step, letting the LLM handle heavy lifting while the surrounding logic controls execution.

Two core patterns are highlighted. The sequential pattern breaks a task into ordered stages—e.g., one LLM drafts an email, a second refines it—allowing programmatic validation between calls. The routing pattern first classifies the user’s request, then dispatches it to the most suitable handler, enabling model selection based on difficulty.

Concrete examples include an email‑draft‑and‑review loop and a classifier that routes simple queries to a lightweight model while reserving a premium model for complex travel planning. The presenter emphasizes that developers can embed checks, such as schema validation, before feeding output to the next LLM.

By structuring LLM interactions as deterministic workflows, teams gain predictability, easier debugging, and cost control. Enterprises can scale AI services while balancing performance and expense, making workflow design a strategic capability in the emerging AI stack.

Original Description

Most "AI agents" aren't actually agents — they're workflows. 👀 (Part 1/2)

Here's the truth every AI developer learns eventually: production AI systems aren't magic. They're predefined code paths where the LLM does the heavy lifting at each step, but YOUR code controls the flow.

The 2 patterns we're covering in Part 1:

🔗 Prompt Chaining — LLM #1 writes the email, LLM #2 reviews & polishes it. Add checks between steps.

🧭 Routing — a classifier LLM reads the input and sends it to the right handler. Cheap models for easy tasks, expensive models for the hard ones.

🎬 Part 2 drops soon — parallelization, orchestrators & evaluator patterns.

💬 Which pattern are you already using? Drop it below.

🔖 Save this for Part 2.

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