Sample article — starter content for NeuralSys.

Introduction

Fully autonomous agents make great demos and fragile products. The pattern that survives production is the agentic workflow: deterministic scaffolding with LLM judgment at narrow, well-defined decision points.

The Spectrum

Script ──→ Workflow ──→ Planned ──→ Autonomous
(most reliable)              (most flexible)

Start left. Move right only where measurement justifies it.

Four Patterns That Work

1. Route, then execute

Classify the task once, then run a specialized path. One smart decision beats ten clever ones.

2. Generate, then verify

Let the model draft; verify with code, tests, or a second model with different instructions.

draft = llm.generate(spec)
verdict = verifier.check(draft, tests=acceptance_tests)
if not verdict.passed:
    draft = llm.repair(draft, verdict.failures)  # bounded: max 2 repairs

3. Decompose with contracts

Break tasks into steps with typed inputs/outputs. Each step is independently testable — the workflow becomes a program you can debug.

4. Human checkpoints at irreversibility

Reads are cheap, writes are expensive. Put approvals before external side effects: sending, deleting, publishing, spending.

Key Takeaways

  • Workflows beat free-form autonomy on reliability per dollar.
  • Verify with something other than the generator.
  • Typed step contracts turn debugging from archaeology into engineering.