Taming the Ai Hydra: From Demo to Durable System
Most Ai pilots fail—not for lack of talent, but because Ai ships without scaffolding. This article shows how Agile, Lean, GRASP, and object-oriented discipline turn impressive demos into dependable systems.
Why pilots stall
Pilots often “wow” in isolation, then wobble in production. Inputs shift. Prompts drift. Retrieval changes under load. Without anchors, the system forgets agreements. Without loops, teams learn slowly. Without governance, every fix risks a new break.
Day Zero discipline
Before we scale anything, we create a minimal scaffolding: immutable backups, explicit anchors, and a health check. Day Zero is not a pause—it’s the fastest path to reliable iteration.
- Backups: prompts, configs, evaluation sets, and retrieval snapshots.
- Anchors: clear contracts for style, facts, and behavior that persist across resets.
- Health check: a tiny suite that catches drift before customers do.
Small loops, fast proof
Swap waterfall plans for short loops: two steps, test; not twenty. Each change runs through a repeatable evaluation harness, producing guardrailed progress instead of brittle heroics.
Treat GPTs like objects, not oracles
GRASP and OO discipline give Ai systems boundaries. Encapsulation reduces cross-bleed. Contracts define inputs and outputs. Composition keeps capabilities modular. In practice: separate retrieval, reasoning, and rendering; keep state small and explicit; prefer messages over side effects.
Governance beside innovation
Governance runs alongside delivery, not after it. We version prompts, track datasets, and log decisions. Failures become instruction, not folklore. When leadership asks “What changed?”, there’s a crisp answer.
What “good” looks like
- Anchors that preserve tone, facts, and boundaries across resets.
- Eval sets that reflect real tasks, not synthetic trivia.
- Observability that catches drift in hours, not quarters.
- Runbooks that make releases boring—in the best way.
From POC to platform: the pivot
The moment a pilot hits value, the goal changes: protect what works, scale what matters. That means codifying today’s behavior (anchors), tightening feedback cycles (loops), and wrapping innovation with telemetry and guardrails (governance).
Want this installed by practitioners? AgiLean.Ai can deploy the Day Zero scaffolding, set up evaluation and telemetry, and stand up two thin-slice wins to prove reliability before you scale.