My AI Agent Isn't Allowed to Decide Anything

说明与用途

Shot‑Delivery Guardian 使用 AI 分析电影制作状态，并建议可以延迟的项目，以避免错过截止日期，同时遵守客户批准、导演优先级和依赖关系。

问题

When a film or TV show pipeline stalls, a human must quickly decide which work to delay to avoid missing deadlines, but this manual process is slow and error‑prone.

结果

In live tests, the system matched the fixed rule’s recommendation 60% of the time, identified backlog causes in 44 seconds on average, and never violated any of the safety constraints (client approvals, director flags, or downstream dependencies).

工作流

• 1. Monitor pipeline stages for backlog and deadline status.
• 2. Query three monitoring tools: overall backlog, work item metadata, and detailed stage timing.
• 3. Apply the fixed rule to filter out client‑approved, director‑flagged, or dependent items.
• 4. Rank remaining items by slack hours and select the one with the most spare time.
• 5. Output the recommendation as a note on the operator’s dashboard.
• 6. Operator reviews and confirms the delay decision.

证据

I built a system that watches the work as it moves through each step, notices when a deadline is about to be missed, figures out why, and says which piece of work is safe to push back.

来源

The AI never got an important decision wrong, because I never let it make one.

来源

def score_bump_candidates(shots):
eligible = [
s for s in shots
if not s["client_approved"]
and not s["director_flagged"]
and not has_downstream_dependents(s, shots)
]
return sorted(eligible, key=lambda s: slack_hours(s), reverse=True)

来源

During one real test, the system hit a real problem reaching one of its three tools halfway through. It said so, plainly, in its own words.

来源

3 of 5 real tests matched what the fixed rule alone would've picked. About 44 seconds on average to look into a problem and respond, timed against the real live system. Zero broken rules across every single test.

来源