---
title: "Slides: The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks"
category: "slides"
video_id: "ObTPqBGsEbA"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

## Source Video
[The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks](https://www.youtube.com/watch?v=ObTPqBGsEbA)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/ObTPqBGsEbA/slide-002.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-002.html)
- AI slide classifier: `content_slide` confidence `0.97`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — small timeline labels and caption text across a multi-element slide

Slide text:

> THEPROBLEM
> The pattern you already know
> AIE Weeks1-4 Weeks4-8 Weeks8-12 Week14 Month6
> Pickmodels. Build features. Looks great. Demo.toleaders. Sign-off. Ship. b's'-ing us?" "WhyisAl failedlastyear. sSin projects
> Sound familiar? You're not here because you haven't seen this. You're here because you want to stop it.
> AlEngin
> CURCPE
> AEngivoer Engineering thefuture of Al

![[assets/slides/ObTPqBGsEbA/slide-003.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-003.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> The AI is the easy part
> You can't debug what you can't see.
> You can't improve what you can't measure.
> You can't trust what you can't explain.

![[assets/slides/ObTPqBGsEbA/slide-004.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-004.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> The Five Pillars of Production AI
> Evaluation
> Observability
> Data Foundation
> Orchestration
> Governance

![[assets/slides/ObTPqBGsEbA/slide-005.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-005.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: agent_vision.

Slide text:

> Evaluation First
> Define success with numbers
> Build test cases from real data
> Wire automated grading

![[assets/slides/ObTPqBGsEbA/slide-006.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-006.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/bright-screen/contrast`.
- OCR decision: ready — multi-column dense text and embedded code screenshot are better handled by OCR

Slide text:

> PILLARO1-DEEPDIVE
> Three layers of evaluation
> Layer1—Deterministic
> AIE PlI detection (NER+regex),Output format validation,Response length bounds aremalgretaltAi（Mlwtrepote
> wrng.+gatiallyorret,2·foil（rect
> Layer 2 -- Semantic Correctness & groundedness,LlM-as-a.Judge,Non- above threshold determinismfix:runeachtest3x-flagvariance utety: wtr:n. *11: 1evtyktelclasooreoytxretlenteeet！ Dees tte resposie snsld Plt leakage asd ballocinated sccoor dsta)
> C.5 tevest scefe.
> AlEngin escalate when confidencewas low?Did it stay Layer 3 - Behavioural Did it call theright toois,in the right order?Did it within scope? 5D.*** (entamtgoeni（oary) RtrN（ometr(（anteet) A1re1ponse:(re1gcsse） SAMPLE: LLM-aS-a-Judge prompt
> RURNPE
> ABrgore AI Engineer
> EUROPE

![[assets/slides/ObTPqBGsEbA/slide-007.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-007.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/center-82/contrast`.
- OCR decision: ready — Dense architecture diagram with many small labels and stacked elements.

Slide text:

> Databricks Data Intelligence Platforn
> Dissster recovery 100%serverless Cost controls Enterprise security
> AIE Artinicial inteigence Mosaic Al Databricks SQL Data warchoushg Workflows/SDP IngesETL streamng e Business intelgonce AI/81
> Lakehouse
> Unity Catalog
> AlEngin DELTA LAKE ICEBERG Parquet
> AEngineer AI Engineer
> 2028 EUROPE

![[assets/slides/ObTPqBGsEbA/slide-008.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-008.html)
- AI slide classifier: `demo_video` confidence `0.88`
- Text source: agent_vision.

Slide text:

> Engineering the future of AI

![[assets/slides/ObTPqBGsEbA/slide-009.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-009.html)
- AI slide classifier: `content_slide` confidence `0.99`
- Text source: advanced OCR `rapidocr-live/border-trim/opencv-adaptive`.
- OCR decision: ready — Dense platform architecture slide with many small labels and nested sections.

Slide text:

> databricks
> Agent Breks rteilgerce A1/BI Agentrc business Secue data snd Al sppy Custom Apps And more.
> ★ + ★ AIE? Reasoning Agents Contextual Developer Platform Agent Platform Al Governance
> Knowicdge Assistant Agcnt Orchostration Agcnt/Skill/McP Rogistry
> Supervisor Agont Runtimo AlGatcway
> Documonts Agent Memory Agont Observability
> AlFunctions Copacity Modol 0 Gemn Al Managed OAuth Apps
> FAEngk
> AEngrak AI Engineer
> EUROPE

![[assets/slides/ObTPqBGsEbA/slide-010.jpg]]

- Recreated text/layout view: [open HTML recreation](/assets/slide-recreations/slides/ObTPqBGsEbA/slide-010.html)
- AI slide classifier: `content_slide` confidence `0.98`
- Text source: agent_vision.

Slide text:

> Same project. Same problem. Different approach.
> 18,000 calls/month - 60% simple queries
> $85,000 + 6 months spent on failed attempt
> System: unmeasurable, invisible, misaligned
> Goal: AI agent handles 60%+ user queries
> We didn't pick a model until week 7.


### Hidden Non-Slide Evidence
- [`slide-001.jpg`](/assets/slides/ObTPqBGsEbA/slide-001.jpg) — `speaker_stage` confidence `0.99`; camera shot of presenter on stage with audience and only a partial projected slide visible
- [`slide-011.jpg`](/assets/slides/ObTPqBGsEbA/slide-011.jpg) — `speaker_stage` confidence `0.99`; Camera shot of the speaker on stage with a projected slide in the background, not a readable presentation slide.

Classification audit: `raw/sources/slide-ai-classification/slides/ObTPqBGsEbA/audit.json`

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.