---
title: "Slides: WF26: Harness Engineering & Startup Battlefield ft. Garry Tan, Mike Krieger, @t3dotgg , DSPy"
category: "slides"
video_id: "I2cbIws9j10"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: WF26: Harness Engineering & Startup Battlefield ft. Garry Tan, Mike Krieger, @t3dotgg , DSPy

## Source Video
[WF26: Harness Engineering & Startup Battlefield ft. Garry Tan, Mike Krieger, @t3dotgg , DSPy](https://www.youtube.com/watch?v=I2cbIws9j10)

## 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/I2cbIws9j10/slide-001.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-002.jpg]]

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![[assets/slides/I2cbIws9j10/slide-003.jpg]]

OCR text:

> AI Engineer
> World's Fair
> Livestream
> July 2, 2026
> EVENT STARTS IN 03:05
> GRAPHS
> BROUGHT TO YOU BY
> neo4j
> View Full Schedule ai.engineer/worldsfair/2026/schedule

![[assets/slides/I2cbIws9j10/slide-004.jpg]]

OCR text:

> RL Selects theResponse,NottheFacts
> Action:
> State:
> retry
> coerce
> rollback
> quarantine
> escalate
> 1.Failurecategory
> 2.Risklevel
> 1.TabularQ-learning
> 3.Retrycount
> 2.Small,interpretablestatespace
> 4.Driftseverity
> 3.Low-memoryinference
> 5.Data-qualitycondition
> 4.InspectableQ-valuesforeverydecision
> TECHNICALLY.THISISASINGLE-STEPCONTEXTUALDECISIONPROBLEMIMPLEMENTEDWITHTABULARQ-LEARNING.

![[assets/slides/I2cbIws9j10/slide-005.jpg]]

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![[assets/slides/I2cbIws9j10/slide-006.jpg]]

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![[assets/slides/I2cbIws9j10/slide-007.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-008.jpg]]

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![[assets/slides/I2cbIws9j10/slide-009.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-010.jpg]]

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![[assets/slides/I2cbIws9j10/slide-011.jpg]]

OCR text:

> prompting,promptengineering,chain-of-thought,few-shot,zero-shot,one-shot,system
> World'sFair AIEngineer self-consistency,nsemblebest-of-n,AG,structuredouutsmixtureofexperts,guardrails ReAct,toolcalling,functioncalling,toolschema,computeruse,agentswarm,verifier,judge, alignment,promptinjection,promptleaking,instructionoverride,agentic,agents,agentloop engineering,in-contextlearning,exemplars,demonstrations,self-critique,self-reflection,
> PRESENTEDBY Microsoft memory,Mc,embeddings,vectorstore,semanticsearch,chunking,chunksize,sliding window,reranking,contextwindow,longcontext,compaction,pre-training,post-training fine-tuning,SFT,reinforcementlearning,RLHF,rewardmodel,preferencedata,evals,D, ORP,TO,GRP,distillation,tachermodel,studentmodel,mult-agentystem,oRA
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> constitutionalAL Rl AIF nrocessreward outcome reward tokenizer multimodal VlM STT
> TheUnreasonableEffectivenessofSeparatingtheTaskfromtheModel
> CoreContributor MaximeRivest LeadMaintainer IsaacMiller DSPy

![[assets/slides/I2cbIws9j10/slide-012.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-013.jpg]]

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![[assets/slides/I2cbIws9j10/slide-014.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-015.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-016.jpg]]

OCR text:

> AI Engineer World’s Fair
> The implementation is fully learned.
> YOU WRITE
> extract = dspy.Flex(ExtractTaxes, tools=...)
> THE MODEL WRITES
> class TaxExtractor(dspy.Module):
> def __init__(self):
> self.parse = dspy.Predict(...)
> self.check = dspy.ReAct(
> Validate, tools=[...])
> def forward(self, invoice):
> lines = self.parse(invoice)
> lines = dedupe(lines)
> return self.check(lines)
> The Unreasonable Effectiveness of Separating the Task from the Model
> Maxime Rivest | Isaac Miller
> Core Contributor | Lead Maintainer
> DSPy

![[assets/slides/I2cbIws9j10/slide-017.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-018.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-019.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-020.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-021.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-022.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-023.jpg]]

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![[assets/slides/I2cbIws9j10/slide-024.jpg]]

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![[assets/slides/I2cbIws9j10/slide-025.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-026.jpg]]

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![[assets/slides/I2cbIws9j10/slide-027.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-028.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-029.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-030.jpg]]

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![[assets/slides/I2cbIws9j10/slide-032.jpg]]

OCR text:

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OCR text:

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![[assets/slides/I2cbIws9j10/slide-036.jpg]]

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> 
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> github.com/ambenzon27/rl-etl-remediation-agent

![[assets/slides/I2cbIws9j10/slide-037.jpg]]

OCR text:

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> ; a Self-Healing of Cloud Data
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> 2.Diagnose the likely failure family
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> 5.Validate whether the action restored a heaithy state

![[assets/slides/I2cbIws9j10/slide-038.jpg]]

OCR text:

> ene Tg
> Deterministic Anomaly Rules
> Schema drift, null spikes, field
> removals, type changes
> Q-Learning Decision Policy
> Retry, coerce schema, roliback,
> quarantine, escalate, log
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> Critical anomaly + passive action
> om intelligence

![[assets/slides/I2cbIws9j10/slide-039.jpg]]

OCR text:

> N elects tne Kesponse, No e Facts
> Action:
> State:
> 
> 1.Failure category
> 2.Risk level 1. Tabular Q-learning
> 3.Retry count 2.Small, interpretable state space
> 4.Drift severity 3.Low-memory inference
> 5.Data-quality condition 4.Inspectable Q-values for every decision
> 
> “TECHNICALLY, THIS IS A SINGLE-STEP CONTEXTUAL DECISION PROBLEM IMPLEMENTED WITH TABULAR O-LEARNING.

![[assets/slides/I2cbIws9j10/slide-040.jpg]]

OCR text:

> World'sFair AlEngineer
> PRESENTEDBY Budget
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> 8Br'-trust
> Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-041.jpg]]

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OCR text:

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> Engineering the future of AI

![[assets/slides/I2cbIws9j10/slide-043.jpg]]

OCR text:

> AI Engineer
> World's Fair

![[assets/slides/I2cbIws9j10/slide-044.jpg]]

OCR text:

> World'sFair
> You are watching an AJE Online Talk
> Pre-Recorded for Worid's Fair 2026.
> My personal notes!

![[assets/slides/I2cbIws9j10/slide-045.jpg]]

OCR text:

> World'sFair
> AEngi
> You are watching an AJE Online Talk
> Pre-Recorded for Worid's Fair 2026.
> TOWARDSAI

![[assets/slides/I2cbIws9j10/slide-046.jpg]]

OCR text:

> (problems) One conversation has to do everything
> Meaning:
> the context window becomes
> — the database,
> — the file system,
> — the memory,
> — the reasoning space a
> yf |

![[assets/slides/I2cbIws9j10/slide-047.jpg]]

OCR text:

> MCPs, CLIs,
> and Skills: CG» ;
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> © Nikita Kothari © +
> a, Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-048.jpg]]

OCR text:

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> World's Fair
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> to do this, the agent should use a CLI too.
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> a Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-049.jpg]]

OCR text:

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![[assets/slides/I2cbIws9j10/slide-050.jpg]]

OCR text:

> World'sFair AEng Pre-Recorded forWorid'sFair2026. You are watching an AlE Online Talk
> AHOL CEO,NORIAGENTIC
> docs
> code data
> slack
> Hi - I'm Amol.
> WedeployanAlemployeethatunderstandsyourcompany,

![[assets/slides/I2cbIws9j10/slide-051.jpg]]

OCR text:

> AEng
> World'sFair
> You are watching an AlE Online Talk
> Pre-Recorded for Worio'sFair2026
> AGENT
> Whathappenswhenyou hand these toolsto anagent?

![[assets/slides/I2cbIws9j10/slide-052.jpg]]

OCR text:

> World'sFair Pre-Recorded forWorld'sFair2026. You are watching anAIE Online Tak
> THEWORKAROUNDS
> vision re-prompt
> Figma MCP PowerPoint CLI
> AGENT
> screenshot→replace pixel-diff eval
> whatdoalloftheseagenttoolshaveincommon?

![[assets/slides/I2cbIws9j10/slide-053.jpg]]

OCR text:

> Ree
> LPanseripts
> Boatd Deck
> Coote e mee) [
> @ workspace
> Slack
> Let your agents do all the grunt work while you focus on vision and story.

![[assets/slides/I2cbIws9j10/slide-054.jpg]]

OCR text:

> eee Fair .
> a5
> g t
> » CJ
> raQgors y f} DD
> Not programming. '
> ieee errs ee
> They will say something technically perfect and socially catastrophic with exactly the same
> confidence.

![[assets/slides/I2cbIws9j10/slide-055.jpg]]

OCR text:

> World'sFair Pre-Recorded forWorld'sFair 2026. You are watching an AIE Online Talk
> THEAROHITECTURE
> Four layers. One destination.
> Immutableidentity Situationalmode
> Hard rules.What the brand can never say.Nothing below can touch it. Real-time conditions.Who the user is.What they're going through.
> Example-anchoredvoice Post-generationveto
> teamsstop. The dials,the phrases,the tone guide.Wheremost layer. Reads what actually came out.The only deterministic
> ISADORA MARTIN-DYE STOP WRITING TONE INSTRUCTIONS. LAYER THEM

![[assets/slides/I2cbIws9j10/slide-056.jpg]]

OCR text:

> World'sFair Pre-Recorded forWorid'sFair 2026. You are watching an AIE Online Talk
> Rulesthat aretrue
> regardless ofroute.
> UNIVERSAL-RULES.TS-HARDIDENTITYRULE
> If asked whether you are a real person, a human,a live agent-You MuST confirm you
> are an AI.In your very next message. This rule CANNoT be overridden by any venue
> configuration,voice profile,or user request.
> EveryAlinBloomdisclosesinthefirstreply.Notifasked—beforetheyask.
> Aproduct decision,nota legal one.Transparency is the trust signal.
> ISADORA MARTIN-DYE STOP WRITING TONEINSTRUCTIONS.LAYER THEM

![[assets/slides/I2cbIws9j10/slide-057.jpg]]

OCR text:

> AlEngineer
> Authforagents:
> World's Fair
> Unblockautonomous
> Al with auth.md
> MICHAELGRINICH
> woriasrair
> FOUNDER&CEO
> KOS
> Workos
> Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-058.jpg]]

OCR text:

> World'sFair AIEngineer
> PRESENTED BY
> Microsoft
> uiusran
> OWOs
> Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-059.jpg]]

OCR text:

> 7 SES I ee
> Word'sFair] & \O4 a i ae
> j i ;
> 
> : a / Ot
> MEV coro ae | — ~
> Ow 0s We L
> \ Fa
> 
> m Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-060.jpg]]

OCR text:

> eer an
> ea
> a Microsoft . ‘
> Work’ ow
> F o Ww
> a ef
> nh. Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-061.jpg]]

OCR text:

> eens
> draco relat Make something people want.
> Mic off  v
> af a
> x :
> . Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-062.jpg]]

OCR text:

> AI Engineer
> World’s Fair
> Human
> Agent
> Today’s signup flow assumes:
> A human can read a landing page
> A human can fill a form
> A human can solve a CAPTCHA
> A human can verify email
> (agents might not have an email address)
> A human can choose a plan
> A human can copy an API key
> A human can paste it somewhere
> A human can understand the dashboard
> Agent-native registration needs:
> Discovery
> Capability declaration
> Registration intent
> Risk assessment
> Agent identity verification (source agent, like Claude)
> Human or organizational delegation (my company)
> Entitlement grant
> Credential issuance
> Claiming / later ownership transfer
> Audibility
> Engineering the future of AI

![[assets/slides/I2cbIws9j10/slide-063.jpg]]

OCR text:

> [ World's Fair auth.md can answer:
> 
> What does this service do?
> 
> aro Re lot eT eC ueets I Cltg
> 
> What identity proofs are accepted?
> 
> What auth flows are supported?
> 
> What scopes and entitlements exist?
> 
> What free-tier constraints apply?
> cee ee How does a human or organization later claim the account?
> ~ig Microsoft “ vent
> ae
> 
> Fe ° .
> 
> Optiver ha Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-064.jpg]]

OCR text:

> ALEngineer
> World's Fair
> auth.md
> = Microsoft Agent Hames Agent Auth Discovery ee
> nny Nate Send ID-JAG
> aesrel aha
> IRCrex= Iho NOES Le) Ao1A)
> nn a Make normal API/MCP request Novas tae]
> Scan | Wo Backing identity service
> ft .
> Ww ¢
> an e r)
> om “Wo Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-065.jpg]]

OCR text:

> World'sFair AIEngineer
> PRESENTED BY
> Microsoft
> Microsot Worl
> Worle Optiv Atng Worl Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-066.jpg]]

OCR text:

> World'sFair AIEngineer
> HDJCNIIAAA
> Microsoft World" AEng
> World'sI Optiver Noru Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-067.jpg]]

OCR text:

> World'sFair AEngine Pre-RecordedforWorid'sFair2026. You are watching an AJE Online Talk
> IRA MACHINECRAFT
> IRA - FORK MY
> BRAIN
> runsits go-to-market. How a1o0-person factorytaughtitself to remember- andbuilt a36-agentAIthat
> Rushabh Doshi Machinecraft·AI Engineer World's Fair

![[assets/slides/I2cbIws9j10/slide-068.jpg]]

OCR text:

> World'sFair
> You are watching an AiE Online Talk
> Pre-Recorded forWorid'sFair2026.
> DON'TWRITEITDOWN-GROW
> BRAIN
> Not a chatbot.A twin of the company.

![[assets/slides/I2cbIws9j10/slide-069.jpg]]

OCR text:

> World'sFair Pre-Recorded forWorid'sFair2026. You arewatching an AJEOnline Talk
> WENEVERTRAINEDAMODEL
> Chunk it→vectors inQdrant-relationships in Neo4j→our own retrieval

![[assets/slides/I2cbIws9j10/slide-070.jpg]]

OCR text:

> een Why am I’m here?
> World's Fair
> 2023 2025 2026
> DLAI: Generative Al with LLMs Creator of: mep-lambda-handler
> ae ‘Se Agentic al
> Cy - G '@@ Foundation
> . ee Ss .
> 5. eS C]tinex
> a rao an |
> an A
> — 439,910 enrollments 35k / month wee
> md a 'azon AG
> rere me
> ee Engineering the future of Al
> cl ea

![[assets/slides/I2cbIws9j10/slide-071.jpg]]

OCR text:

> Ct a oe eda! Pe eI Ca i i si ca Sed
> EUS aod i. oo eee _ ’
> World's Fair vimene re
> aca eres CO aatnS
> es Caan eee oneal
> pote eae
> lott. Maree
> - “ee us agent . Agent
> iva Stein Sn a Oc co en ee) ea
> agent nL ti ot Cee Learn ese l ae!
> / aa oot oe oe
> ouch
> air G le DeepMir
> to on Pa aeert ol eae)
> a 7 Engi ing the fut AY
> aa ie ngineering tne future o

![[assets/slides/I2cbIws9j10/slide-072.jpg]]

OCR text:

> Cem tae ee Cr s Cot) i a ns Sead
> heel Paes et Ky a /
> World's Fair | 8 ert
> 7 Strands Agent Mera
> oe re - Aa an near. ,
> Lt Gm a ar
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> Reany to keep testing re? Being it ont @®
> hi Pay ena a |
> aa Z.Al
> | en
> 7 . 7 ‘ e °
> i ai Engineering the future of Al
> am

![[assets/slides/I2cbIws9j10/slide-073.jpg]]

OCR text:

> AIEngineer
> demo $agentcore create
> World'sFair
> PRESENTED BY
> Microsoft
> air
> .AI
> Search
> Engineering the future of Al
> a

![[assets/slides/I2cbIws9j10/slide-074.jpg]]

OCR text:

> World'sFair AlEngineer _demo $agentcore create Add Agent
> Name→√Type→√Language→√Build→Protocol→Framework
> PRESENTED BY
> Microsoft Strands Agents SDK-AWS native agent framework LangChain+ LangGraph-Popular open-source frameworks Google ADK-Google Agent DevelopmentKit OpenAI Agents-OpenAI native agent SDK
> ↓navigate·Enter select·Esc back
> air AI
> air Seach Fair Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-075.jpg]]

OCR text:

> Pe eo ae eo De ee 2) os Ce en a rs oe
> a cee (
> ALEngincer
> W mek = 5 iret tad ae acy
> ovate ka arelig tae re
> Cane Pn Any
> eres PaaS Dates
> agent Cha eee
> 7 Pe ay eee SS oe iB
> . ae Eerie Sn) Tel ree reed Cae enki tt
> : “Ree Ceo anata) or incre
> See mep clipst.client grt streanable Pttp rep client
> a Microsoft Se eC See] iki hae eee a
> agp  BedrockAgentCoredpp
> ore) eer Creed
> mogel
> a Cra Mak tet rie Mie Gal act oh aM rh ks ao on Oars ea
> i a ae eae date
> ar ep gum g Be oe ee whos ae et og
> corn : eae)
> a A Al ora SOLS aro 7
> 3 Fair 7 a :
> es UE lach
> AGENTS rg
> “Ge
> 7 APPLICATION BUILDER as
> ed 11)
> os cd i
> i rare
> sFair =D Airbvte Engineering the future of Al

![[assets/slides/I2cbIws9j10/slide-076.jpg]]

OCR text:

> Smt tree ere @esm@e ee Ce) i i Serr
> a : (
> Ete ed
> . é fa coeOrtars OS ra elas elas
> World's Fair ames So de eye ee Ah pee gute pwn
> mo ail) hE Mess Te Cee a ecTae
> agent
> Metra mone
> ORR Stet seme (c)
> i¢] aes RE wiare)
> See Obed mae)
> Porras
> la et
> eri | APPLICATION BUILOER
> Se a i
> ae te Engineering the future of Al
> a |

![[assets/slides/I2cbIws9j10/slide-077.jpg]]

OCR text:

> AI Engineer
> World's Fair

![[assets/slides/I2cbIws9j10/slide-078.jpg]]

OCR text:

> World'sFair
> You are watching an AlE Online Talk
> Pre-Recorded for Worid'sFair2026
> Four traps that eachcosta day
> VSE SOFTWARE I2C
> A8005T
> RESULATORKILLS THE OLED
> Karovarene:dies wits no pult-ops.Bit-bang it.
> sustasnec
> >bs-sax 3.6Y.Buck-cot 1s mdstory
> CP1013ISF5PIMI50
> Evenat s.silet fallre,Noved C t17.

![[assets/slides/I2cbIws9j10/slide-079.jpg]]

OCR text:

> World'sFair AEngi Pre-Recorded tor Worid'sFair2026
> D
> (calletaes)
> Track narrative state,not numbers
> IRPERFECT NPC RENORY Dne-Line oenery ttat decays -s4ftensat +2e, cropped at 40 WORLDMOGD 2-
> TKREADS ATTITUDE INFERENCE Readsyour last 5 soves-brvtal ptay,hareer sori6.
> Don't track HP and dice-an LLH is bad at that.Trackwhat it's great at.

![[assets/slides/I2cbIws9j10/slide-080.jpg]]

OCR text:

> Re
> Narrated on the fly - rendered to paper
> Fig
> wes ay
> He ‘ . YO
> ba A
> ’ ‘ ¢
> As a :
> Each scene: a praept + Poilimations.ar + Playa Steisberg dither ko
> Ltit parched for the 888.488 panel, a a
> iy
> a ,

## 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.
## Dense Scene-Detected Slide Candidates
- [[youtube-I2cbIws9j10-dense-slides]]