Slides: The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents
Source Video
The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents
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
OCR text:
A hatem mellem tS
Domain-Specific Agents
or
Nene re | 4
OCR text:
oo vento (@) StandardAgents |
2 FFEEILE arrou:s ® jsonreader
ZODOLIEI * FormkKit > AutoAnimate
TEMPO MSU COT
X. eipschrosder — 7
OCR text:
ry
~~
OCR text:
Ce UM elastic texas eam termes
a
OCR text:
What is an agent?
X @ipschroeder
OCR text:
% Claude ®
@ oh 7
X. @ipschroeder A
OCR text:
But everyone 1s
ob NKebberee: asl eras
OCR text:
Why:
OCR text:
Agents are hard
¢ Agentic loop orchestration
¢ Provider abstraction
* Durable execution
OCR text:
It’s amess out there
¢ Building robust agents is hard.
¢ There is no defined way to do it.
¢ Telemetry/observability is hard.
OCR text:
Client Resources Prompts Toots Discovery Sampling Roots Elicitation Instructions ‘
Sire x x & ? x x ? ?
AgentAl x x uw ? x x 2 ?
AgenticFlow u iv) ta ts x x ? ?
AIQL TUUI iv] iv) iv] ta iv] x 4 ?
Amazon Q CLI x ud iV] 2 x x 2 ?
Amazon Q IDE x x G x x x 2 ?
Amp iv} ta Ga x iV} x ? ?
Apify MCP x x i % x x 2 ?
Tester
Augment Code x x iv] x x x ? ?
Avatar Shell iv] x 4 x x x ? ?
BeeAl x x Wi x x x
Framework
BoltAl x x iv] ? x x & .
Call Chirp x iV} iV] x x x . y
Chatbox x x G x x x
ChatFrame x x iv] x x x
ChatGPT 4 4 td Ww +4 a ? >?
OCR text:
i aie
_— rz a , fin .
* vA y _ Psi - @ : . : x Ze
- ob a ef Ne as
ne ny ES . 4 oh Mee i : By ay
q a at ae ¥ <p ‘ “a 7 + ;
* = ee : | | i ; g ¢ re "A " f i a fl
N J SS =" Fae ea , Y & oo
aaa i -—) oe S| aL om es
i] a , a SS ee ay (mri iN ae ead ys iN
d I . +8 DOP: ne | ’ AA aga on % ff i
' 7 Sh ™ . # re , sad % a or
mea roy a) OO" ee 3 a ae “et a
y tj 1 or ro ty ae a2 54 rho re
1 2 el AC ; ee
i ee. | ls el
iene en. Mi ae ° a u ; Amr
f Fy oe tw et fr ae a i < - ry od ] F Py z
po i, ee tees a ae . x , F
rr er ne a a a =
ar eae tee ice ae, a i" WV a .
ne eo eee ar G a
ee ey = 7 nics
a ea
* nite —_ ee! le . t : i
iin aid
oe sg. a - ee ae ww ~~
By Re afar Ce Ste en ak
OCR text:
System Prompt
Poses aa ;
/
OCR text:
Messages
MCP
Skill
Context
Tools
SystemPrompt
Model
OCR text:
Messages
MCP
KAYAK
NAVAN
Figma
Playwright
Gmail
Google Sheets
Skill
Tools
SystemPrompt
Model
OCR text:
Pe ,
Own NAVAN
FFigMa & Playwright
mm Gmail B Google Sheets ‘
aus 7 enance
Ay aT |
OR feat Code Fix btinter |
nt
DO tithe
ae
OCR text:
System Prompt
i FFigma : om
*oncoscencusecscnsceconccee® ss x
=.
OCR text:
/ | ae \
System Prompt System Prompt System Prompt SNES Gg
i FFigma : : Gmail : ; gnavan He = Keree [=
wacccrevccscecerscesenacen® Pasecccccvscuccessvcouaces® A eeceecccsssevacsascccasas® ouscceecces ‘es *
cD Cx Ci r
OCR text:
ae =
eee eee _
a 0 \
or a
aR Mee rt ag eRe ta.
SN eet Ont ae ee a are |
? eet + if ; oA
a \: ae ss int } FANG ; aha \* 4 e. & NS "oe
“, <a ‘ as | \i A ‘9 “E SN ZS . ‘
ah\. KAS \N ar oN eee aa
MS OS ec a)
a YY ‘ ea 3 me ;
wr : a rr se " As . fend
. a 5 Oe 4 Le e
" cee We y N | * -
en # \* ‘
ee ‘ 7 ae
OCR text:
Domain Specific Agents...
¢ Far more efficient with tokens
OCR text:
Domain Specific Agents...
¢ Far more efficient with tokens
= ky
OCR text:
Domain Specific Agents...
¢ Far more efficient with tokens
* Make small language models practical
DeepSeek v4 flash is
137x cheaper than Fable &
per task A 7
e" if
OCR text:
Domain Specific Agents...
¢ Far more efficient with tokens
¢ Make small language models practical
* Can enforce strict limits on capabilities
OCR text:
Domain Specific Agents...
¢ Far more efficient with tokens
* Make small language models practical
* Can enforce strict limits on capabilities
¢ Have excellent scaling characteristics .
my
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.