---
title: "Slides: Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic"
category: "slides"
video_id: "TqC1qOfiVcQ"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: Claude Agent SDK [Full Workshop] — Thariq Shihipar, Anthropic

## Source Video
[Claude Agent SDK (Full Workshop) — Thariq Shihipar, Anthropic](https://www.youtube.com/watch?v=TqC1qOfiVcQ)

## 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/TqC1qOfiVcQ/slide-001.jpg]]

OCR text:

> / ANTHROPIC
> Claude Agent SDK

![[assets/slides/TqC1qOfiVcQ/slide-002.jpg]]

OCR text:

> Agenda
> e What is the Claude Agent SDK?
> e Why use it?
> e How do you design an agent?
> e Example: prototyping an agent
> ANTHROPAC CONHDENTAL 2

![[assets/slides/TqC1qOfiVcQ/slide-003.jpg]]

OCR text:

> Ta
> ce
> 7 N
> y : ~
> | ; ‘ v a .
> ve ok . : | .
> ee ae — = E ; .
> L | |
> 7) 7 ;

![[assets/slides/TqC1qOfiVcQ/slide-004.jpg]]

OCR text:

> The evolution of agents
> . “8 @
> y aa 3 a
> Single-LLM Features JER WOIKIOWS am: Uae c
> Summarization, LLMs orchestrated LLMs deciding their Agency %
> classification, by code own trajectories Capability +
> extraction ...
> Flexibility t
> ANTHROP\C COME IGEN TIAL 4

![[assets/slides/TqC1qOfiVcQ/slide-005.jpg]]

OCR text:

> Claude Code
> Claude Code is our an agent that Oe
> lets developers automate software
> development tasks with Claude. : ee
> This is the first digital worker
> doing hours of work.
> a
> ANTHROP\C CONFIDENTIAL 8

![[assets/slides/TqC1qOfiVcQ/slide-006.jpg]]

OCR text:

> Building effective
> agents
> 1/ Models
> High-performance
> reasoning models
> that understand
> complex instructions
> and execute multi
> step workflows.
> Models
> mm Fastest with near-frontier intelligence Bee T SC Tee ley efor ad bis ta eee geen
> ANTHROP\C COSEIDEN TIAL

![[assets/slides/TqC1qOfiVcQ/slide-007.jpg]]

OCR text:

> Building effective
> agents
> 5/ File System bce ee seen tect cn cin en 2
> Give Claude a
> persistent workspace Tools Prompts File System
> to pass files, write
> just-in-time code, “we com Neen Prosens ies
> and learn from Custom Custom mess
> example outputs File System Workllows Ex Output
> Claude Haiku Claude Sonnet Pelt Ce tT]
> Dee em csul iad RT a teva e evel tg beet Ere Bi gn
> ANTHROP\C CONHPIBENTIAL

![[assets/slides/TqC1qOfiVcQ/slide-008.jpg]]

OCR text:

> Building effective Your Application
> Pp :
> agents
> Claude Agent SDK
> 9/ Application Fe = a a RT 9 TN Re TE =
> . SKILLS Enhancements.
> Focus on your unique Subagents
> value - build tne user Tools Prompts File System Web Search
> experience an Research Mode
> domain workflows “we Som Neen Proce is Auto Compacting
> that differentiate your Coton “sen ures Hooks
> product File System Workllows Ex Output Memory
> - [ é Models
> oT CS Seat POUT CRS Tita Claude Opus
> DE mesure Smartest for complex agents /coding be se Eire BC mete ag
> ANTHROP\C CONEIDEN TIAL

![[assets/slides/TqC1qOfiVcQ/slide-009.jpg]]

OCR text:

> Users are already building cutting-edge agents
> with the CC SDK
> e All software tasks
> SRE agents
> Security agents
> - Incident triage agents
> Automated bug fixing
> e Site & Dashboard builders
> e MS Office agents
> e Legal, Finance, Healthcare, HR
> e Custom agent instructions + any integration
> Cloud logs, CI/CD, ete.
> ANTHROP\C CONEIDEN TIAL w

![[assets/slides/TqC1qOfiVcQ/slide-010.jpg]]

OCR text:

> Why use the Claude Agent SDK?
> 
> - We realized people were using Claude Code for non-coding tasks.
> 
> - We build our agents on top of our Agent SDK.
> 
> - Itis built on top of lessons we've learned deploying Claude Code to
> millions of users.
> 
> - We have strong opinions on the best way to build agents.
> 
> - One of our biggest learnings: the Bash tool is the most powerful agent
> tool.

![[assets/slides/TqC1qOfiVcQ/slide-011.jpg]]

OCR text:

> The Agent SDK is the best way to build agents in the
> “Anthropic Way”
> - Unix Primitives: Every agent should use bash [1] &a file system [2] (e.g.
> skills & memory)
> - Agents > Workflows: Agents build their own context + decide what to do
> with tools.
> - Code Generation for non-coding: We use code gen to generate docs,
> query the web, etc.
> - Every agent has a container.
> More on this here.

![[assets/slides/TqC1qOfiVcQ/slide-012.jpg]]

OCR text:

> Bash is all you Need
> Bash is what makes Claude Code so good.
> Bash is a generic version for “code mode” or programmatic tool calling.
> The bash tool allows you to:
> 
> - Store the results of tool calls to files, so you can search them.
> 
> - Store memory in files.
> 
> - Dynamically generate scripts and call them.
> 
> - Compose functionality, use unix primitives like tail, grep, cat, etc.
> antundSe existing powerful software like ffmpeg, libreoffice, etc.

![[assets/slides/TqC1qOfiVcQ/slide-013.jpg]]

OCR text:

> Without Bash With Bash
> User: How much did I spend on ride sharing User: How much did I spend on ride sharing
> this week? this week?
> Tool Cali (Gmail Query Search) query: “uber Tool Call (Bash): ‘gmail_search.ts query
> OR lyft” “uber OR lyft*
> Thinking: I found the following emails that Tool Call (Bash):
> mention Uber and Lyft, I need to find the
> cost of each email. gmait_search.ts ~- a> :
> wot sf
> ret > : " IN
> Claude: The total is... (hallucinated) wee : 1 \ a, /

![[assets/slides/TqC1qOfiVcQ/slide-014.jpg]]

OCR text:

> e
> ae i ee: a 7 an a 7 re re ye an keer ee ae ae ad / eT
> ce or ee ne
> Con Cams CE Co OY 6 FS 6 Tobiys apna 55) \
> nL ae ee Ld OY
> JQ atte te ia
> sort  . >» contacts this week.txt
> Ch ee ce en ke ee to er ne ee eee
> read ematl;
> (10 as nt ee ce \
> rT eee are Oe a CLT Cs LS ar 2k oa 01a: (en Oe (Ree Xel an
> Sleep (il 8 tate ar
> < contacts this week.txt
> a cae ee ea ed
> fem contact detatls.jsonl > final contacts.json

![[assets/slides/TqC1qOfiVcQ/slide-015.jpg]]

OCR text:

> ®
> a) 7 ee 2 Ce eee Pia ee 8
> a aes a |B Bue BO pee 4 / en ans
> fimpeg earnings call.apd .: rn Lee aone Mr: TUrO ROC PRU LG
> whisper audio.way mene @ ROLL a ee 2:1 0)) ee ese a Oh OED 01D
> ee i ea ee ee ee
> ‘gee a PP tee transcript.json > moments. txt
> ce ora oe ee nr re ee: ce
> _ ag ee transcriupt.}son | \
> read start end,
> {fepeq earnings call.mp4 ren ae rn
> Como) 0 EO

![[assets/slides/TqC1qOfiVcQ/slide-016.jpg]]

OCR text:

> ANTHROPIC
> Workflows & Agents
> ANTHROPIC
> CONFIDENTIAL

![[assets/slides/TqC1qOfiVcQ/slide-017.jpg]]

OCR text:

> Workflows & Agents
> 
> We build both agents and workflows on the Claude agent SDK.
> Agents are like Claude Code - You talk to them in natural language
> and they take action.
> 
> Workflows are like our Github action - You define inputs and
> outputs, e.g. take in a PR and give a code review.

![[assets/slides/TqC1qOfiVcQ/slide-018.jpg]]

OCR text:

> Workflows & Agents
> When building workflows, use our structured outputs:
> 
> https: //platform.claude.com/docs /en /agent-sdk/structured-o
> utputs

![[assets/slides/TqC1qOfiVcQ/slide-019.jpg]]

OCR text:

> Verify
> work
> CONFIDENTIAL

![[assets/slides/TqC1qOfiVcQ/slide-020.jpg]]

OCR text:

> Claude Agent SDK Loop
> Gather Take Verify
> context action work
> ANTHROP\C COMPIEENTIAL 29

![[assets/slides/TqC1qOfiVcQ/slide-021.jpg]]

OCR text:

> Tools vs Bash vs Code Generation
> Tools:
> - Pros: Highly structured, highly reliable
> - Cons: High context usage, not composable
> Bash:
> - Pros: Composable, static scripts, low context usage
> - Cons: Longer discovery time, slightly lower call rate
> Code Gen:
> - Pros: Highly composable, dynamic scripts
> - Cons: Needs linting & possibly compilation, careful API design

![[assets/slides/TqC1qOfiVcQ/slide-022.jpg]]

OCR text:

> Tools
> Use tools for: Atomic actions your agent mostly needs to execute
> in sequence
> For example:
> - Writing a file
> - Sending an email

![[assets/slides/TqC1qOfiVcQ/slide-023.jpg]]

OCR text:

> e
> Why Skills?
> agent-skills / skills / public; (0
> Skills let one agent accomplish longer, more complex = eter
> 7 * @ peteriai-ant adc ng better docx redir ng {B19b)
> tasks without needing Subagents. The agent can use
> many Skills and read them only when needed. Name
> se
> Nom fet roe cteate ibe Laced dovument th all the information fue gathered. and ben
> eile an cxevuthe summary in Woed Be docx
> eae meee gd ete MD pptx
> In oe oe
> Some tet me dorate The Wotd document with the execuxlve summary
> a Law. QUAN aeegineRneie
> New Pilteute the Ward document cveuthe sunimasy:

![[assets/slides/TqC1qOfiVcQ/slide-024.jpg]]

OCR text:

> What are Skills?
> An organized collection of files (instructions, nd onw
> executable code, assets) containing everything docs.nd
> Claude needs for a specific task. L apply..template. py
> Skills give an agent:
> 
> General capabilities Claude isn’t good at out of the box (yet)
> 
> e.g. creating PDFs, Excel, & Powerpoint files
> 
> Knowledge of an organization’s workflows and best practices
> 
> e.g. Anthropic’s Brand Styling

## 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.