The End of
User Centric
Product Development

Software agent, a computer program that performs various actions continuously and autonomously on behalf of an individual or an organization.

A Generative AI Agent
is defined as an application 
that tries to achieve a goal by
observing the world and acting

upon it using the tools it

has at his disposal​

Ich möchte nächstes Wochenende von Berlin nach Hamburg fahren. Suche mir einen Bus bei Flixbus heraus.

DemoTime

Conversational Agents

• Chat with your documents
• Q&A, Knowledge Interactions
• User queries (like NQL) :-)
• fulfil user transactions
   
• Everything CoPilot, ChatGPT, ...
   
• Chat, Voice, Mail. 

Chatbots are the Ford Model T of AI

• This is not just a faster horse.
• It is a lot more flexible and configurable
• It is faster, can carry heavier weights, more comfortable
   
• The actual revolution was still something else: automation

Autonomous /
Workflow Agents

• Limited User Interaction
• Event driven, Queues of Tasks
• Chains of task execution

Human in the loop

Ask and remember

How to build
an Agent

Perception & Tools

Right now: 

• all documents
• all images
• all sounds
• all apis
• all databases
• all services 

Soon:

• your desktop
• your phone
• robots

Strategy & Planning

• o1-preview: evaluate plans before acting
   
• Who taught o1 the strategies?
   
• Yep, we did.
   
• We can do this for
  agents, too.

Decisions

• Did we reach our goal?
   
• What tool should we try next?
   
• Are we in a loop?
   
• Is the result of the tool helpful?

Memory

• Short Term Memory
  • "What do i need to know
     for this decision"
  • "What do i need to
     plan my next steps"
  • Mostly prompt
     
• Long Term Memory
  • Which strategy actually worked out?
  • which strategy went bad?
  • Which steps were useless?
  • Mostly vectordb

Reflection and Learning

The reason why Strawberry's R are not a problem anymore

• Reflection                                                               

• Feedback loops

• Backtesting

• RLHF

Flow Engineering

In Agentic AI,
user activity
is waste
expensive.

User Story Mapping?
Impact Mapping

How to capture requirements
without any users ...

Todo:
The Cognitive Architecture
 

• Strategy/Planning

• Decisions and Knowledge

• Reflection and Learning

• Self-Optimization / Generalisation        

"As an Agent, i want
to remember working
 strategies to learn."

We haven't solved this one yet.

BUT ...

You never know if you can rely on AI ...

Backtesting, Learning.

"Create a product vision for the company replacing flixbus."

"Decide which is the most important app. Provide UX and user journey concept."

"Create a prototype for this user journey."

React-Prototype after 30 seconds:

This could have
been 100 Click-
Dummies

It's not human,
some tasks need empathy.

It's just a fad.
It won't stay.

“The horse is here to stay, but the automobile is only a novelty – a fad.”


President of the Michigan Savings Bank, advising Horace Rackham (Henry Ford’s lawyer) not to invest in the Ford Motor Company, 1903

• Automatisierung der Produktion:
  10.000 Fahrzeuge pro Tag
   
• Ein Ford Model T wurde in
  93 Minuten vollständig produziert
   
• Die Preise konnten in 5 Jahren von
  950$ auf 360$ gesenkt werden

Industrialisierung

The industrialization of
knowledge work:
elastic knowledge work

• 100 times more expensive than software
• 100 times less expensive than humans.
   
• If you got one agent, you got 1000, too.
• Failing 20 times before it works is still cheap.

Helpful metaphors

• 1000 smart & eager to work interns:
  
  What should they do for you?
  
   
• If you had 100 hours instead of one
  for the next task, what would you do?

Horace Rackham still invested
5.000$ in Ford stock and sold them
later  for 12.5000.000$


 

You could use a car instead of a horse, too.
 

Or even scale it and travel by bus or train.

Automating Knowledge

Work