The document discusses open-source models in artificial intelligence, highlighting their accessibility, cost-saving benefits, and customization options for users. It also covers AI agents, their capabilities, and the importance of agentic design patterns that enhance problem-solving through tool use and collaboration. Additionally, it addresses future trends in AI agents, emphasizing the need for ethical considerations and bias mitigation in their development.

1. Patterns
Patterns
By Omar El Monayar
By Omar El Monayar
AI Agentic
AI Agentic
Design
Design

2. OPEN SOURCE MODELS
AI open-source models refer to artificial
intelligence algorithms, frameworks, and
tools that are freely available for public
access, use, and modification. These models
are typically developed collaboratively by a
community of researchers, developers, and
enthusiasts, and are distributed under open-
source licenses, allowing anyone to study,
modify, and distribute the code.

5. 01 02 03
ACCESSIBILITY
Open-source
models
democratize access
to cutting-edge AI
technologies,
making them
available to
developers,
researchers, and
organizations
regardless of their
resources or
expertise.
BENEFITS
COST SAVING
Open-source
projects foster
collaboration and
knowledge sharing
among developers,
researchers, and
enthusiasts, leading
to faster innovation
and collective
problem-solving.
CUSTOMIZATION
Users can
customize and
adapt open-source
models to suit their
specific needs,
whether it's fine-
tuning pre-trained
models, adding
new features, or
integrating them
into existing
workflows.
03
COST-
EFFIECIENCY
By leveraging
open-source
models,
organizations can
reduce
development costs,
accelerate time-to-
market, and avoid
vendor lock-in
associated with
proprietary AI
solutions.

6. AI AGENTS
An AI agent is a software program that can perceive
its environment, reason about gathered
information, and take autonomous actions to
achieve a specific goal.
AI Agents
These agents can be simple or incredibly complex,
ranging from automated chatbots to sophisticated
self-driving systems.

7. Learning &
Adaption
NLP
KEY CAPABILITIES OF MODERN
AGENTS
AI agents leverage machine learning to
improve their performance over time.
They can analyze data, identify patterns, and
adjust their behavior accordingly.
Agents are increasingly sophisticated in
understanding and responding to human
language.
This enables seamless conversations and
enhances user experiences.

8. AGENTIC DESIGN PATTERNS
Agentic design patterns are specific
techniques that empower AI agents to
execute more complex tasks.
They provide a framework for building
agents that can break down problems,
access various tools, and collaborate.

9. AGENTIC DESIGN PATTERNS

10. TOOL USE
01
CODE EXECUTION FOR CALCULATIONS
02
WEB SEARCH FOR INFORMATION GATHERING
The ability for an AI agent to call upon external tools and
functions to extend its capabilities.
IMAGE GENERATION OR MANIPULATION
03

11. Improved
Accuracy
Expanded
Capabilities
Problem
Solving
Using the right tool can
make complex problems
manageable.
TOOL USE BENEFITS
Accessing specialized tools
can enhance the accuracy
of an agent's output.
Agents can perform tasks
they might not be able to
do with direct language
modeling alone.

12. Reflection
AGENTIC DESIGN
PATTERNS
Definition: The ability of an AI
agent to analyze its own output
and suggest improvements.
Process: The agent can be
prompted to identify errors, stylistic
shortcomings, or ways to be more
concise.
Benefits: Enhances response
quality and builds trust by
demonstrating the agent's
awareness of its limitations.
Benefits Multi-Agent
Collaborations
Definition: An agent's ability to
break down a complex task into a
sequence of smaller, achievable
steps.
Benefits: Enables agents to tackle
problems that might be too
difficult to solve in a single shot.
Example: An agent might plan out
the steps required to organize a
business trip, including booking
flights, hotels, and scheduling
meetings.
Definition: Multiple AI agents
working together to achieve a
common goal.
Benefits:
Specialized agents bring unique
skills
Distribution of workload for
tackling challenging problems
Enhanced understanding of
intricate tasks through multi-
agent input.

15. AI AGENT FUTURE
AI agents will increasingly automate tedious or
repetitive tasks, freeing up humans to focus on
more creative work.
Enhanced Automation
Agents can tailor their responses and actions
to the individual preferences of users.
Personalized Experiences
The development of AI-as-a-Service will broaden
the availability of AI technology, enabling more
people to create custom AI solutions.
Increased Accessibility

16. AI AGENT FUTURE

17. 01 02 03
BIAS
It's crucial that AI
agents are trained
on diverse data to
avoid bias in
decision-making.
CHALLENGES AND
CONSIDERATIONS
RESPONSIBILITY
As agents evolve, it's
important to
establish clear
guidelines regarding
ownership and
responsibility for their
actions.
ETHICS
There's a need for a
strong ethical
framework to guide
AI agent
development,
addressing issues like
privacy and
transparency.

18. Thank You!