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1. AI AGENTS
The Future of Autonomous Decision-Making

2. “Dear friends, I think AI agent workflows
will drive massive AI progress this year —
perhaps even more than the next
generation of foundation models. This is
an important trend, and I urge everyone
who works in AI to pay attention to it.”
Andrew NG

3. MONOLITHIC VS
COMPUND SYSTEMS
AI AGENTS
MULTI AGENT
FRAMEWORK
REALITY OF TODAY INDUSTRY APPLICATIONS WHAT THE FUTURE HOLDS
AGENDA

4. SINGLE STRUCTURE
AI
Scalability Constraints
Data limitations
Lack of Flexibility
Resource-Heavy Training
COMPOUND AI
SYSTEMS
Modularity and Flexibility
Scalability and Efficiency
Cost and Resource Efficiency

5. GPT3.5 and GPT4 performance using zero shot and agent workflows

6. TOOLS
Act
AI MODEL
Reason / Make decisions
MEMORY
Access memory
AI Agent

7. Reasoning – what is the
brain?
1.Analyze the given objective
2.Identify the necessary steps to achieve the goal
3.Prioritize these steps in a logical sequence
4.Adapt the plan based on new information

8. Act via Tools
LLM Agents: the objective of the LLM to identify the
function to execute and identify the parameters to
execute the function.
•Performing web searches
•Doing calculations
•Executing code
•Accessing databases
•Interacting with other software systems via APIs
•Accessing other AI Models

9. Langchain tool execution example.
NAME
Title or Position
NAME
Title or Position

10. Memory
•Short-term memory for the duration of a single conversation
•Long-term memory persisting across multiple interactions
•Maintaining context in ongoing conversations
•Learning from previous experiences
•Improving performance over time
•Providing personalized responses based on user history

12. Multi agent
frameworks
oSpecialization
oParallelization
oCustomization
oDynamic Decision-Making
oScalability
oInterpretability

13. AI Agent Frameworks
AI agent frameworks are software platforms designed to simplify creating, deploying, and
managing AI agents. These frameworks provide developers with pre-built components,
abstractions, and tools that streamline the development of complex AI systems. 

14. Framework Strengths
Langchain Versatility, external integrations
LangGraph Complex workflows, agent coordination
CrewAI Collaborative problem-solving, team dynamics
Microsoft Semantic Kernel Security, compliance, existing codebase integration
Microsoft Autogen Robustness, modularity, conversation management
Transformers Agents 2.0 Modular, self-correcting RAG, tool integration
Swarm Efficient handoffs, highly testable
Llama Index
Enhanced document indexing, efficient query handling,
integration with external data sources

15. Code generation
Customer Service
Digital labor and
Research
Virtual assistants Industry use cases
Finance
Healthcare
Marketing
Manufacturing
Retail
Legal

16. Challenges
•Scalability
•Integrations
•Accuracy and Reliability
•Memory Limitations and Contextual Management
•Security
•End-to-End Agent Operations
•Error rate accumulation

17. What the future
holds

18. Autonomo
us Agents
of
tomorrow
oMulti-Modal Multi-Agents
oSensory / Spatial / Emotional / Behavioral Input Data
oTool design-awareness layer
oOvercoming Context and Memory constraints
oSelf-discovering objectives
oDynamic learning
oInter-Agent Synchronization and Communication
oSelf-upgrading mechanisms
oEthical Frameworks

19. THANK YOU!

20. Additional
slides

21. Strategic roadmap for Agent
integration
•Industry-Specific Metrics: Tailor
goals for sectors
•KPIs: Focus on measurable
outcomes
•Workflow Analysis: Pinpoint
bottlenecks, repetitive tasks,
decision points
Identify
Business Needs
•Tech Inventory: List all your
existing tools and systems.
•Choosing AI Frameworks based
on needs
•What to Look For:
Security
Scalability
Compatibility
Evaluate Your
Tech
Environment
•Financial Analysis
•Pilot Program.
Cost & Testing
for Value

22. Framework Key Focus Strengths Best For
Langchain LLM-powered applications Versatility, external integrations General-purpose AI development
LangGraph Stateful multi-actor systems Complex workflows, agent coordination Interactive, adaptive AI applications
CrewAI Role-playing AI agents
Collaborative problem-solving, team
dynamics
Simulating complex organizational tasks
Microsoft Semantic Kernel Enterprise AI integration
Security, compliance, existing codebase
integration
Enhancing enterprise applications with AI
Microsoft Autogen Multi-agent conversational systems
Robustness, modularity, conversation
management
Advanced conversational AI and task
automation
Transformers Agents 2.0 Agent-based AI workflows
Modular, self-correcting RAG, tool
integration
High-performance agent systems
Swarm Lightweight multi-agent orchestration Efficient handoffs, highly testable Orchestrating multi-agent systems
Llama Index Data-centric LLM applications
Enhanced document indexing, efficient
query handling, integration with external
data sources
Complex data retrieval, context-rich
applications