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- 1. AI AGENTS The Futureof Autonomous Decision-Making
- 2. “Dear friends, Ithink 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 AIAGENTS MULTI AGENT FRAMEWORK REALITY OF TODAY INDUSTRY APPLICATIONS WHAT THE FUTURE HOLDS AGENDA
- 4. SINGLE STRUCTURE AI Scalability Constraints Datalimitations Lack of Flexibility Resource-Heavy Training COMPOUND AI SYSTEMS Modularity and Flexibility Scalability and Efficiency Cost and Resource Efficiency
- 5. GPT3.5 and GPT4performance using zero shot and agent workflows
- 6. TOOLS Act AI MODEL Reason /Make decisions MEMORY Access memory AI Agent
- 7. Reasoning – whatis 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 LLMAgents: 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 executionexample. NAME Title or Position NAME Title or Position
- 10. Memory • Short-term memoryfor 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 o Specialization oParallelization o Customization o Dynamic Decision-Making o Scalability o Interpretability
- 13. AI Agent Frameworks AIagent 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 Digitallabor 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
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- 18. Autonomo us Agents of tomorrow o Multi-ModalMulti-Agents o Sensory / Spatial / Emotional / Behavioral Input Data o Tool design-awareness layer o Overcoming Context and Memory constraints o Self-discovering objectives o Dynamic learning o Inter-Agent Synchronization and Communication o Self-upgrading mechanisms o Ethical Frameworks
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- 21. Strategic roadmap forAgent 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 FocusStrengths 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