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- 1. AI AGENTS The Futureof Autonomous Decision-Making
- 2. âDear friends, Ithink AI agent workflows will drive massive AI progress this year â 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.â 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 â 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. âą Performing web searches âą Doing calculations âą Executing code âą Accessing databases âą Interacting with other software systems via APIs âą Accessing other AI Models
- 9. Langchain tool executionexample. NAME Title or Position NAME Title or Position
- 10. Memory âą Short-term memoryfor the duration of a single conversation âą Long-term memory persisting across multiple interactions âą Maintaining context in ongoing conversations âą Learning from previous experiences âą Improving performance over time âą 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 âą Scalability âą Integrations âąAccuracy and Reliability âą Memory Limitations and Contextual Management âą Security âą End-to-End Agent Operations âą 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 âą Industry-Specific Metrics: Tailor goals for sectors âą KPIs: Focus on measurable outcomes âą Workflow Analysis: Pinpoint bottlenecks, repetitive tasks, decision points Identify Business Needs âą Tech Inventory: List all your existing tools and systems. âą Choosing AI Frameworks based on needs âą What to Look For: ïŒ Security ïŒ Scalability ïŒ Compatibility Evaluate Your Tech Environment âą Financial Analysis âą 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