panaversity / learn-agentic-ai
- воскресенье, 11 мая 2025 г. в 00:00:04
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
This repo is part of the Panaversity Certified Agentic & Robotic AI Engineer program. It covers AI-201, AI-202 and AI-301 courses.
We have Two Hunches, the future of Pakistan depends on it, let's make sure that we are not wrong:
It is very important for Pakistan that we bet on the right horses for the upcoming age of Agentic AI. We will be training millions of Agentic AI Developers all over Pakistan and online around the world and building startups, we cant afford to be wrong.
Hunch #1: Dapr We feel Dapr, Dapr Actors, Dapr Workflows, and Dapr Agents will be the core technology in building the next generation multi ai agentic systems, is my hunch correct?
Hunch #2: OpenAI Agents SDK We also have a hunch that OpenAI Agents SDK will be the go to framework for beginners to start learning Agentic AI?
Let us see what the best AI has to say about our hunches:
https://chatgpt.com/share/6811b893-82cc-8001-9037-e45bcd91cc64
https://g.co/gemini/share/1f31c876520b
https://grok.com/share/bGVnYWN5_4343d342-c7df-4b06-9174-487a64f59d53
“How do we design AI Agents that can handle 10 million concurrent AI Agents without failing?”
Note: The challenge is intensified as we must guide our students to solve this issue with minimal financial resources available during training.
Kubernetes with Dapr can theoretically handle 10 million concurrent agents in an agentic AI system without failing, but achieving this requires extensive optimization, significant infrastructure, and careful engineering. While direct evidence at this scale is limited, logical extrapolation from existing benchmarks, Kubernetes’ scalability, and Dapr’s actor model supports feasibility, especially with rigorous tuning and resource allocation.
Condensed Argument with Proof and Logic:
Kubernetes Scalability:
Dapr’s Efficiency for Agentic AI:
Handling AI Workloads:
Networking and Storage:
Resilience and Monitoring:
Feasibility with Constraints:
Conclusion: Kubernetes with Dapr can handle 10 million concurrent users in an agentic AI system, supported by their proven scalability, real-world case studies, and logical extrapolation. For students with minimal budgets, small-scale simulations, open-source tools, and cloud credits make the problem tractable, though production-scale deployment requires hyperscale resources and expertise.
Agentic AI Top Trend of 2025
Let's understand and learn about "Dapr Agentic Cloud Ascent (DACA)", our winning design pattern for developing and deploying planet scale multi-agent systems.
The Dapr Agentic Cloud Ascent (DACA) guide introduces a strategic design pattern for building and deploying sophisticated, scalable, and resilient agentic AI systems. Addressing the complexities of modern AI development, DACA integrates the OpenAI Agents SDK for core agent logic with the Model Context Protocol (MCP) for standardized tool use and the Agent2Agent (A2A) protocol for seamless inter-agent communication, all underpinned by the distributed capabilities of Dapr. Grounded in AI-first and cloud-first principles, DACA promotes the use of stateless, containerized applications deployed on platforms like Azure Container Apps (Serverless Containers) or Kubernetes, enabling efficient scaling from local development to planetary-scale production, potentially leveraging free-tier cloud services and self-hosted LLMs for cost optimization. The pattern emphasizes modularity, context-awareness, and standardized communication, envisioning an Agentia World where diverse AI agents collaborate intelligently. Ultimately, DACA offers a robust, flexible, and cost-effective framework for developers and architects aiming to create complex, cloud-native agentic AI applications that are built for scalability and resilience from the ground up.
Comprehensive Guide to Dapr Agentic Cloud Ascent (DACA) Design Pattern
Table 1: Comparison of Abstraction Levels in AI Agent Frameworks
Framework | Abstraction Level | Key Characteristics | Learning Curve | Control Level | Simplicity |
---|---|---|---|---|---|
OpenAI Agents SDK | Minimal | Python-first, core primitives (Agents, Handoffs, Guardrails), direct control | Low | High | High |
CrewAI | Moderate | Role-based agents, crews, tasks, focus on collaboration | Low-Medium | Medium | Medium |
AutoGen | High | Conversational agents, flexible conversation patterns, human-in-the-loop support | Medium | Medium | Medium |
Google ADK | Moderate | Multi-agent hierarchies, Google Cloud integration (Gemini, Vertex AI), rich tool ecosystem, bidirectional streaming | Medium | Medium-High | Medium |
LangGraph | Low-Moderate | Graph-based workflows, nodes, edges, explicit state management | Very High | Very High | Low |
Dapr Agents | Moderate | Stateful virtual actors, event-driven multi-agent workflows, Kubernetes integration, 50+ data connectors, built-in resiliency | Medium | Medium-High | Medium |
The table clearly identifies why OpenAI Agents SDK should be the main framework for agentic development for most use cases:
If your priority is ease of use, flexibility, and quick iteration in agentic development, OpenAI Agents SDK is the clear winner based on the table. However, if your project requires enterprise-scale features (e.g., Dapr Agents) or maximum control for complex workflows (e.g., LangGraph), you might consider those alternatives despite their added complexity.
Note: These videos are for additional learning, and do not cover all the material taught in the onsite classes.
Prerequisite: Successful completion of AI-101: Modern AI Python Programming - Your Launchpad into Intelligent Systems
Prerequisite: Successful completion of AI-201
Prerequisite: Successful completion of AI-201 & AI-202