Design, scale, and govern autonomous AI agents for real-world enterprise systems
β±οΈ Length: 4.6 total hours
π₯ 64 students
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- Course Overview
- Delve into the revolutionary discipline of AgenticOps, a field that merges traditional DevOps principles with the unique requirements of autonomous AI agent lifecycles.
- Explore the transition from Prompt-Response interactions to Goal-Oriented autonomous loops that can plan, execute, and refine their own strategies.
- Analyze the fundamental architecture of AI-Native Systems, focusing on how to build software where the LLM is not just a feature, but the central reasoning engine.
- Investigate the nuances of Cognitive Architectures, including the implementation of “System 1” (fast, intuitive) and “System 2” (slow, analytical) thinking within digital agents.
- Deconstruct the complexity of Multi-Agent Orchestration, learning how specialized agents can be organized into collaborative “crews” to tackle multifaceted enterprise problems.
- Study the Lifecycle Management of agents, covering everything from initial persona design and tool-set provisioning to deployment, monitoring, and eventual retirement.
- Examine the critical role of State Management in long-running autonomous processes, ensuring agents maintain context and progress across disconnected execution windows.
- Focus on the concept of Systemic Autonomy, where agents are empowered to interact directly with databases, cloud infrastructure, and third-party software suites without manual oversight.
- Address the shift in Software Development Life Cycles (SDLC) necessitated by non-deterministic AI outputs, moving toward a more iterative and evaluation-centric approach.
- Requirements / Prerequisites
- Advanced proficiency in Python 3.10+, with a specific emphasis on asynchronous programming using asyncio for managing concurrent agent tasks.
- A solid understanding of Large Language Model (LLM) fundamentals, including tokenization, context windows, and the limitations of current transformer architectures.
- Hands-on experience with RESTful API Integration and the ability to work with JSON Schema for structured data exchange between agents and tools.
- Familiarity with Docker and container orchestration concepts to provide isolated, reproducible environments for agent execution “sandboxes.”
- Conceptual knowledge of Vector Databases (such as Pinecone, Milvus, or Qdrant) and the mechanics of Retrieval-Augmented Generation (RAG).
- Basic awareness of Git workflows and CI/CD pipelines, as these form the bedrock of the “Ops” portion of AgenticOps.
- An analytical mindset capable of Logic Modeling to map out complex business processes into discrete, agent-executable steps.
- Skills Covered / Tools Used
- Mastery of high-level Agent Frameworks including LangGraph for cyclic graphs, CrewAI for role-based collaboration, and Microsoft AutoGen for conversational agents.
- Implementation of Tool-Calling and Function-Calling protocols, allowing agents to securely interface with SQL databases, web search engines, and local file systems.
- Advanced utilization of Observability and Tracing tools like LangSmith, Arize Phoenix, or Honeyhive to visualize the internal reasoning chains of agents.
- Development of Governance and Safety Layers using NeMo Guardrails or Guardrails AI to enforce ethical boundaries and prevent prompt injection attacks.
- Configuration of Agentic Memory Systems, distinguishing between short-term episodic memory and long-term semantic memory for persistent agent personalities.
- Expertise in Evaluation Frameworks such as RAGAS or DeepEval to mathematically quantify the performance, faithfulness, and relevancy of agent responses.
- Deployment of Human-in-the-Loop (HITL) checkpoints to maintain oversight in high-stakes autonomous workflows without sacrificing operational speed.
- Strategies for Token Budgeting and Cost Optimization to prevent autonomous loops from consuming excessive cloud credits through “infinite loop” scenarios.
- Benefits / Outcomes
- Acquire the ability to architect Production-Ready Autonomous Systems that move beyond simple chat interfaces into functional business actors.
- Gain a competitive edge in the job market by mastering Agentic Design Patterns, the next frontier of artificial intelligence implementation in the enterprise.
- Develop a Governance Roadmap that allows your organization to scale AI agents safely while meeting strict compliance and security requirements.
- Achieve significant Operational Efficiency by automating high-cognition tasks that previously required expensive human-in-the-loop cycles.
- Learn to build Self-Healing Workflows where agents can detect their own errors, browse documentation for solutions, and re-attempt tasks autonomously.
- Transform your technical portfolio with a capstone project featuring a Multi-Agent Enterprise Ecosystem capable of solving real-world logistics or data analysis problems.
- Master the art of Systemic Evaluation, ensuring that every update to your agentic system is backed by rigorous, automated benchmarking data.
- PROS
- Provides a forward-looking curriculum that anticipates the shift from static AI to dynamic, autonomous “agentic” labor.
- Focuses heavily on industrial-strength reliability, moving away from “toy” demos to systems that can survive real-world edge cases.
- Offers a framework-agnostic perspective, teaching you the underlying principles of AgenticOps that apply regardless of which library becomes the industry standard.
- Includes deep-dive modules on governance, a critical but often ignored aspect of AI that is essential for any enterprise-level deployment.
- CONS
- The extreme volatility of the AI landscape means that certain specific tool implementations may require updates or self-guided research shortly after the course concludes.
Learning Tracks: English,Development,Data Science
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