Through this course learnt many new things:-
Day 1: Establishing the Autonomous Paradigm
The initial module focuses on establishing a robust theoretical and architectural understanding. Participants will master the foundational concepts of AI agents, clearly distinguishing their defining characteristics and the functional necessity of agentic architectures in contrast to traditional LLM applications. This session culminates in the ability to design the conceptual blueprints for intelligent, autonomous systems.
Day 2: Advanced Tool Integration and External Interoperability
This module transitions to practical action enablement. Learners will gain the competency to equip agents with external functionalities, allowing them to leverage diverse APIs and tools to execute real-world tasks. The session provides deep expertise in utilizing the Model Context Protocol (MCP) for efficient tool discovery, integration, and seamless system-to-system interoperability.
Day 3: Engineering Contextual Persistence and Memory Management
The third module focuses on building stateful, context-aware agents. Graduates will be proficient in advanced context engineering, including the strategic implementation of both short-term memory (session management) and long-term memory (knowledge retention) to enable agents to handle complex, sequential, and multi-turn workflows with high fidelity and robustness.
Day 4: Quality Assurance, Observability, and Performance Optimization
This critical segment ensures the development of professional-grade, reliable agents. Participants will acquire essential skills in agent quality assurance, learning to implement comprehensive observability, logging, and tracing systems for critical visibility. Furthermore, they will master key metrics and sophisticated evaluation strategies necessary to iteratively optimize and validate agent performance.
Day 5: Production Deployment, Scaling, and Multi-Agent Collaboration
The final module provides the expertise to scale systems from proof-of-concept to enterprise deployment. Learners will be equipped with best practices for deployment and scaling of AI agents in production environments. The capstone skill acquired is the ability to architect and govern complex, distributed multi-agent systems (MAS) using the Agent2Agent (A2A) Protocol for coordinated, collaborative task execution.
Thank you for providing the detailed five-day curriculum for the Agentic AI Systems program.
The structured outline effectively clarifies the technical scope of the training, particularly the progression from foundational architectures (Day 1) to production scaling and multi-agent collaboration via the Agent2Agent (A2A) Protocol (Day 5).
This information is sufficient for our evaluation of the program's strategic fit.
Sincerely,
Atri
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