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Goals

1. Map the territory of Agentic AI
2. Build a real Agentic AI application
3. Build a multi-agentic AI application
4. Discover any other major patterns and document for potential implementation </aside>

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This Week

1. Tier 2 Reading
2. Practice one Case Study
3. Review Well Aware Code, Agentic System & Evals
4. Module 2 of Agentic AI

Stretch Goal:

Add an Agentic System to my Exobrain

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Outcomes

• Create a Excalidraw diagram for Agents of all components and common patterns
• Practice 5-10 case studies on Agents so that I know which components to use and considerations to make when developing an Agentic System
  • Customer support agent
  • Research synthesis agent
  • Eval/monitoring agent
  • FAQ bot with tools
  • Support ticket router
  • Writing agent with self-review
  • Multi-agent code review pipeline
  • Content moderation agent with escalation _
• Create an Agent for my Exobrain_
• Create a multi-agentic workflow_
• Review use of agents in WellAware_
• Implement an evals system for WellAware to learn evals
• Create a cheat sheet for Agents to match with Excalidraw diagram. Consider publishing this. </aside>

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Links

Roadmaps (Agents)

https://github.com/krishnaik06/Roadmap-To-Learn-Agentic-AI

Github project

• [ ] https://www.kdnuggets.com/agentic-ai-a-self-study-roadmap

Safety / Observability

Deploying agentic AI with safety and security

https://opentelemetry.io/blog/2025/ai-agent-observability/

https://docs.langchain.com/oss/javascript/langchain/tools

AI Agents Roadmap

Design Patterns

https://arxiv.org/abs/2210.03629

https://docs.cloud.google.com/architecture/choose-design-pattern-agentic-ai-system

LangGraph

https://www.coursera.org/learn/agentic-ai-with-langchain-and-langgraph#outcomes https://www.coursera.org/learn/agentic-ai-with-langgraph-crewai-autogen-and-beeai?specialization=building-ai-agents-and-agentic-workflows

https://langchain-ai.github.io/langgraph/tutorials/lats/lats/#graph-state

https://docs.langchain.com/oss/javascript/langchain/tools

Github Projects

LangGraph

https://github.com/wassim249/fastapi-langgraph-agent-production-ready-template

https://github.com/ForLoopCodes/contextplus

https://github.com/jamwithai/production-agentic-rag-course

Agentic AI Interview Resources

• [ ] https://blog.promptlayer.com/the-agentic-system-design-interview-how-to-evaluate-ai-engineers/
• [ ] https://www.kdnuggets.com/10-essential-agentic-ai-interview-questions-for-ai-engineers
• [ ] https://atul4u.medium.com/the-complete-agentic-ai-system-design-interview-guide-2026-f95d0cfeb7cf
• [ ] https://igotanoffer.com/en/advice/generative-ai-system-design-interview

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Moltbook

Moltbook (OpenClaw) Agent Analysis

https://github.com/knostic/openclaw-telemetry

Captures tool calls, LLM usage, agent lifecycle, and message events. Outputs to JSONL file and optionally to syslog for SIEM integration

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AI Agents - Reading List

Tier 3 — Deeper study for differentiation:

Chain-of-Thought Prompting (https://arxiv.org/abs/2201.11903), Tree of Thoughts (https://arxiv.org/abs/2305.10601), Reflexion (https://arxiv.org/abs/2303.11366), Lil'Log and Stanford's Generative Agents (https://arxiv.org/abs/2304.03442). The 86-page survey "The Rise and Potential of LLM-Based Agents" (https://arxiv.org/abs/2308.11432) is comprehensive reference material. For production patterns, the arXiv paper on "Production-Grade Agentic AI Workflows" (https://arxiv.org/abs/2512.08769) bridges theory and deployment. arXiv