docs(day2): Added ai agent talk notes

content/day2/02_agent.md

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+---
+title: "Works on my LLM: Building your own ai code assistant that isn't completely useless"
+weight: 2
+tags:
+ - ai
+ - vibecoding
+---
+
+<!-- {{% button href="https://youtu.be/rkteV6Mzjfs" style="warning" icon="video" %}}Watch talk on YouTube{{% /button %}} -->
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+
+Build or improvde your own ai coding agent (well mostly improve).
+
+## Baseline
+
+- AI enables us to produce usless code 10x faster
+- Problem: Traditional vibe coding is just a short instruction "build me a web app"
+- Solution: Context Engineering to support the next step with the right information
+- Agent has Multiple Parts: LLM, Context Window, External Context (files), MCP
+
+## Set up the bootloader
+
+- Rule-File: Coding style, conventions, best practives -> "always do this"
+- Workflows: Helpers like scripts, etc
+  - e.g.: Gather Requirements -> Clarify -> Create specification
+- Can be wirtten in normal english and maybe annotated using agent-specific tags
+
+## Load domain specific knowledge
+
+- Useful: Add questions regarding approach/architecture to your workflows
+- This is where mcp servers can come in
+- Challenge: Picking the right and right amount of information to provide to the agent
+
+## Micro context strategy
+
+- Problem: Monolythic context that can be filled up and even trunkated
+- Idea: Split into multiple smaller contexts that will be combined before sending to the ai
+- Implementation: Save the context into different files and chunk the results into files
+- Pro: Can be used for statless interaction
+
+## State Managmeent
+
+- Memory Bank: Always keep updated documents with summaries for the implementation task
+- The rabit hole problem: Trying workaround after workaround resulting in a full context with useless non-working workaround
+- Checkpoint Restoration: Create checkpoints and recreate contexts from them instead of trying to force the ai back on track