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Inside AI Agents: From Reason in g to Real-World Action

Explore the core components of AI agents, including planning, tool calling, memory, and evaluation.

SS
Soham Sharma
AI Engineer, Botmartz · May 8, 2024 · 9 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
9 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Inside AI Agents: From Reasoning to Real-World Action AI Agents represent the next frontier of LLM applications, transitioning from passive content generators to active decision-makers that can plan, reason, call tools, and execute workflows autonomously. ## Core Components of an AI Agent 1. **Reasoning & Planning**: Decomposing complex goals into manageable steps (e.g., ReAct, Chain-of-Thought). 2. **Tool Integration**: Connecting the agent to external APIs, databases, and web browsers. 3. **Memory Systems**: Short-term memory (conversation state) and long-term memory (vector storage). 4. **Agentic Loops**: Self-correction and reflection mechanisms.

Closing Takeaways

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Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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SS
Soham Sharma
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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