Chunking Strategies for Agents: Optimizing for Logic and Reasoning

Even with Gemini's massive 2-million token context window, you cannot simply "dump" a chaotic pile of text into an agent and expect perfect performance. To retrieve the right information via RAG, or to process information efficiently in-context, your data must be Chunked (broken down into manageable, logical units).

In this lesson, we will explore why Naive Chunking (splitting by character count) fails for agents and how to implement Semantic and Hierarchical Chunking. We will learn how to preserve "Logical Continuity" so that an agent doesn't lose the meaning of a sentence just because it was split in half by a character limit.

1. The Goal: Preserving Narrative and Logic

The ultimate goal of chunking for an agent is to ensure that a single "Chunk" contains one complete thought or fact.

The Failure of Fixed-Size Chunking:

If you split a document every 1,000 characters:

Result A: Contains the first half of a critical warning.
Result B: Contains the second half and the remediation steps.
The Problem: During search, the agent might retrieve Result A and miss the instructions in Result B.

2. Strategy 1: Semantic Chunking

Instead of counting characters, we split where the Meaning changes.

How it works:

Sentence Splitting: Break the text into individual sentences.
Cluster Analysis: Group sentences together as long as they are "Semantically Similar."
The Break: When a new sentence introduces a new topic (calculated via embeddings), start a new chunk.

Best For: Natural language documents, blog posts, and research papers where the structure is fluid.

3. Strategy 2: Structural/Markdown Chunking

If your data is formatted (Markdown, HTML, PDF), use the Structure as the split point.

H1/H2 Headers: Every section becomes its own chunk.
Tables: Ensure a table is Never split across chunks. A table's meaning relies on the relationship between all its rows.
Lists: Keep a full bulleted list in a single chunk whenever possible.

graph TD
    A[Raw Markdown Doc] --> B{Structure Splitter}
    B --> C[Chunk 1: Header + Intro]
    B --> D[Chunk 2: Sub-header + Table]
    B --> E[Chunk 3: Sub-header + List]
    
    style B fill:#F4B400,color:#fff

4. Strategy 3: Overlapping Chunks (Sliding Window)

To prevent "Edge Loss" (where a fact is split between two chunks), we use Overlaps.

Example:

Chunk 1: Tokens 0 to 500.
Chunk 2: Tokens 400 to 900.
Context Overlap: 100 tokens.

Why this helps agents: It provides "Contextual Anchors." When the agent reads Chunk 2, it still sees the tail end of the previous thought, allowing it to maintain the logical "Breadcrumbs" of the document.

5. Strategy 4: Hierarchical/Recursive Chunking

For massive documents, we use a "Parent-Child" relationship.

Parent Chunks: Large summaries of entire chapters (e.g., 2,000 tokens).
Child Chunks: The granular paragraphs within that chapter (e.g., 200 tokens).

Agent Workflow:

The Agent first searches the Parents to find the right chapter.
It then zoom-in to search the Children to find the specific fact.
Benefit: This prevents the agent from getting "Lost in the Weeds" of millions of tiny, disconnected chunks.

6. Implementation: Semantic Splitting with Python

Let's look at a conceptual implementation of a structure-aware splitter.

def structure_aware_chunker(text: str, max_size: int = 1000):
    # 1. First, split by Markdown headers
    sections = text.split("\n## ")
    
    final_chunks = []
    for section in sections:
        # 2. Check if section is too big
        if len(section) < max_size:
            final_chunks.append(section)
        else:
            # 3. If too big, split by Paragraph instead of Characters
            paragraphs = section.split("\n\n")
            current_buffer = ""
            for p in paragraphs:
                if len(current_buffer) + len(p) < max_size:
                    current_buffer += p + "\n\n"
                else:
                    final_chunks.append(current_buffer)
                    current_buffer = p + "\n\n"
            final_chunks.append(current_buffer)
            
    return final_chunks

7. Token-Aware Chunking

Gemini charges and measures limits per Token, not per Character.

A 1,000-character chunk of English is ~250 tokens.
A 1,000-character chunk of Python code (lots of spaces and brackets) might be ~400 tokens.

Pro Rule: Use a tokenizer (like tiktoken or the Gemini SDK's built-in token counter) to measure your chunk sizes to avoid "Overflow" errors or truncated models.

8. Summary and Exercises

Chunking is the Packaging of knowledge.

Naive chunking breaks logic; Semantic chunking preserves it.
Overlaps provide the "Contextual Glue" between pieces of data.
Hierarchical chunking allows for efficient "Search then Inspect" workflows.
Structure-awareness (Markdown/Tables) is vital for technical documents.

Exercises

Manual Splitting: Take a 2-page article. Try to split it into 5 chunks of 500 characters each. Did any sentences get cut in the middle? How would you change the split points to preserve the meaning?
Overlap Logic: If you have a 1,000-word document and want chunks of 200 words with a 10% overlap, how many chunks will you have? Where do the overlaps occur?
Table Design: You have a 50-row CSV table. If you chunk it every 10 rows, how can you ensure the "Column Headers" are included in every chunk so the agent knows what the numbers mean?

In the next lesson, we will look at Knowledge Graphs and Structured Data, exploring how to connect our agents to relational knowledge.