An extraction agent is an autonomous LLM that explores documents and decides how to extract data, rather than following a fixed extraction strategy. It uses tools to read, search, and navigate documents before producing output.

How It Differs from Fixed Strategies [#how-it-differs-from-fixed-strategies]

| Approach   | How It Works                                               |
| ---------- | ---------------------------------------------------------- |
| Simple     | Process entire document in one LLM call                    |
| Parallel   | Split into chunks, process simultaneously                  |
| Sequential | Process chunks in order, building up results               |
| **Agent**  | Explore document, decide what to read, extract iteratively |

Why Use an Agent? [#why-use-an-agent]

Fixed strategies work well when you know the document structure upfront. But when documents vary:

* **Unknown structure** — Agent discovers layout dynamically
* **Variable length** — Agent reads only what's needed
* **Complex navigation** — Agent can search, skip, revisit sections
* **Adaptive extraction** — Agent adjusts strategy per document

How Agents Work [#how-agents-work]

An extraction agent is given:

1. **A virtual filesystem** — Access to document content
2. **Tools** — Read, grep, find, explore
3. **Output schema** — What data to extract
4. **Control tools** — Set/update output, finish, fail

The agent:

1. Explores the document using tools
2. Identifies relevant sections
3. Extracts data iteratively
4. Validates and corrects
5. Signals completion

Example: Contract Analysis [#example-contract-analysis]

```
Agent: "I need to find the parties involved."
→ uses grep("party") 
→ finds section 2.1

Agent: "Let me read section 2.1"
→ uses read("/artifacts/contract.pdf#section-2.1")
→ extracts party names

Agent: "Now I need the effective date"
→ uses grep("effective date")
→ extracts date

Agent: "I have all required fields"
→ uses finish()
```

Trade-offs [#trade-offs]

| Advantage                     | Disadvantage                |
| ----------------------------- | --------------------------- |
| Handles unknown structures    | Variable token cost         |
| Adapts to document variations | Requires tool-calling model |
| Can skip irrelevant sections  | More complex to debug       |
| Better for complex documents  | Overkill for simple cases   |

When to Use an Agent [#when-to-use-an-agent]

Use an agent strategy when:

* Document structure varies significantly
* You don't know what sections contain relevant data
* Documents are long but only parts are relevant
* You need to cross-reference within the document

Use simpler strategies when:

* Documents have consistent structure
* Entire document is relevant
* You know exactly what to extract

See Also [#see-also]

* [What is Structured Data Extraction?](/docs/what-is-structured-data-extraction)
* [Choosing an Extraction Strategy](/blog/agent-vs-simple-vs-parallel)
* [Struktur Documentation](/docs)