What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What is AI agent document processing?

AI agent document processing is when an autonomous AI agent reads, interprets, and acts on documents — PDFs, emails, invoices, contracts — without manual intervention. Unlike traditional OCR, the agent doesn't just extract text; it reasons about the content, validates it, and triggers downstream actions like updating a CRM or flagging an anomaly. See our [guide to AI agent use cases](/blog/ai-agent-use-cases/) for more examples.

How do AI agents handle unstructured data from PDFs and emails?

AI agents combine OCR (for scanned documents), layout analysis, and large language models to interpret unstructured content. They identify key fields (amounts, dates, parties), infer context from surrounding text, and normalize the output into structured formats like JSON or database rows — even when document layouts vary wildly between senders.

What is the difference between traditional IDP and agentic document processing?

Traditional intelligent document processing (IDP) follows fixed rules: extract field X from position Y. Agentic document processing uses LLM-based reasoning to handle novel layouts, ambiguous fields, and multi-document workflows. When confidence is low, the agent can ask a clarifying question, escalate to a human, or search a knowledge base — rather than failing silently.

How accurate are AI agents at extracting data from documents?

Modern AI agents achieve 98–99%+ accuracy on clean printed documents. For handwritten or highly variable layouts, accuracy typically ranges from 85–95% depending on the model and post-processing. Most production deployments use a confidence-threshold pattern: high-confidence extractions are auto-approved, low-confidence ones are queued for human review.

Which AI frameworks are best for document processing agents?

LlamaIndex is purpose-built for document-centric agents and is a strong default choice. LangGraph is better when you need complex conditional routing between agents. For teams who want a managed platform rather than rolling their own framework, [cowork.ink](https://app.cowork.ink) provides pre-built agent workflows for document intake, review, and routing with no custom code required.

AI Agents for Document Processing: PDF, Email & Unstructured Data

Learn how AI agent document processing handles PDFs, emails, and unstructured data. STEP-BY-STEP guide with frameworks, architecture, and real examples.

Frequently Asked Questions

What is AI agent document processing?: AI agent document processing is when an autonomous AI agent reads, interprets, and acts on documents — PDFs, emails, invoices, contracts — without manual intervention. Unlike traditional OCR, the agent doesn't just extract text; it reasons about the content, validates it, and triggers downstream actions like updating a CRM or flagging an anomaly. See our [guide to AI agent use cases](/blog/ai-agent-use-cases/) for more examples.
How do AI agents handle unstructured data from PDFs and emails?: AI agents combine OCR (for scanned documents), layout analysis, and large language models to interpret unstructured content. They identify key fields (amounts, dates, parties), infer context from surrounding text, and normalize the output into structured formats like JSON or database rows — even when document layouts vary wildly between senders.
What is the difference between traditional IDP and agentic document processing?: Traditional intelligent document processing (IDP) follows fixed rules: extract field X from position Y. Agentic document processing uses LLM-based reasoning to handle novel layouts, ambiguous fields, and multi-document workflows. When confidence is low, the agent can ask a clarifying question, escalate to a human, or search a knowledge base — rather than failing silently.
How accurate are AI agents at extracting data from documents?: Modern AI agents achieve 98–99%+ accuracy on clean printed documents. For handwritten or highly variable layouts, accuracy typically ranges from 85–95% depending on the model and post-processing. Most production deployments use a confidence-threshold pattern: high-confidence extractions are auto-approved, low-confidence ones are queued for human review.
Which AI frameworks are best for document processing agents?: LlamaIndex is purpose-built for document-centric agents and is a strong default choice. LangGraph is better when you need complex conditional routing between agents. For teams who want a managed platform rather than rolling their own framework, [cowork.ink](https://app.cowork.ink) provides pre-built agent workflows for document intake, review, and routing with no custom code required.