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 the difference between a data pipeline and an AI data pipeline?

A traditional data pipeline moves data from source to destination — typically batch ETL (extract, transform, load) with fixed transformation rules. An AI data pipeline does all of that, but also handles continuous feature extraction, vector embeddings, model-serving formats, and feedback loops so that AI agents and ML models always receive up-to-date, structured context. See our [guide to agentic RAG](/blog/agentic-rag/) for how retrieval fits into this picture.

What are the stages of an AI data pipeline?

The five core stages are: (1) Ingestion — pulling data from APIs, databases, files, and streams; (2) Transformation — cleaning, normalizing, and enriching raw data; (3) Governance — validating schema, enforcing data contracts, and managing lineage; (4) Serving — making data available as embeddings, feature vectors, or context payloads; and (5) Feedback — monitoring agent outputs to detect drift and trigger reprocessing.

What tools are used to build AI data pipelines?

Common open-source tools include Apache Kafka or Redpanda for streaming ingestion, dbt for SQL-based transformation, Great Expectations or Soda for validation, and Qdrant, Weaviate, or pgvector for the vector serving layer. Managed options like Fivetran, Airbyte, or Estuary Flow handle ingestion with minimal infra.

What is an agentic data pipeline?

An agentic data pipeline treats the AI agent — not a dashboard or analyst — as the primary consumer. It prioritizes low-latency serving, context-window-sized payloads, and real-time refresh cycles instead of nightly batch loads. The pipeline reacts to agent queries and continuously updates embeddings as upstream data changes.

How do I build an AI data pipeline for LLM agents?

Start by mapping your agent's context needs — what data it must recall and in what format. Then build backward: design the serving layer first (embeddings + retrieval), then the transformation rules that produce clean records, then the ingestion connectors to source systems. Use schema validation at each stage boundary and add observability from day one.

AI Data Pipeline: Feed Your Agents Clean, Real-Time Data

Learn how to build an AI data pipeline in 5 stages — ingestion, transformation, governance, serving, and feedback. PRACTICAL guide with tools and pitfalls. Start now.

Frequently Asked Questions

What is the difference between a data pipeline and an AI data pipeline?: A traditional data pipeline moves data from source to destination — typically batch ETL (extract, transform, load) with fixed transformation rules. An AI data pipeline does all of that, but also handles continuous feature extraction, vector embeddings, model-serving formats, and feedback loops so that AI agents and ML models always receive up-to-date, structured context. See our [guide to agentic RAG](/blog/agentic-rag/) for how retrieval fits into this picture.
What are the stages of an AI data pipeline?: The five core stages are: (1) Ingestion — pulling data from APIs, databases, files, and streams; (2) Transformation — cleaning, normalizing, and enriching raw data; (3) Governance — validating schema, enforcing data contracts, and managing lineage; (4) Serving — making data available as embeddings, feature vectors, or context payloads; and (5) Feedback — monitoring agent outputs to detect drift and trigger reprocessing.
What tools are used to build AI data pipelines?: Common open-source tools include Apache Kafka or Redpanda for streaming ingestion, dbt for SQL-based transformation, Great Expectations or Soda for validation, and Qdrant, Weaviate, or pgvector for the vector serving layer. Managed options like Fivetran, Airbyte, or Estuary Flow handle ingestion with minimal infra.
What is an agentic data pipeline?: An agentic data pipeline treats the AI agent — not a dashboard or analyst — as the primary consumer. It prioritizes low-latency serving, context-window-sized payloads, and real-time refresh cycles instead of nightly batch loads. The pipeline reacts to agent queries and continuously updates embeddings as upstream data changes.
How do I build an AI data pipeline for LLM agents?: Start by mapping your agent's context needs — what data it must recall and in what format. Then build backward: design the serving layer first (embeddings + retrieval), then the transformation rules that produce clean records, then the ingestion connectors to source systems. Use schema validation at each stage boundary and add observability from day one.