b2afd6e91e
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: ian.alton@airbyte.io <ian.alton@airbyte.io> Co-authored-by: octavia-bot[bot] <108746235+octavia-bot[bot]@users.noreply.github.com>
83 lines
5.1 KiB
Markdown
83 lines
5.1 KiB
Markdown
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sidebar_position: 1
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# AI agents
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Airbyte provides a set of tools to help you automate, understand, move, and work with your data in coordination with AI agents. Some of these tools are standalone open source solutions, and others are paid solutions built on top of Airbyte Cloud.
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- **Agent connectors**: Use Airbyte's AI-optimized, type-safe connectors to power your own AI agents, or use them with Airbyte's Connector MCP server. [View the GitHub repo](https://github.com/airbytehq/airbyte-agent-connectors) or [try the tutorial](quickstarts). You can set up your own agent and explore your data in 15 minutes or less.
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- **Airbyte Embedded**: Add hundreds of integrations into your product instantly. Your end-users can authenticate into their data sources and begin syncing data to your product. You no longer need to spend engineering cycles on data movement. Focus on what makes your product great, rather than maintaining data integrations.
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<!-- import Taxonomy from "@site/static/_taxonomy_of_data_movement.md";
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Airbyte's Agent engine is a set of tools to help you automate, understand, move, and work with your data in coordination with AI agents. Some of these tools are standalone open source solutions, and others are pay solutions built on top of Airbyte Cloud.
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The platform is a solution for all types of audiences, from AI engineers who are deploying agents in large enterprises down to individual founders who need real-time context and action in their platforms.
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- **Agent connectors**: AI-optimized, type-safe connectors, usable with Airbyte's Connector MCP server or your own Python agents. [View the GitHub repo](https://github.com/airbytehq/airbyte-agent-connectors).
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- **Airbyte Embedded**: Add hundreds of integrations into your product instantly. Your end-users can authenticate into their data sources and begin syncing data to your product. You no longer need to spend engineering cycles on data movement. Focus on what makes your product great, rather than maintaining data integrations.
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:::info New and growing
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The Agent engine is new and growing. Airbyte is actively seeking feedback, design partners, and community involvement. Expect this library of tools to grow and change rapidly.
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:::
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## Why Airbyte?
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- **Agents hallucinate or fail**: Stale and incomplete data erodes agent effectiveness. AI agents need real-time context from multiple business systems to be fully effective.
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- **Custom API integrations are brittle and expensive**: Airbyte's library of agentic connectors are open source. Benefit from the economy of scale as third-party APIs evolve, add new endpoints, and deprecate old ones.
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- **Agents need to write, not just read**: Airbyte provides the operational backbone needed to make agentic AI actually work in production. Fetch, write, and reason with live business data through a standardized, open protocol.
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:::note
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Writes aren't supported in Airbyte yet.
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:::
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### The use case for agentic data
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The Agent engine enables agents to fetch, search, and reason with live business data.
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Even if you're not a data expert, you still need to interpret vendor data. That means cleaning, normalizing, stitching fields together, and transforming your and your customers' data into entities your agents can actually use.
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The Agent engine is an ideal solution when you:
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- Don't want storage
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- Care a lot about freshness and latency
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- Are working with a small amount of data
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- Need to trigger side effects, like sending an email or closing a ticket
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The agentic data platform _isn't_ ideal when you:
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- Need all your data in one place
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- Need to join across datasets
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- Need more pipelines that can be slower
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- Want storage
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- Want to update content, but not trigger side effects
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- Rely on APIs that aren't good
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If agentic data isn't what you're looking for and you need complex data aggregation for data analysis, [data replication](/platform) is likely the right solution.
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### Taxonomy of data movement
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<Taxonomy />
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## Airbyte Embedded
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[Airbyte Embedded](embedded) equips product and software teams with the tools needed to move customer data and deliver context to AI applications.
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Airbyte Embedded creates isolated workspaces for each of your customers, allowing them to configure their own data sources while keeping their data separate and secure. The Embedded Widget provides a pre-built UI component that handles the entire user onboarding flow from authentication to source configuration.
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Once Airbyte enables your organization on Airbyte Embedded, you can begin onboarding customers via the Embedded Widget. You can download the Embedded demo app [from GitHub](https://github.com/airbytehq/embedded-demoapp).
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## Agent connectors
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Airbyte's agent connectors are Python packages that equip AI agents to call third-party APIs through strongly typed, well-documented tools. Each connector is ready to use directly in your Python app, in an agent framework, or exposed through an MCP. [Learn more >](connectors) or [view the GitHub repo](https://github.com/airbytehq/airbyte-agent-connectors).
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## Connector MCP
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Use [agent connectors](connectors) to interact with your data using natural language.
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-->
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