@kapa.ai explain Airbyte Agents to me.

Airbyte Agents (Agent Engine) Airbyte's Agent Engine is a data layer designed specifically for AI agents. It solves a core problem: AI agents are limited by stale training data, lack real-time knowledge, and can't easily access or act on live business data. Building the infrastructure to fix this (storage, indexing, pipelines, permissions) is expensive and complex. [About Agent Engine] How It Works The Agent Engine has three main components: [About Agent Engine] 1. Open-source, type-safe agent connectors — Python packages that let agents retrieve information, perform actions, interact with external systems, and trigger side-effects (e.g., sending emails, updating databases, starting workflows). 2. Credential management — Centralized storage and management of end-user credentials, handling OAuth flows, API keys, and token refresh automatically. 3. Entity caching (Context Store) — Out-of-the-box caching that powers low-latency search operations across connected systems, even those without native search APIs. Agent Connectors vs. Replication Connectors Agent connectors are not the same as Airbyte's traditional data replication connectors: [Agent connectors] What Agents Can Do Through the Agent Engine, your AI agent can: [MCP server] • Fetch fresh data in real time from any connected source • Search across systems, including unstructured data like contracts and PDFs • Write — create issues, assign owners, send messages inside SaaS tools • Discover available entities and relationships across systems Available Connectors The Agent Engine launched with 10 production-ready connectors: Gong, Zendesk Support, GitHub, HubSpot, Salesforce, Jira, Asana, Stripe, Greenhouse, and Linear — with more being added weekly. The replication connector catalog has 600+. [FAQs] How to Use It You have two options: • Managed platform: Sign up at app.airbyte.ai to manage connectors, credentials, and data replication in the cloud. [Agent Engine platform] • Open-source SDK: Install the

airbyte-agent-sdk

as a standalone Python package and manage storage/credentials yourself. It works with frameworks like pydantic-ai, LangChain, OpenAI Agents, and FastMCP. [GitHub README] You can also expose connectors via an MCP server (Model Context Protocol), which Airbyte hosts and manages — nothing to install. [MCP server] Who It's For • AI companies building multi-tenant SaaS agentic solutions • Engineering teams building internal agents • Anyone who wants to empower an AI agent quickly without building data infrastructure from scratch [About Agent Engine] 👍 0 button 👎 0 button ℹ️ Tip: Mention @kapa.ai in the thread for follow-ups or DM the app for 1-to-1 help., with interactive elements

Where are you getting your information from and how stale is it?

My information comes from Airbyte's official documentation and related sources. Specifically, the Airbyte knowledge MCP server (which powers this assistant) provides semantic search over: [Airbyte knowledge MCP] • Airbyte's official documentation (docs.airbyte.com) • Airbyte's website • OpenAPI specs • YouTube content • GitHub issues, discussions, and pull requests As for freshness, I don't have a specific "last updated" timestamp I can share. However, the knowledge sources do reflect current documentation — for example, they reference the Agent Engine Public Beta as recently live, which is a recent development. That said, since this is a knowledge base with a sync cycle, there may be some lag between the latest changes and what I can access. For the most up-to-date information, I'd recommend checking docs.airbyte.com directly. 👍 0 button 👎 0 button ℹ️ Tip: Mention @kapa.ai in the thread for follow-ups or DM the app for 1-to-1 help.