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AI start pages (new start pages for the AI integration branch and the embeddings and vector search sections, edits in existing GenAI and AI agents pages), added embeddings & vector search use cases, and some editing of existing AI content.
*[Reducing throughput and expediting LLM response](../../ai-integration/ai-agents/ai-agents_overview#reducing-throughput-and-expediting-llm-response)
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*[Common use cases](../../ai-integration/ai-agents/ai-agents_overview#common-use-cases)
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</Admonition>
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## Common use cases
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AI agents are designed to easily integrate AI capabilities into applications and workflows. They can interact with users, intelligently retrieve and process data from proprietary databases, and apply actions based on roles they are requested to take and the data they have access to. Some of the tasks and applications they can be tailored to perform include:
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<aid="customer-support-chatbot-agents"/> **Customer support chatbot agents** can answer customer queries based on information stored in databases and internal knowledge bases, provide troubleshooting steps, and guide users through processes in real time.
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<aid="data-analysis-and-reporting-agents"/> **Data analysis and reporting agents** can analyze large datasets to extract relevant data and present it in a user-friendly format, escalate customer issues and application output, create reports and highlight points of interest, and help businesses make informed decisions.
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<aid="automated-content-generation-agents"/> **Automated content generation agents** can generate summaries, add automated comments to articles and application-generated content, reference readers to related material, and create marketing content based on user input and stored information.
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<aid="workflow-automation-agents"/> **Workflow automation agents** can automate repetitive tasks such as email sorting, spam filtering, form filling, or file organization.
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<aid="intelligent-recommendation-agents"/> **Intelligent recommendation agents** can provide personalized recommendations based on user preferences and available data, e.g. a _library assistant_ suggesting books and other resources, an _HR office assistant_ recommending rewards for employees based on their performance and available facilities near their residence, or an _e-commerce assistant_ recommending products.
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<hr />
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## Defining and running an AI agent
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AI agents can be created by RavenDB clients (providing they have database administration permissions).
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You can limit the number of times that the LLM is allowed to trigger database queries in response to a single user prompt.
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[Setting iterations limit using the API](../../ai-integration/ai-agents/creating-ai-agents/creating-ai-agents_api#set-maximum-number-of-iterations)
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<hr />
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## Common use cases
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AI agents are designed to easily integrate AI capabilities into applications and workflows. They can interact with users, intelligently retrieve and process data from proprietary databases, and apply actions based on roles they are requested to take and the data they have access to. Some of the tasks and applications they can be tailored to perform include -
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***Customer support chatbot agents**
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Agents can answer customer queries based on information stored in databases and internal knowledge bases, provide troubleshooting steps, and guide users through processes in real time.
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***Data analysis and reporting agents**
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Agents can analyze large datasets to extract relevant data and present it in a user-friendly format, escalate customer issues and application output, create reports and highlight points of interest, and help businesses make informed decisions.
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***Content generation agents**
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Agents can generate summaries, add automated comments to articles and application-generated content, reference readers to related material, and create marketing content based on user input and stored information.
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***Workflow automation agents**
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Agents can automate repetitive tasks like email sorting, spam filtering, form filling, or file organization.
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***Intelligent recommendation agents**
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Agents can provide personalized recommendations based on user preferences and available data, e.g. a _library assistant_ suggesting books and other resources, an _HR office assistant_ recommending rewards for employees based on their performance and available facilities near their residence, or an _e-commerce assistant_ recommending products.
AI agents are built to easily integrate AI capabilities into RavenDB clients.
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An AI agent serves as a client's proxy to an AI model; the agent can not only maintain a continuous conversation with the model but also enable it to securely query a RavenDB database and request the client to perform actions.
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Using AI agents frees developers from the need to manage an AI model in their code, enhances the model by giving it access to a credible and relevant data source, and opens the door to numerous operational scenarios.
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## Use cases
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Creating an AI agent and assigning it a role can be done in minutes using Studio or the API, making it easy to address a wide variety of use cases like -
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-[Customer support chatbot agents](../../ai-integration/ai-agents/ai-agents_overview#customer-support-chatbot-agents)
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-[Data analysis and reporting agents](../../ai-integration/ai-agents/ai-agents_overview#data-analysis-and-reporting-agents)
Our technical documentation explains in detail what AI agents are and how to define and use them.
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If you're new to AI agents, we recommend the [overview](../../ai-integration/ai-agents/ai-agents_overview) page as a good starting point.
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@@ -40,19 +27,27 @@ If you're new to AI agents, we recommend the [overview](../../ai-integration/ai-
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<CardWithImagetitle="Easily creating and testing an AI agent using Studio"url="../../ai-integration/ai-agents/creating-ai-agents/creating-ai-agents_studio"imgSrc={aiAgentsStartStudioImage}imgAlt="imgAlt"ctaLabel="Read" />
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</ColGrid>
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## Videos
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## In-depth articles
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Once you get acquainted with AI agents, expand your expertise with a comprehensive guide that includes some practical insights.
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<ColGridcolCount={3}>
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<CardWithImagetitle="Practical Look at AI Agents with RavenDB"description="Article by Gracjan Sadowicz"imgSrc=""url="https://ravendb.net/articles/practical-look-at-ai-agents-with-ravendb"imgAlt="imgAlt"ctaLabel="Read" />
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</ColGrid>
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## Videos
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Watch our webinars to see AI agents in action and learn practical implementation techniques.
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<ColGridcolCount={2}>
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<CardWithImageHorizontaltitle="How to run AI agents natively in your database"description="Webinar with Oren Eini"url="https://www.youtube.com/watch?v=A17GSLGN-cQ"imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc"imgAlt="imgAlt"ctaLabel="Watch" />
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<CardWithImageHorizontaltitle="How to create powerful and secure AI agents with RavenDB"description="Webinar with Oren Eini"url="https://www.youtube.com/watch?v=jzUxL9P17G4"imgSrc="https://media.licdn.com/dms/image/v2/D4D10AQG81cXtiYRc2w/image-shrink_800/B4DZZYYLcTHwAk-/0/1745239456036?e=2147483647&v=beta&t=yQVz6ji4wD4reOTXtlPpERK0fdpr1f2VoG_SEV9x3lc"imgAlt="imgAlt"ctaLabel="Watch" />
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</ColGrid>
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## In-depth articles
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## Use cases
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Creating an AI agent and assigning it a role can be done in minutes using Studio or the API, making it easy to address a wide variety of use cases like -
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Once you get acquainted with AI agents, expand your expertise with a comprehensive guide that includes some practical insights.
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-[Customer support chatbot agents](../../ai-integration/ai-agents/ai-agents_overview#customer-support-chatbot-agents)
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-[Data analysis and reporting agents](../../ai-integration/ai-agents/ai-agents_overview#data-analysis-and-reporting-agents)
<CardWithImagetitle="Practical Look at AI Agents with RavenDB"description="Article by Gracjan Sadowicz"imgSrc=""url="https://ravendb.net/articles/practical-look-at-ai-agents-with-ravendb"imgAlt="imgAlt"ctaLabel="Read" />
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